Dissertations / Theses on the topic 'Genetic Algorithm Heuristic'
To see the other types of publications on this topic, follow the link: Genetic Algorithm Heuristic.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Genetic Algorithm Heuristic.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Komínek, Jan. "Heuristické algoritmy pro optimalizaci." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2012. http://www.nusl.cz/ntk/nusl-230306.
Full textAbstract:
This diploma thesis deals with genetic algorithms and their properties. Particular emphasis is placed on finding the influence of mutation and population size. Genetic algorithms are applied on inverse heat conduction problems (IHCP) in the second part of the thesis. Several different approaches and coding methods were tested. Properties of genetic algorithms were improved by definition of two new genetic operators – manipulation and sorting. Reported theoretical findings were tested on the real data of inverse heat conduction problem. The library for easy implementation of GA for solving general optimization problems in C ++ was created and is described in the last chapter.
2
Bilal, Mohd. "A Heuristic Search Algorithm for Asteroid Tour Missions." Thesis, Luleå tekniska universitet, Rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71361.
Full textAbstract:
Since the discovery of Ceres, asteroids have been of immense scientific interest and intrigue. They hold answers to many of the fundamental questionsabout the formation and evolution of the Solar System. Therefore, a missionsurveying the asteroid belt with close encounter of carefully chosen asteroidswould be of immense scientific benefit. The trajectory of such an asteroidtour mission needs to be designed such that asteroids of a wide range ofcompositions and sizes are encountered; all with an extremely limited ∆Vbudget.This thesis presents a novel heuristic algorithm to optimize trajectoriesfor an asteroid tour mission with close range flybys (≤ 1000 km). The coresearch algorithm efficiently decouples combinatorial (i.e. choosing the asteroids to flyby)and continuous optimization (i.e. optimizing critical maneuversand events) of what is essentially a mixed integer programming problem.Additionally, different methods to generate a healthy initial population forthe combinatorial optimization are presented.The algorithm is used to generate a set of 1800 feasible trajectories withina 2029+ launch frame. A statistical analysis of these set of trajectories isperformed and important metrics for the search are set based on the statistics.Trajectories allowing flybys to prominent families of asteroids like Flora andNysa with ∆V as low as 4.99 km/s are obtained.Two modified implementations of the algorithm are presented. In a firstiteration, a large sample of trajectories is generated with a limited numberof encounters to the most scientifically interesting targets. While, a posteriori, trajectories are filled in with as many small targets as possible. Thisis achieved in two different ways, namely single step extension and multiplestep extension. The former fills in the trajectories with small targets in onestep, while the latter optimizes the trajectory by filling in with one asteroid per step. The thesis also presents detection of asteroids for successfullyperforming flybys. A photometric filter is developed which prunes out badlyilluminated asteroids. The best trajectory is found to perform well againstthis filter such that nine out of the ten planned flybys are feasible.
3
Ma, Jiya. "A Genetic Algorithm for Solar Boat." Thesis, Högskolan Dalarna, Datateknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3488.
Full textAbstract:
Genetic algorithm has been widely used in different areas of optimization problems. Ithas been combined with renewable energy domain, photovoltaic system, in this thesis.To participate and win the solar boat race, a control program is needed and C++ hasbeen chosen for programming. To implement the program, the mathematic model hasbeen built. Besides, the approaches to calculate the boundaries related to conditionhave been explained. Afterward, the processing of the prediction and real time controlfunction are offered. The program has been simulated and the results proved thatgenetic algorithm is helpful to get the good results but it does not improve the resultstoo much since the particularity of the solar driven boat project such as the limitationof energy production
4
Lianjie, Shen. "Optimization and Search in Model-Based Automotive SW/HW Development." Thesis, Linköpings universitet, Programvara och system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-105394.
Full textAbstract:
In this thesis two case studies are performed about solving two design problems we face during the design phase of new Volvo truck. One is to solve the frame packing problem on CAN bus. The other is to solve the LDC allocation problem. Both solutions are targeted to meet as many end-to-end latency requirements as possible. Now the solution is obtained through manually approach and based on the designer experience. But it is still not satisfactory enough. With the development of artificial intelligence method we propose two methods based on genetic algorithm to solve our design problem we face today. In first case study about frame packing we perform one single genetic algorithm process to find the optimal solution. In second case study about LDC allocation we proposed how to handle two genetic algorithm processes together to reach the optimal solution. In this thesis we show the feasibility of adopting artificial intelligence concept in some activities of the truck design phases like we do in both case studies.
5
Han, Limin. "An investigation of a genetic algorithm based hyper-heuristic applied to scheduling problems." Thesis, University of Nottingham, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.422736.
Full text
6
Cheng, Lin. "A genetic algorithm for the vehicle routing problem with time windows /." Electronic version (PDF), 2005. http://dl.uncw.edu/etd/2005/chengl/lincheng.pdf.
Full text
7
Woodside-Oriakhi, Maria. "Portfolio optimisation with transaction cost." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/5839.
Full textAbstract:
Portfolio selection is an example of decision making under conditions of uncertainty. In the face of an unknown future, fund managers make complex financial choices based on the investors perceptions and preferences towards risk and return. Since the seminal work of Markowitz, many studies have been published using his mean-variance (MV) model as a basis. These mathematical models of investor attitudes and asset return dynamics aid in the portfolio selection process. In this thesis we extend the MV model to include the cardinality constraints which limit the number of assets held in the portfolio and bounds on the proportion of an asset held (if any is held). We present our formulation based on the Markowitz MV model for rebalancing an existing portfolio subject to both fixed and variable transaction cost (the fee associated with trading). We determine and demonstrate the differences that arise in the shape of the trading portfolio and efficient frontiers when subject to non-cardinality and cardinality constrained transaction cost models. We apply our flexible heuristic algorithms of genetic algorithm, tabu search and simulated annealing to both the cardinality constrained and transaction cost models to solve problems using data from seven real world market indices. We show that by incorporating optimization into the generation of valid portfolios leads to good quality solutions in acceptable computational time. We illustrate this on problems from literature as well as on our own larger data sets.
8
Hanek, Petr. "Implementace problému směrování vozidel pomocí algoritmu mravenčích kolonií a částicových rojů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400931.
Full textAbstract:
This diploma thesis focuses on meta-heuristic algorithms and their ability to solve difficult optimization problems in polynomial time. The thesis describes different kinds of meta-heuristic algorithms such as genetic algorithm, particle swarm optimization or ant colony optimization. The implemented application was written in Java and contains ant colony optimization for capacitated vehicle routing problem and particle swarm optimization which finds the best possible parameters for ant colonies.
9
Demirbas, Korkut. "Optimal Management Of Coastal Aquifers Using Heuristic Algorithms." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613135/index.pdf.
Full textAbstract:
Excessive pumping in coastal aquifers results in seawater intrusion where optimal and efficient planning is essential. In this study, numerical solution of single potential solution by Strack is combined with genetic algorithm (GA) to find the maximum extraction amount in a coastal aquifer. Seawater intrusion is tracked with the potential value at the extraction well locations. A code is developed by combining GA and a subroutine repeatedly calling MODFLOW as a numerical solver to calculate the potential distribution for different configurations of solution (trial solutions). Potential distributions are used to evaluate the fitness values for GA. The developed model is applied to a previous work by Mantoglou. Another heuristic method, simulated annealing (SA) is utilized to compare the results of GA. Different seawater prevention methods (i.e. injection wells, canals) and decision variables related to those methods (i.e. location of the injection wells or canals) are added to model to further prevent the seawater intrusion and improve the coastal aquifer benefit. A method called &ldquoAlternating Constraints Method&rdquo
is introduced to improve the solution for the cases with variable location. The results show that both proposed method and the regular solution with GA or SA prove to be successful methods for the optimal management of coastal aquifers.
10
Hassan, Fadratul Hafinaz. "Heuristic search methods and cellular automata modelling for layout design." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/7581.
Full textAbstract:
Spatial layout design must consider not only ease of movement for pedestrians under normal conditions, but also their safety in panic situations, such as an emergency evacuation in a theatre, stadium or hospital. Using pedestrian simulation statistics, the movement of crowds can be used to study the consequences of different spatial layouts. Previous works either create an optimal spatial arrangement or an optimal pedestrian circulation. They do not automatically optimise both problems simultaneously. Thus, the idea behind the research in this thesis is to achieve a vital architectural design goal by automatically producing an optimal spatial layout that will enable smooth pedestrian flow. The automated process developed here allows the rapid identification of layouts for large, complex, spatial layout problems. This is achieved by using Cellular Automata (CA) to model pedestrian simulation so that pedestrian flow can be explored at a microscopic level and designing a fitness function for heuristic search that maximises these pedestrian flow statistics in the CA simulation. An analysis of pedestrian flow statistics generated from feasible novel design solutions generated using the heuristic search techniques (hill climbing, simulated annealing and genetic algorithm style operators) is conducted. The statistics that are obtained from the pedestrian simulation is used to measure and analyse pedestrian flow behaviour. The analysis from the statistical results also provides the indication of the quality of the spatial layout design generated. The technique has shown promising results in finding acceptable solutions to this problem when incorporated with the pedestrian simulator when demonstrated on simulated and real-world layouts with real pedestrian data.
11
Šebek, Petr. "Heuristiky v optimalizačních úlohách třídy RCPSP." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234904.
Full textAbstract:
This thesis deals with the description of the state of resource-constrained project scheduling problem. It defines the formal problem and its complexity. It also describes variants of this problem. Algorithms for solving RCPSP are presented. Heuristic genetic algorithm GARTH is analyzed in depth. The implementation of prototypes solving RCPSP using GARTH is outlined. Several improvements to the original algorithm are designed and evaluated.
12
Uzer, Cevdet Can. "Shape Optimization Of An Excavator Boom By Using Genetic Algorithm." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12609575/index.pdf.
Full textAbstract:
This study concerns with the automated structural optimization of an excavator boom. The need for this work arises due to the fact that the preparation of the CAD model, performing finite element analysis and model data evaluation are time consuming processes and require experienced man power. The previously developed software OptiBOOM, which generates a CAD model using a finite set of parameters and then performs a finite element analysis by using a commercial program has been modified. The model parameter generation, model creation, analysis data collection and data evaluation phases are done by the Python and Delphi based computer codes. A global heuristic search strategy such as genetic algorithm is chosen to search different boom models and select an optimum.
13
Burdová, Jana. "Heuristické a metaheuristické metody řešení úlohy obchodního cestujícího." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-75095.
Full textAbstract:
Minimal length of a travelling salesman's problem had been studied in this diploma these. Travelling salesman must come trough each place just once and then go back to the starting place. This problem can be illustrated as a problem of graph theory, such that places are the vertices, roads are the edges, distances of roads are the lengths of edges. The optimal travelling salesman's problem tour is the shortest Hamiltionian cycle in the graph. It is a classical NP-complete problem. There is no algorithm that solves this problem in polynomial time. This problem can be solved by using various approximation algorithms, they offer less time consumption and lowest quality than optimization. This diploma these had been dedicated to approximation algorithms, for example: nearest neighbor method, minimal spanning tree method, Christofide's method, 2-opt., genetic algorithm, etc.
14
Herrington, Hira B. "A Heuristic Evolutionary Method for the Complementary Cell Suppression Problem." NSUWorks, 2015. http://nsuworks.nova.edu/gscis_etd/28.
Full textAbstract:
Cell suppression is a common method for disclosure avoidance used to protect sensitive information in two-dimensional tables where row and column totals are published along with non-sensitive data. In tables with only positive cell values, cell suppression has been demonstrated to be non-deterministic NP-hard. Therefore, finding more efficient methods for producing low-cost solutions is an area of active research.
Genetic algorithms (GA) have shown to be effective in finding good solutions to the cell suppression problem. However, these methods have the shortcoming that they tend to produce a large proportion of infeasible solutions. The primary goal of this research was to develop a GA that produced low-cost solutions with fewer infeasible solutions created at each generation than previous methods without introducing excessive CPU runtime costs.
This research involved developing a GA that produces low-cost solutions with fewer infeasible solutions produced at each generation; and implementing selection and replacement operations that maintained genetic diversity during the evolution process. The GA's performance was tested using tables containing 10,000 and 100,000 cells. The primary criterion for the evaluation of effectiveness of the GA was total cost of the complementary suppressions and the CPU runtime.
Experimental results indicate that the GA-based method developed in this dissertation produced better quality solutions than those produced by extant heuristics. Because existing heuristics are very effective, this GA-based method was able to surpass them only modestly.
Existing evolutionary methods have also been used to improve upon the quality of solutions produced by heuristics. Experimental results show that the GA-based method developed in this dissertation is computationally more efficient than GA-based methods proposed in the literature. This is attributed to the fact that the specialized genetic operators designed in this study produce fewer infeasible solutions.
The results of these experiments suggest the need for continued research into non-probabilistic methods to seed the initial populations, selection and replacement strategies that factor in genetic diversity on the level of the circuits protecting sensitive cells; solution-preserving crossover and mutation operators; and the use of cost benefit ratios to determine program termination.
15
Horký, Aleš. "Systém pro pokročilé plánování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234893.
Full textAbstract:
This master thesis deals with the automatic design of examinations and courses scheduling. The design is adapted to the specific requirements of the Faculty of Information Technology of Brno University of Technology. A genetic algorithm and a heuristic algorithm are employed to solve this task. The genetic algorithm is used to specify the sequence of the examinations (or the courses) and then the heuristic algorithm spread them out into a timetable. An implementation (written in Python 3) provides a fast parallel processing calculation which can generate satisfactory schedules in tens of minutes. Performed experiments show approximately 13% better results in all considered criteria in comparison with utilized examination schedules in the past. The development was periodically consulted with persons responsible for the schedule processing at the faculty. The program will be used while designing of examination schedules for the academic year 2015/2016.
16
Olsson, Jonas. "Solving a highly constrained multi-level container loading problem from practice." Thesis, Linköpings universitet, Optimeringslära, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-134430.
Full textAbstract:
The container loading problem considered in this thesis is to determine placements of a set of packages within one or multiple shipping containers. Smaller packages are consolidated on pallets prior to being loaded in the shipping containers together with larger packages. There are multiple objectives which may be summarized as fitting all the packages while achieving good stability of the cargo as well as the shipping containers themselves. According to recent literature reviews, previous research in the field have to large extent been neglecting issues relevant in practice. Our real-world application was developed for the industrial company Atlas Copco to be used for sea container shipments at their Distribution Center (DC) in Texas, USA. Hence all applicable practical constraints faced by the DC operators had to be treated properly. A high variety in sizes, weights and other attributes such as stackability among packages added complexity to an already challenging combinatorial problem. Inspired by how the DC operators plan and perform loading manually, the batch concept was developed, which refers to grouping of boxes based on their characteristics and solving subproblems in terms of partial load plans. In each batch, an extensive placement heuristic and a load plan evaluation run iteratively, guided by a Genetic Algorithm (GA). In the placement heuristic, potential placements are evaluated using a scoring function considering aspects of the current situation, such as space utilization, horizontal support and heavier boxes closer to the floor. The scoring function is weighted by coefficients corresponding to the chromosomes of an individual in the GA population. Consequently, the fitness value of an individual in the GA population is the rating of a load plan. The loading optimization software has been tested and successfully implemented at the DC in Texas. The software has been proven capable of generating satisfactory load plans within acceptable computation times, which has resulted in reduced uncertainty and labor usage in the loading process. Analysis using real sea container shipments shows that the GA is able to tune the scoring coefficients to suit the particular problem instance being solved.
17
Hillblom, Jonathan. "Evaluating Different Genetic Algorithms for a State-machine Combining Assignment Problem." Thesis, Karlstads universitet, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-79019.
Full textAbstract:
Deep packet inspection (DPI) is useful as a tool for analyzing internet traffic. Regular expressions (regexps) can be used to detect the network traffic patterns that the DPI is able to identify. These regexps can be represented as state-machines, and sometimes combining smaller state-machines into larger state-machines can result in more efficient processing. This thesis looks at how to decide which state-machines used in DPI-classes should be combined with which other state-machines in an efficient manner using genetic algorithms. The goal being to create as few resulting state-machines from the combination while still maintaining a upper limit on the size of the resulting state-machines. The problem is modelled as an assignment problem for which an emulated surrogate problem is used in order to make experimental evaluations. Several genetic algorithms are suggested in an attempt to explore a wide range of parameters. It is also evaluated if different genetic algorithms perform differently depending on if the state-machines represent DPI-classes used to parse UDP or TCP traffic. A 2-dimensional representation is used that allows for a better capture of the underlying assignment problem. Different approaches to fitness are explored and found to have different efficacy in different situations. Several genetic algorithm operators are suggested for different situations and a difference is found between what works for UDP and for TCP.Deep packet inspection (DPI) ̈ar användbart som ett verktyg f ̈or att analysera internettrafik. Regular expressions (regexps) kan användas för att detektera trafik mönster somDPI:n kan identifiera. De här regexps kan representeras som state-machines, och ibland så kan kombinationen av mindre state-machines till större state-machines resultera i mer effektiv bearbetning. Den här tesen undersöker hur man kan bestämma vilka state-machines som används iDPI-klassen bör bli kombinerade på ett effektivt sätt med genetiska algoritmer. Målet är att skapa så fǻ resulterande state-machines från kombineringen på ett sådant sätt att storleken på alla resulterande state-machines håller sig under en övre gräns. Problemet är modellerat som ett assignment problem för vilket ett emulerat surrogatproblem används för att tillåta experiment att utföras. Ett flertal genetiska algoritmer är föreslagna i ett försök att undersöka en bred räckvidd av parametrar. Det är också undersökt om olika genetiska algoritmer har olika prestanda beroende på om state-machines representerar DPI-klasser använda för UDP eller TCP trafik. En 2-dimensionell representation som fångar det underliggande problemet på ett bras sätt är använd. Olika tillvägagångssätt för att representera fitness är undersökta och är upptäckta att ha olika effektivitet i olika situationer. Ett flertal genetiska algoritm operatorer är föreslagna för olika situationer och en skillnad är hittad mellan vad som fungerar för UDP och vad som fungerar för TCP.
18
Burnett, Linda Dee. "Heuristic Optimization of Boolean Functions and Substitution Boxes for Cryptography." Thesis, Queensland University of Technology, 2005. https://eprints.qut.edu.au/16023/1/Linda_Burnett_Thesis.pdf.
Full textAbstract:
Fundamental to the electronic security of information and communication systems, is the correct use and application of appropriate ciphers. The strength of these ciphers, particularly in their ability to resist cryptanalytic attacks, directly in
uences the overall strength of the entire system. The strength of the underlying cipher is reliant upon a robust structure and the carefully designed interaction between components in its architecture. Most importantly, however, cipher strength is critically dependent on the strength of the individual components of which it is comprised.
Boolean functions and substitution boxes (s-boxes) are among the most common and essential components of ciphers. This is because they are able to provide a cipher with strengthening properties to resist known and potential cryptanalytic attacks. Thus, it is not surprising that significant research effort has been made in trying to develop ways of obtaining boolean functions and substitution boxes with
optimal achievable measures of desirable cryptographic properties. Three of the
main cryptographic properties required by strong boolean functions and s-boxes are nonlinearity, correlation immunity and propagation criteria, with different cryptographic applications requiring different acceptable measures of these and other properties. As combinations of cryptographic properties exhibited by functions
can be conicting, finding cryptographically strong functions often means that a trade-off needs to be made when optimizing property values. Throughout this thesis, the term "optimization" specifically refers to seeking to obtain the best achievable combination of target property values which may be exhibited by boolean functions and s-boxes, regardless of whether the relevant properties are
conflicting or complementary.
This thesis focusses on a particular class of techniques for obtaining strong functions for cryptographic applications, referred to as heuristic methods or, simply, heuristics. Three new heuristic methods, each aimed at generating boolean functions optimizing one or more of the main cryptographic properties mentioned above, in addition to other desirable properties, are presented. The first of the new heuristic methods developed for this thesis focusses on generating boolean functions which are balanced and exhibit very high nonlinearities. Highly nonlinear balanced functions are critical to many cryptographic applications, as they provide good resistance to linear cryptanalytic attacks. This first method is based on the recursive modification of a starting bent function and is shown to be highly successful and efficient at generating numerous such functions, which also exhibit low autocorrelation values, in a very short computational time.
The generation of balanced, correlation immune boolean functions that also exhibit the confl
icting property of high nonlinearity is the focus of the second new heuristic method developed for this thesis. By concatenating selected pairs of lower-dimensional boolean functions together in the Walsh Hadamard transform domain, direct optimization for both resilience and nonlinearity was able to take place at each level towards and for the final function. This second method was able to generate examples of boolean functions with almost all of the best known optimal combinations of target property values. Experiments have shown the success of this method in consistently generating highly nonlinear resilient boolean functions, for a range of orders of resilience, with such functions possessing optimal algebraic degree.
A third new heuristic method, which searches for balanced boolean functions which satisfy a non-zero degree of propagation criteria and exhibit high nonlinearity, is presented. Intelligent bit manipulations in the truth table of starting
functions, based on fundamental relationships between boolean function transforms and measures, provide the design rationale for this method. Two new function generation schemes have been proposed for this method, to efficiently
satisfy the requirements placed on the starting functions utilized in the computational
process. An optional process attempts to increase the algebraic degree of the resulting functions, without sacrificing the optimalities that are achievable. The validity of this method is demonstrated through the success of various
experimental trials.
Switching the focus from single output boolean functions to multiple output boolean functions (s-boxes), the effectiveness of existing heuristic techniques (namely Genetic Algorithm, Hill Climbing Method and combined Genetic Algorithm/Hill Climbing) in primarily being applied to improve the nonlinearity of s-boxes of various dimensions, is investigated. The prior success of these heuristic techniques for improving the nonlinearity of boolean functions has been previously demonstrated, as has the success of hill climbing in isolation when applied to bijective s-boxes. An extension to the bijective s-box optimization work is presented in this thesis. In this new research, a Genetic Algorithm, Hill Climbing Method
and the two in combination are applied to the nonlinearity and autocorrelation optimization of regular NxM s-boxes (N > M) to investigate the effectiveness and efficiency of each of these heuristics. A new breeding scheme, utilized in the Genetic Algorithm and combined Genetic Algorithm/Hill Climbing trials, is also presented. The success of experimental results compared to random regular s-box generation is demonstrated.
New research in applying the Hill Climbing Method to construct NxM sboxes (N > M) required to meet specific property criteria is presented. The consideration of the characteristics desired by the constructed s-boxes largely dictated the generation process. A discussion on the generation process of the component functions is included. Part of the results produced by experimental trials were incorporated into a commonly used family of stream ciphers, thus further supporting the use of heuristic techniques as a useful means of obtaining strong functions suitable for incorporation into practical ciphers.
An analysis of the cryptographic properties of the s-box used in the MARS block cipher, the method of generation and the computational time taken to obtain this s-box, led to the new research reported in this thesis on the generation of MARS-like s-boxes. It is shown that the application of the Hill Climbing
Method, with suitable requirements placed on the component boolean functions, was able to generate multiple MARS-like s-boxes which satisfied the MARS sbox requirements and provided additional properties. This new work represented an alternative approach to the generation of s-boxes satisfying the MARS sbox
property requirements but which are cryptographically superior and can be obtained in a fraction of the time than that which was taken to produce the MARS s-box. An example MARS-like s-box is presented in this thesis.
The overall value of heuristic methods in generating strong boolean functions and substitution boxes is clearly demonstrated in this thesis. This thesis has made several significant contributions to the field, both in the development of new, specialized heuristic methods capable of generating strong boolean functions, and in the analysis and optimization of substitution boxes, the latter achieved
through applying existing heuristic techniques.
19
Burnett, Linda Dee. "Heuristic Optimization of Boolean Functions and Substitution Boxes for Cryptography." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16023/.
Full textAbstract:
Fundamental to the electronic security of information and communication systems, is the correct use and application of appropriate ciphers. The strength of these ciphers, particularly in their ability to resist cryptanalytic attacks, directly in uences the overall strength of the entire system. The strength of the underlying cipher is reliant upon a robust structure and the carefully designed interaction between components in its architecture. Most importantly, however, cipher strength is critically dependent on the strength of the individual components of which it is comprised. Boolean functions and substitution boxes (s-boxes) are among the most common and essential components of ciphers. This is because they are able to provide a cipher with strengthening properties to resist known and potential cryptanalytic attacks. Thus, it is not surprising that significant research effort has been made in trying to develop ways of obtaining boolean functions and substitution boxes with optimal achievable measures of desirable cryptographic properties. Three of the main cryptographic properties required by strong boolean functions and s-boxes are nonlinearity, correlation immunity and propagation criteria, with different cryptographic applications requiring different acceptable measures of these and other properties. As combinations of cryptographic properties exhibited by functions can be conicting, finding cryptographically strong functions often means that a trade-off needs to be made when optimizing property values. Throughout this thesis, the term "optimization" specifically refers to seeking to obtain the best achievable combination of target property values which may be exhibited by boolean functions and s-boxes, regardless of whether the relevant properties are conflicting or complementary. This thesis focusses on a particular class of techniques for obtaining strong functions for cryptographic applications, referred to as heuristic methods or, simply, heuristics. Three new heuristic methods, each aimed at generating boolean functions optimizing one or more of the main cryptographic properties mentioned above, in addition to other desirable properties, are presented. The first of the new heuristic methods developed for this thesis focusses on generating boolean functions which are balanced and exhibit very high nonlinearities. Highly nonlinear balanced functions are critical to many cryptographic applications, as they provide good resistance to linear cryptanalytic attacks. This first method is based on the recursive modification of a starting bent function and is shown to be highly successful and efficient at generating numerous such functions, which also exhibit low autocorrelation values, in a very short computational time. The generation of balanced, correlation immune boolean functions that also exhibit the confl icting property of high nonlinearity is the focus of the second new heuristic method developed for this thesis. By concatenating selected pairs of lower-dimensional boolean functions together in the Walsh Hadamard transform domain, direct optimization for both resilience and nonlinearity was able to take place at each level towards and for the final function. This second method was able to generate examples of boolean functions with almost all of the best known optimal combinations of target property values. Experiments have shown the success of this method in consistently generating highly nonlinear resilient boolean functions, for a range of orders of resilience, with such functions possessing optimal algebraic degree. A third new heuristic method, which searches for balanced boolean functions which satisfy a non-zero degree of propagation criteria and exhibit high nonlinearity, is presented. Intelligent bit manipulations in the truth table of starting functions, based on fundamental relationships between boolean function transforms and measures, provide the design rationale for this method. Two new function generation schemes have been proposed for this method, to efficiently satisfy the requirements placed on the starting functions utilized in the computational process. An optional process attempts to increase the algebraic degree of the resulting functions, without sacrificing the optimalities that are achievable. The validity of this method is demonstrated through the success of various experimental trials. Switching the focus from single output boolean functions to multiple output boolean functions (s-boxes), the effectiveness of existing heuristic techniques (namely Genetic Algorithm, Hill Climbing Method and combined Genetic Algorithm/Hill Climbing) in primarily being applied to improve the nonlinearity of s-boxes of various dimensions, is investigated. The prior success of these heuristic techniques for improving the nonlinearity of boolean functions has been previously demonstrated, as has the success of hill climbing in isolation when applied to bijective s-boxes. An extension to the bijective s-box optimization work is presented in this thesis. In this new research, a Genetic Algorithm, Hill Climbing Method and the two in combination are applied to the nonlinearity and autocorrelation optimization of regular NxM s-boxes (N > M) to investigate the effectiveness and efficiency of each of these heuristics. A new breeding scheme, utilized in the Genetic Algorithm and combined Genetic Algorithm/Hill Climbing trials, is also presented. The success of experimental results compared to random regular s-box generation is demonstrated. New research in applying the Hill Climbing Method to construct NxM sboxes (N > M) required to meet specific property criteria is presented. The consideration of the characteristics desired by the constructed s-boxes largely dictated the generation process. A discussion on the generation process of the component functions is included. Part of the results produced by experimental trials were incorporated into a commonly used family of stream ciphers, thus further supporting the use of heuristic techniques as a useful means of obtaining strong functions suitable for incorporation into practical ciphers. An analysis of the cryptographic properties of the s-box used in the MARS block cipher, the method of generation and the computational time taken to obtain this s-box, led to the new research reported in this thesis on the generation of MARS-like s-boxes. It is shown that the application of the Hill Climbing Method, with suitable requirements placed on the component boolean functions, was able to generate multiple MARS-like s-boxes which satisfied the MARS sbox requirements and provided additional properties. This new work represented an alternative approach to the generation of s-boxes satisfying the MARS sbox property requirements but which are cryptographically superior and can be obtained in a fraction of the time than that which was taken to produce the MARS s-box. An example MARS-like s-box is presented in this thesis. The overall value of heuristic methods in generating strong boolean functions and substitution boxes is clearly demonstrated in this thesis. This thesis has made several significant contributions to the field, both in the development of new, specialized heuristic methods capable of generating strong boolean functions, and in the analysis and optimization of substitution boxes, the latter achieved through applying existing heuristic techniques.
20
Allard, David M. "A Multi-Objective Genetic Algorithm to Solve Single Machine Scheduling Problems Using a Fuzzy Fitness Function." Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1180968613.
Full text
21
Shakya, Siddhartha. "DEUM : a framework for an estimation of distribution algorithm based on Markov random fields." Thesis, Robert Gordon University, 2006. http://hdl.handle.net/10059/39.
Full textAbstract:
Estimation of Distribution Algorithms (EDAs) belong to the class of population based optimisation algorithms. They are motivated by the idea of discovering and exploiting the interaction between variables in the solution. They estimate a probability distribution from population of solutions, and sample it to generate the next population. Many EDAs use probabilistic graphical modelling techniques for this purpose. In particular, directed graphical models (Bayesian networks) have been widely used in EDA. This thesis proposes an undirected graphical model (Markov Random Field (MRF)) approach to estimate and sample the distribution in EDAs. The interaction between variables in the solution is modelled as an undirected graph and the joint probability of a solution is factorised as a Gibbs distribution. The thesis describes a model of fitness function that approximates the energy in the Gibbs distribution, and shows how this model can be fitted to a population of solutions to estimate the parameters of the MRF. The estimated MRF is then sampled to generate the next population. This approach is applied to estimation of distribution in a general framework of an EDA, called Distribution Estimation using Markov Random Fields (DEUM). The thesis then proposes several variants of DEUM using different sampling techniques and tests their performance on a range of optimisation problems. The results show that, for most of the tested problems, the DEUM algorithms significantly outperform other EDAs, both in terms of number of fitness evaluations and the quality of the solutions found by them. There are two main explanations for the success of DEUM algorithms. Firstly, DEUM builds a model of fitness function to approximate the MRF. This contrasts with other EDAs, which build a model of selected solutions. This allows DEUM to use fitness in variation part of the evolution. Secondly, DEUM exploits the temperature coefficient in the Gibbs distribution to regulate the behaviour of the algorithm. In particular, with higher temperature, the distribution is closer to being uniform and with lower temperature it concentrates near some global optima. This gives DEUM an explicit control over the convergence of the algorithm, resulting in better optimisation.
22
Kingry, Nathaniel. "Heuristic Optimization and Sensing Techniques for Mission Planning of Solar-Powered Unmanned Ground Vehicles." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523874767812408.
Full text
23
Doungsa-ard, Chartchai. "Generation of Software Test Data from the Design Specification Using Heuristic Techniques. Exploring the UML State Machine Diagrams and GA Based Heuristic Techniques in the Automated Generation of Software Test Data and Test Code." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5380.
Full textAbstract:
Software testing is a tedious and very expensive undertaking. Automatic test data generation is, therefore, proposed in this research to help testers reduce their work as well as ascertain software quality. The concept of test driven development (TDD) has become increasingly popular during the past several years. According to TDD, test data should be prepared before the beginning of code implementation. Therefore, this research asserts that the test data should be generated from the software design documents which are normally created prior to software code implementation. Among such design documents, the UML state machine diagrams are selected as a platform for the proposed automated test data generation mechanism. Such diagrams are selected because they show behaviours of a single object in the system. The genetic algorithm (GA) based approach has been developed and applied in the process of searching for the right amount of quality test data. Finally, the generated test data have been used together with UML class diagrams for JUnit test code generation. The GA-based test data generation methods have been enhanced to take care of parallel path and loop problems of the UML state machines. In addition the proposed GA-based approach is also targeted to solve the diagrams with parameterised triggers. As a result, the proposed framework generates test data from the basic state machine diagram and the basic class diagram without any additional nonstandard information, while most other approaches require additional information or the generation of test data from other formal languages. The transition coverage values for the introduced approach here are also high; therefore, the generated test data can cover most of the behaviour of the system.
24
Ayo, Babatope S. "Data-driven flight path rerouting during adverse weather: Design and development of a passenger-centric model and framework for alternative flight path generation using nature inspired techniques." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/17387.
Full textAbstract:
A major factor that negatively impacts flight operations globally is adverse weather. To reduce the impact of adverse weather, avoidance procedures such as finding an alternative flight path can usually be carried out. However, such procedures usually introduce extra costs such as flight delay. Hence, there exists a need for alternative flight paths that efficiently avoid adverse weather regions while minimising costs.
Existing weather avoidance methods used techniques, such as Dijkstra’s and artificial potential field algorithms that do not scale adequately and have poor real time performance. They do not adequately consider the impact of weather and its avoidance on passengers.
The contributions of this work include a new development of an improved integrated model for weather avoidance, that addressed the impact of weather on passengers by defining a corresponding cost metric. The model simultaneously considered other costs such as flight delay and fuel burn costs.
A genetic algorithm (GA)-based rerouting technique that generates optimised alternative flight paths was proposed. The technique used a modified mutation strategy to improve global search. A discrete firefly algorithm-based rerouting method was also developed to improve rerouting efficiency. A data framework and simulation platform that integrated aeronautical, weather and flight data into the avoidance process was developed. Results show that the developed algorithms and model produced flight paths that had lower total costs compared with existing techniques. The proposed algorithms had adequate rerouting performance in complex airspace scenarios. The developed system also adequately avoided the paths of multiple aircraft in the considered airspace.
25
Škrabal, Ondřej. "Genetické algoritmy a rozvrhování." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2010. http://www.nusl.cz/ntk/nusl-229180.
Full textAbstract:
This work deals with scheduling problem in particular plastic production service. The solution is based on heuristic algorithms, programming languages C + +, C # and is built on the .NET framework and LINQ to XML. It provides the users with comparisons of the heuristic approach with genetic algorithms applied to production problem. All methods results are compared in relation to hand-arranged plans.
26
Nevrlý, Vlastimír. "Modely a metody pro svozové úlohy." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2016. http://www.nusl.cz/ntk/nusl-242880.
Full textAbstract:
This master's thesis deals with mathematical model building for routing problems and ways to solve them. There are discussed and implemented deterministic and heuristic approaches that are suitable to be utilized. A big effort is put into building of the mathematical model describing a real world problem from the field of waste management. Appropriate algorithms are developed and modified to solve a particular problem effectively. An original graphical environment is created to illustrate acquired results and perform testing computations.
27
Wagner, Stefan. "Looking inside genetic algorithms /." Linz : Trauner, 2005. http://aleph.unisg.ch/hsgscan/hm00116856.pdf.
Full text
28
Švadlenka, Jiří. "Informační systém pro školy s automatickou tvorbou rozvrhů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2008. http://www.nusl.cz/ntk/nusl-235924.
Full textAbstract:
This thesis devote itself to use of information system for school agenda administration. Schools are forced to administer big amounts of informations, not only referred to their students. Broad issue is very extensive and disparate, so the most common types of data and demands on school information system operation are stated. The system for automatic generation of timetables is part of the school information system. At the first, basic conceptions of scheduling scope are defined and tied together with them are methods and algorithms for timetable creation problem solving. School timetabling is problem of scheduling lessons with certain limitative conditions. Further, thesis is engaged in design of school information system, data organization in such system and solving of system design problems. Designed information system accentuates on easy expandability and wide range of usage possibilities. Also suggested algorithm for solving of defined school timetabling is stated in this part of thesis.
29
Ameli, Mostafa. "Heuristic Methods for Calculating Dynamic Traffic Assignment Simulation-based dynamic traffic assignment: meta-heuristic solution methods with parallel computing Non-unicity of day-to-day multimodal user equilibrium: the network design history effect Improving traffic network performance with road banning strategy: a simulation approach comparing user equilibrium and system optimum." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSET009.
Full textAbstract:
Les systèmes de transport sont caractérisés de manière dynamique non seulement par des interactions non linéaires entre les différents composants, mais également par des boucles de rétroaction entre l'état du réseau et les décisions des utilisateurs. En particulier, la congestion du réseau impacte à la fois la répartition de la demande locale en modifiant les choix d’itinéraire et la demande multimodale globale. Selon les conditions du réseau, ils peuvent décider de changer, par exemple, leur mode de transport. Plusieurs équilibres peuvent être définis pour les systèmes de transport. L'équilibre de l'utilisateur correspond à la situation dans laquelle chaque utilisateur est autorisé à se comporter de manière égoïste et à minimiser ses propres frais de déplacement. L'optimum du système correspond à une situation où le coût total du transport de tous les utilisateurs est minimal. Dans ce contexte, l’étude vise à calculer les modèles de flux d'itinéraires dans un réseau prenant en compte différentes conditions d’équilibre et à étudier l’équilibre du réseau dans un contexte dynamique. L'étude se concentre sur des modèles de trafic capables de représenter une dynamique du trafic urbain à grande échelle. Trois sujets principaux sont abordés. Premièrement, des méthodes heuristiques et méta-heuristiques rapides sont développées pour déterminer les équilibres avec différents types de trafic. Deuxièmement, l'existence et l'unicité des équilibres d'utilisateurs sont étudiées. Lorsqu'il n'y a pas d'unicité, la relation entre des équilibres multiples est examinée. De plus, l'impact de l'historique du réseau est analysé. Troisièmement, une nouvelle approche est développée pour analyser l’équilibre du réseau en fonction du niveau de la demande. Cette approche compare les optima des utilisateurs et du système et vise à concevoir des stratégies de contrôle afin de déplacer la situation d'équilibre de l'utilisateur vers l'optimum du systèmeTransport systems are dynamically characterized not only by nonlinear interactions between the different components but also by feedback loops between the state of the network and the decisions of users. In particular, network congestion affects both the distribution of local demand by modifying route choices and overall multimodal demand. Depending on the conditions of the network, they may decide to change for example their transportation mode. Several equilibria can be defined for transportation systems. The user equilibrium corresponds to the situation where each user is allowed to behave selfishly and to minimize his own travel costs. The system optimum corresponds to a situation where the total transport cost of all the users is minimum. In this context, the study aims to calculate route flow patterns in a network considering different equilibrium conditions and study the network equilibrium in a dynamic setting. The study focuses on traffic models capable of representing large-scale urban traffic dynamics. Three main issues are addressed. First, fast heuristic and meta-heuristic methods are developed to determine equilibria with different types of traffic patterns. Secondly, the existence and uniqueness of user equilibria is studied. When there is no uniqueness, the relationship between multiple equilibria is examined. Moreover, the impact of network history is analyzed. Thirdly, a new approach is developed to analyze the network equilibrium as a function of the level of demand. This approach compares user and system optimums and aims to design control strategies in order to move the user equilibrium situation towards the system optimum
30
Svensson, Philip. "Designing bus route networks with algorithms." Thesis, KTH, Optimeringslära och systemteori, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276961.
Full textAbstract:
The aim of this thesis is to make use of real world travel time and demand data and implement an algorithm which designs bus networks. Consideration is taken to both passenger and bus operator interests. Thereafter answering the questions: How well does the algorithm perform when applied to Södertälje, Sweden? Can the proposed method assist in the network design stage of real bus network planning? Heuristics and the multiobjective genetic algorithm NSGA-II (Non-dominated Sorting Genetic Algorithm II) were chosen. Three different problem cases were set up. It was found that the high computation time poses a great obstacle, over 80 hours for a network of 58 stations and 18 routes. Even then a longer run time would have led to improved results. When comparing a smaller problem, 24 stations and four routes, to the real bus routes it is based on, a superior solution was found based on the model. It is however not possible to argue for the proposed network being superior to the existing one if replaced in reality due to the fact a subsystem is modelled. It is believed that the proposed algorithm may be of assistance to traffic planners in the way of suggesting single links or routes, not replacing the complete bus network design process.Målet med denna studie är att använda verklig resedata och efterfrågan och implementera en algoritm som designar busslinjenät med avseende på passagerar -och operatörsintressen. Därefter svara på frågorna: Hur bra presterar algoritmen när den tillämpas på Södertälje, Sverige? Kan den föreslagna algoritmen bidra i designfasen av ett verkligt busslinjenät? Heuristik och den multiobjektiva genetiska algoritmen NSGA-II (Non-dominated Sorting Genetic Algorithm II) användes. Tre olika problem ställdes upp. Det framkom att den långa beräkningstiden är ett stort hinder, över 80 timmar för ett busslinjenät med 58 stationer och 18 busslinjer. Den begränsande faktorn var den långa körtiden, bättre lösningar hade kunnat hittas om programmet fått fortsätta köra. Endast ett mindre nätverk, 24 stationer med fyra busslinjer, baserades på verkliga busslinjer och kunde jämföras. Det resulterade i lösningar som var bättre än de verkliga busslinjerna inom ramen för modellen. Det kan dock inte betraktas som bättre än det verkliga nätverket i mån av att ersätta det, då endast ett subsystem modellerades. Det anses att den föreslagna algoritmen kan vara av assistans för trafikplanerare genom att föreslå länkar mellan busstationer eller hela busslinjer, däremot inte ersätta den nuvarande processen av att designa bussnätverk.
31
Lü, Haili, and 吕海利. "A comparative study of assembly job shop scheduling using simulation, heuristics and meta-heuristics." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B47029018.
Full text
32
Mansouri, Abdelkhalek. "Generic heuristics on GPU to superpixel segmentation and application to optical flow estimation." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA012.
Full textAbstract:
Déterminer des clusters dans des nuages de points et apparier des graphes sont des tâches primordiales en informatique, analyse de donnée, traitement d’image, généralement modélisées par des problèmes d’optimisation de classe NP-difficile. Avec l’avènement des multiprocesseurs à bas coût, l’accélération des procédures heuristiques pour ces tâches devient possible et nécessaire. Nous proposons des implantations parallèles sur système GPU (graphics processing unit) pour des algorithmes génériques appliqués ici à la segmentation d’image en superpixels et au problème du flot optique. Le but est de fournir des algorithmes génériques basés sur des structures de données décentralisées et aisément adaptables à différents problèmes d’optimisation sur des graphes et plateformes parallèles.Les algorithmes parallèles proposés sur GPU incluent le classique k-means et le calcul de forêt couvrante minimum pour la segmentation en superpixels. Ils incluent également un algorithme de recherche locale parallèle et un algorithme mémétique à base de population de solutions appliqués à l’estimation du flot optique via des appariements de superpixels. Tandis que les opérations sur les données exploitent le GPU, l’algorithme mémétique opère en tant que coalition de processus exécutés en parallèle sur le CPU multi-cœur et requérant des ressources GPU. Les images sont des nuages de points de l’espace euclidien 3D (domaine espace-intensité), et aussi des graphes auxquels sont associés des grilles de processeurs. Les kernels GPU exécutent des transformations en parallèle sous contrôle du CPU qui a un rôle réduit de détection des conditions d’arrêt et de séquencement des transformations.La contribution présentée est composée de deux grandes parties. Dans une première partie, nous présentons des outils pour la segmentation en superpixels. Une implémentation parallèle de l’algorithme des k-means est présentée et appliquée aux données 3D. Elle est basée sur une subdivision cellulaire de l’espace 3D qui permet des recherches de plus proche voisin en parallèle en temps optimal constant pour des distributions bornées. Nous présentons également une application de l’algorithme parallèle de calcul de forêt couvrante de Boruvka à la segmentation superpixel de type ligne de partage-des-eaux (watershed). Dans une deuxième partie, en se basant sur les superpixels générés, des procédures parallèles de mise en correspondance sont dérivées pour l’estimation du flot optique avec prise en compte des discontinuités. Ces méthodes incluent des heuristiques de construction et d’amélioration, telles que le winner-take-all et la recherche locale parallèle, et leur intégration dans une métaheuristique à base de population. Diverses combinaisons d’exécution sont présentées et évaluées en comparaison avec des algorithmes de l’état de l’art performantsFinding clusters in point clouds and matching graphs to graphs are recurrent tasks in computer science domain, data analysis, image processing, that are most often modeled as NP-hard optimization problems. With the development and accessibility of cheap multiprocessors, acceleration of the heuristic procedures for these tasks becomes possible and necessary. We propose parallel implantation on GPU (graphics processing unit) system for some generic algorithms applied here to image superpixel segmentation and image optical flow problem. The aim is to provide generic algorithms based on standard decentralized data structures to be easy to improve and customized on many optimization problems and parallel platforms.The proposed parallel algorithm implementations include classical k-means algorithm and application of minimum spanning forest computation for super-pixel segmentation. They include also a parallel local search procedure, and a population-based memetic algorithm applied to optical flow estimation based on superpixel matching. While data operations fully exploit GPU, the memetic algorithm operates like a coalition of processes executed in parallel on the multi-core CPU and requesting GPU resources. Images are point clouds in 3D Euclidean space (space-gray value domain), and are also graphs to which are assigned processor grids. GPU kernels execute parallel transformations under CPU control whose limited role only consists in stopping criteria evaluation or sequencing transformations.The presented contribution contains two main parts. Firstly, we present tools for superpixel segmentation. A parallel implementation of the k-means algorithm is presented with application to 3D data. It is based on a cellular grid subdivision of 3D space that allows closest point findings in constant optimal time for bounded distributions. We present an application of the parallel Boruvka minimum spanning tree algorithm to compute watershed minimum spanning forest. Secondly, based on the generated superpixels and segmentation, we derive parallel optimization procedures for optical flow estimation with edge aware filtering. The method includes construction and improvement heuristics, as winner-take-all and parallel local search, and their embedding into a population-based metaheuristic framework. The algorithms are presented and evaluated in comparison to state-of-the-art algorithms
33
Ayechew, Mossie A. "Genetic algorithms and Lagrangean based heuristics for vehicle routing." Thesis, Coventry University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324142.
Full text
34
Carvalho, Marcia Braga de. "Aplicações de meta-heuristica genetica e fuzzy no sistema de colonia de formigas para o problema do caixeiro viajante." [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261876.
Full textAbstract:
Orientador: Akebo YamakamiDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
Made available in DSpace on 2018-08-08T23:52:00Z (GMT). No. of bitstreams: 1 Carvalho_MarciaBragade_M.pdf: 2154346 bytes, checksum: caafd847980349294a73d2ad38d6414c (MD5) Previous issue date: 2007
Resumo: Dentre as várias técnicas heurísticas e exatas existentes para a resolução de problemas combinatórios, os algoritmos populacionais de otimização por colônia de formigas e genéticos têm se destacado devido à sua boa performance. Em especial os algoritmos de colônia de formigas são considerados atualmente como uma das técnicas mais bem sucedidas para a resolução de vários problemas combinatórios, dentre eles o problema do caixeiro viajante. Neste trabalho é apresentado um algoritmo híbrido que trabalha com as meta-heurísticas de sistema de colônia de formigas e genético conjuntamente aplicados no problema do caixeiro viajante simétrico. Além disso, apresentamos uma proposta para o algoritmo de formigas quando temos incertezas associadas aos parâmetros do problema. Os resultados obtidos com as metodologias propostas apresentam resultados satisfatórios para todas as instâncias utilizadas
Abstract: Amongst the several existing heuristical and accurate techniques for the resolution of combinatorial problems, the population algorithms ant colony optimization and genetic have been detached due to their good performance. In special the ant colony algorithms are considered currently as one of the techniques most succeeded for the resolution of some combinatorial problems, amongst them the travelling salesman problem. In this work is presented a hybrid algorithm which works with the ant colony system and genetic metaheuristics jointly applied in the symmetric travelling salesman problem. Moreover, we presented a proposal for the ant algorithm when we have uncertainties associated to problem parameters. The results gotten with the methodology proposals present resulted satisfactory for all the used instances
Mestrado
Automação
Mestre em Engenharia Elétrica
35
Onder, Ilter. "A Genetic Algorithm For Tsp With Backhauls Based On Conventional Heuristics." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608726/index.pdf.
Full textAbstract:
A genetic algorithm using conventional heuristics as operators is considered in this study for the traveling salesman problem with backhauls (TSPB). Properties of a crossover operator (Nearest Neighbor Crossover, NNX) based on the nearest neighbor heuristic and the idea of using more than two parents are investigated in a series of experiments. Different parent selection and replacement strategies and generation of multiple children are tried as well. Conventional improvement heuristics are also used as mutation operators. It has been observed that 2-edge exchange and node insertion heuristics work well with NNX using only two parents. The best settings among different alternatives experimented are applied on traveling salesman problem with backhauls (TSPB). TSPB is a problem in which there are two groups of customers. The aim is to minimize the distance traveled visiting all the cities, where the second group can be visited only after all cities in the first group are already visited. The approach we propose shows very good performance on randomly generated TSPB instances.
36
Lee, Yin Nam. "Sequential and parallel solutions of the convoy movement problem using branch-and-bound and heuristic hybrid techniques." Thesis, University of East Anglia, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296564.
Full text
37
Parker, Gary B. "Genetic algorithms for the development of real-time multi-heuristic search strategies." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23911.
Full text
38
Uzinski, Henrique [UNESP]. "Otimização de problemas multimodais usando meta-heurísticas evolutivas." Universidade Estadual Paulista (UNESP), 2014. http://hdl.handle.net/11449/115780.
Full textAbstract:
Made available in DSpace on 2015-03-03T11:52:33Z (GMT). No. of bitstreams: 0
Previous issue date: 2014-10-24Bitstream added on 2015-03-03T12:06:30Z : No. of bitstreams: 1
000808114.pdf: 869921 bytes, checksum: d49e859927277a17ca43ba9e2d635677 (MD5)Neste trabalho é proposta a resolução de problemas multimodais usando duas diferentes meta-heurísticas: Algoritmo Genético de Chu-Beasley modificado e o Algoritmo Genético de Chaves Aleatórias Viciadas (BRKGA), com foco principal nos resultados obtidos por esta última. É feita especificamente a implementação das meta-heurísticas e comparação dos resultados obtidos por estas diferentes técnicas. Uma característica muito importante do BRKGA é a estruturação que permite separar o algoritmo em duas parcelas claramente diferenciadas, uma parcela que depende exclusivamente das características do BRKGA e, portanto, independente do problema que se pretende resolver e outra parcela que depende exclusivamente das características especificas do problema que pretendemos resolver. Essa característica geral do BRKGA permite que ele seja facilmente aplicado a uma grande variedade de problemas, já que a primeira parcela pode ser integralmente aproveitada na resolução de um novo problema. Por outro lado, o Algoritmo Genético de Chu-Beasley (AGCB) é caracterizado pela substituição de um único indivíduo no ciclo geracional e pelo controle máximo de diversidade, mas isto não é suficiente para resolução de problemas complexos e multimodais, sendo assim, é apresentado o AGCB modificado, onde o critério de diversidade é estendido, a população inicial e o descendente gerado no ciclo geracional passa por uma melhoria local. Essas características tornam-o competitivo justificando a comparação com o BRKGA
In this work it is proposed the resolution of multimodal problems using two different meta- heuristics: Chu-Beasley’s Genetic Algorithm and Biased Random Key Genetic Algorithm (BRKGA), focusing mainly on the results obtained by the latter. Specifically the imple- mentation and comparison of results obtained by these different techniques is made. There are several metaheuristics, each with its own specific characteristics which have advan- tages and disadvantages for the resolution of certain problems and in several ways in the implementation and results. A very important feature of the BRKGA is the structure that allows to separate the algorithm into two clearly different parts, one part that depends exclusively on the characteristics of BRKGA and therefore independent of the problem to be solved and another part that depends exclusively on the specific characteristics of the problem we intend to solve. This general feature of the BRKGA allows it to be readily applied to a variety of problems, because the first component part can be fully utilized to solve a new problem. On the other hand, Chu-Beasley’s Genetic Algorithm (AGCB) is characterized by the replacement of a single individual in the generation cycle and by maximum control of diversity, but this is not enough to solve complex and multimodal problems, therefore it is presented the modified AGCB, where the diversity criterion is extended, the initial population and the descendant generated in the generational cycle passes through a local improvement. These features make it competitive, justifying the comparison with BRKGA
39
Uzinski, Henrique. "Otimização de problemas multimodais usando meta-heurísticas evolutivas /." Ilha Solteira, 2014. http://hdl.handle.net/11449/115780.
Full textAbstract:
Orientador: Rubén Augusto Romero LázaroBanca: Marina Lavorato de Oliveira
Banca: Marcelo Escobar de Oliveira
Resumo: Neste trabalho é proposta a resolução de problemas multimodais usando duas diferentes meta-heurísticas: Algoritmo Genético de Chu-Beasley modificado e o Algoritmo Genético de Chaves Aleatórias Viciadas (BRKGA), com foco principal nos resultados obtidos por esta última. É feita especificamente a implementação das meta-heurísticas e comparação dos resultados obtidos por estas diferentes técnicas. Uma característica muito importante do BRKGA é a estruturação que permite separar o algoritmo em duas parcelas claramente diferenciadas, uma parcela que depende exclusivamente das características do BRKGA e, portanto, independente do problema que se pretende resolver e outra parcela que depende exclusivamente das características especificas do problema que pretendemos resolver. Essa característica geral do BRKGA permite que ele seja facilmente aplicado a uma grande variedade de problemas, já que a primeira parcela pode ser integralmente aproveitada na resolução de um novo problema. Por outro lado, o Algoritmo Genético de Chu-Beasley (AGCB) é caracterizado pela substituição de um único indivíduo no ciclo geracional e pelo controle máximo de diversidade, mas isto não é suficiente para resolução de problemas complexos e multimodais, sendo assim, é apresentado o AGCB modificado, onde o critério de diversidade é estendido, a população inicial e o descendente gerado no ciclo geracional passa por uma melhoria local. Essas características tornam-o competitivo justificando a comparação com o BRKGA
Abstract: In this work it is proposed the resolution of multimodal problems using two different meta- heuristics: Chu-Beasley's Genetic Algorithm and Biased Random Key Genetic Algorithm (BRKGA), focusing mainly on the results obtained by the latter. Specifically the imple- mentation and comparison of results obtained by these different techniques is made. There are several metaheuristics, each with its own specific characteristics which have advan- tages and disadvantages for the resolution of certain problems and in several ways in the implementation and results. A very important feature of the BRKGA is the structure that allows to separate the algorithm into two clearly different parts, one part that depends exclusively on the characteristics of BRKGA and therefore independent of the problem to be solved and another part that depends exclusively on the specific characteristics of the problem we intend to solve. This general feature of the BRKGA allows it to be readily applied to a variety of problems, because the first component part can be fully utilized to solve a new problem. On the other hand, Chu-Beasley's Genetic Algorithm (AGCB) is characterized by the replacement of a single individual in the generation cycle and by maximum control of diversity, but this is not enough to solve complex and multimodal problems, therefore it is presented the modified AGCB, where the diversity criterion is extended, the initial population and the descendant generated in the generational cycle passes through a local improvement. These features make it competitive, justifying the comparison with BRKGA
Mestre
40
Šandera, Čeněk. "Heuristické algoritmy pro optimalizaci." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2008. http://www.nusl.cz/ntk/nusl-228326.
Full textAbstract:
Práce se zabývá určením pravděpodobnostních rozdělení pro stochastické programování, při kterém jsou optimální hodnoty účelové funkce extrémní (minimální nebo maximální). Rozdělení se určuje pomocí heuristických metod, konkrétně pomocí genetických algoritmů, kde celá populace aproximuje hledané rozdělení. První kapitoly popisují obecně matematické a stochastické programování a dále jsou popsány různé heuristické metody a s důrazem na genetické algoritmy. Těžiště práce je v naprogramování daného algoritmu a otestování na úlohách lineárních a kvadratických stochastických modelů.
41
Ozdemir, Ersin. "Evolutionary methods for the design of digital electronic circuits and systems." Thesis, Cardiff University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326874.
Full text
42
McGarvey, William. "Evaluating Heuristics and Crowding on Center Selection in K-Means Genetic Algorithms." NSUWorks, 2014. http://nsuworks.nova.edu/gscis_etd/31.
Full textAbstract:
Data clustering involves partitioning data points into clusters where data points within the same cluster have high similarity, but are dissimilar to the data points in other clusters. The k-means algorithm is among the most extensively used clustering techniques. Genetic algorithms (GA) have been successfully used to evolve successive generations of cluster centers. The primary goal of this research was to develop improved GA-based methods for center selection in k-means by using heuristic methods to improve the overall fitness of the initial population of chromosomes along with crowding techniques to avoid premature convergence. Prior to this research, no rigorous systematic examination of the use of heuristics and crowding methods in this domain had been performed.
The evaluation included computational experiments involving repeated runs of the genetic algorithm in which values that affect heuristics or crowding were systematically varied and the results analyzed. Genetic algorithm performance under the various configurations was analyzed based upon (1) the fitness of the partitions produced, and by (2) the overall time it took the GA to converge to good solutions. Two heuristic methods for initial center seeding were tested: Density and Separation. Two crowding techniques were evaluated on their ability to prevent premature convergence: Deterministic and Parent Favored Hybrid local tournament selection.
Based on the experiment results, the Density method provides no significant advantage over random seeding either in discovering quality partitions or in more quickly evolving better partitions. The Separation method appears to result in an increased probability of the genetic algorithm finding slightly better partitions in slightly fewer generations, and to more quickly converge to quality partitions. Both local tournament selection techniques consistently allowed the genetic algorithm to find better quality partitions than roulette-wheel sampling. Deterministic selection consistently found better quality partitions in fewer generations than Parent Favored Hybrid. The combination of Separation center seeding and Deterministic selection performed better than any other combination, achieving the lowest mean best SSE value more than twice as often as any other combination. On all 28 benchmark problem instances, the combination identified solutions that were at least as good as any identified by extant methods.
43
Hail, Nourredine. "Méthodes algorithmiques pour les lignes de production avec des machines parallèles." Université Joseph Fourier (Grenoble), 1995. http://www.theses.fr/1995GRE10019.
Full textAbstract:
Cette thèse présente un problème d'ordonnancement sur une ligne de production flexible. Dans une telle ligne, les postes de travail sont disposes séquentiellement, et chacun d'eux contient un certain nombre de machines parallèles identiques. Les pièces passent de poste en poste selon le même ordre et sont usinées par une des machines de chaque poste. Nos travaux portent sur l'étude de la minimisation de la date d'achèvement de la dernière pièce sur le dernier poste (makespan). Ce problème est np-difficile au sens fort. Nous étudions d'abord l'intérêt de ce type de ligne notamment en ce qui concerne la flexibilité, ensuite un état de l'art de ce domaine est présenté. Puis nous entamerons l'étude du flow shop flexible. Dans une première partie, nous proposons une borne inferieure pour le problème d'affectation (réduction à un seul poste). Ensuite nous développerons une heuristique pour ce cas particulier, en utilisant les algorithmes génétiques. Dans une seconde partie, nous présentons une heuristique pour un cas particulier du flow shop flexible à deux postes, puis on fera une étude théorique de sa performance. Nous proposons à la fin de cette thèse trois heuristiques pour le flow shop flexible, en utilisant trois méthodes différentes: l'amélioration locale, la méthode tabou et les algorithmes génétiques
44
Ponterosso, Pasquale. "Novel techniques of heuristically seeding genetic algorithms for engineering analysis and optimisation." Thesis, University of Portsmouth, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302440.
Full text
45
Kovàcs, Akos. "Solving the Vehicle Routing Problem with Genetic ALgorithm and Simulated Annealing." Thesis, Högskolan Dalarna, Datateknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3306.
Full textAbstract:
This Thesis Work will concentrate on a very interesting problem, the Vehicle Routing Problem (VRP). In this problem, customers or cities have to be visited and packages have to be transported to each of them, starting from a basis point on the map. The goal is to solve the transportation problem, to be able to deliver the packages-on time for the customers,-enough package for each Customer,-using the available resources- and – of course - to be so effective as it is possible.Although this problem seems to be very easy to solve with a small number of cities or customers, it is not. In this problem the algorithm have to face with several constraints, for example opening hours, package delivery times, truck capacities, etc. This makes this problem a so called Multi Constraint Optimization Problem (MCOP). What’s more, this problem is intractable with current amount of computational power which is available for most of us. As the number of customers grow, the calculations to be done grows exponential fast, because all constraints have to be solved for each customers and it should not be forgotten that the goal is to find a solution, what is best enough, before the time for the calculation is up. This problem is introduced in the first chapter: form its basics, the Traveling Salesman Problem, using some theoretical and mathematical background it is shown, why is it so hard to optimize this problem, and although it is so hard, and there is no best algorithm known for huge number of customers, why is it a worth to deal with it. Just think about a huge transportation company with ten thousands of trucks, millions of customers: how much money could be saved if we would know the optimal path for all our packages.Although there is no best algorithm is known for this kind of optimization problems, we are trying to give an acceptable solution for it in the second and third chapter, where two algorithms are described: the Genetic Algorithm and the Simulated Annealing. Both of them are based on obtaining the processes of nature and material science. These algorithms will hardly ever be able to find the best solution for the problem, but they are able to give a very good solution in special cases within acceptable calculation time.In these chapters (2nd and 3rd) the Genetic Algorithm and Simulated Annealing is described in details, from their basis in the “real world” through their terminology and finally the basic implementation of them. The work will put a stress on the limits of these algorithms, their advantages and disadvantages, and also the comparison of them to each other.Finally, after all of these theories are shown, a simulation will be executed on an artificial environment of the VRP, with both Simulated Annealing and Genetic Algorithm. They will both solve the same problem in the same environment and are going to be compared to each other. The environment and the implementation are also described here, so as the test results obtained.Finally the possible improvements of these algorithms are discussed, and the work will try to answer the “big” question, “Which algorithm is better?”, if this question even exists.
46
Thomas, Gina M. "Weibull parameter estimation using genetic algorithms and a heuristic approach to cut-set analysis." Ohio : Ohio University, 1995. http://www.ohiolink.edu/etd/view.cgi?ohiou1178901727.
Full text
47
Skidmore, Gerald. "Metaheuristics and combinatorial optimization problems /." Online version of thesis, 2006. https://ritdml.rit.edu/dspace/handle/1850/2319.
Full text
48
Viana, Monique Simplicio. "Algoritmo genético com operador de transgenia para minimização de makespan da programação reativa da produção." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/9087.
Full textAbstract:
Submitted by Alison Vanceto (alison-vanceto@hotmail.com) on 2017-08-30T12:26:40Z
No. of bitstreams: 1
DissMSV.pdf: 2771156 bytes, checksum: add74067c9db203edececa7202e83a52 (MD5)Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-09-20T14:06:15Z (GMT) No. of bitstreams: 1 DissMSV.pdf: 2771156 bytes, checksum: add74067c9db203edececa7202e83a52 (MD5)
Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-09-20T14:06:22Z (GMT) No. of bitstreams: 1 DissMSV.pdf: 2771156 bytes, checksum: add74067c9db203edececa7202e83a52 (MD5)
Made available in DSpace on 2017-09-20T14:11:11Z (GMT). No. of bitstreams: 1 DissMSV.pdf: 2771156 bytes, checksum: add74067c9db203edececa7202e83a52 (MD5) Previous issue date: 2016-08-29
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
In recent years, several studies have been carried out to minimize the production time (makespan) in a production schedule of a scenario that represents a manufacturing system. The problem of production scheduling is classified as a combinatorial problem belongs to the NP-hard class of computational problems. Furthermore, in a real world production system, there are many unexpected events (eg, review of production, entry of new products, breaking machines, etc.). To deal with the interruptions of the initial programming, we need to change any settings, which is called reactive production schedule or, simply, reactive scheduling. As a problem of combinatorial features, meta-heuristics is widely used in its resolution. This paper proposes a method that uses an evolutionary meta-heuristic Genetic Algorithm in conjunction with an operator called “Transgenics”, which allows to manipulate the genetic material of individuals adding features which are believed to be important, with the proposal to direct some population of individuals to a more favorable solution to the problem without removing the diversity of the population with a lower cost of time. The objective of this study is to use the Genetic Algorithm with transgenics operator obtain a reactive programming acceptable response time to minimize the makespan value. The objective of this study is to use the Genetic Algorithm with transgenics Operator obtain a reactive programming acceptable response time to minimize the makespan value. Experimental results show the proposed algorithm is able to bring better results than the makespan algorithm and compared in a shorter processing time due to the search direction which provides transgenic operator.
Nos últimos anos, várias pesquisas vêm sendo realizadas a fim de minimizar o tempo total de produção (makespan) em uma programação da produção de algum cenário que representa um sistema de manufatura. O problema da programação da produção é classificado como sendo um problema combinatório pertencente à classe NP-Hard dos problemas computacionais. Além disso, em um sistema de produção real, há muitos eventos inesperados (por exemplo, a revisão da produção, chegada de novos produtos, quebra máquinas, etc.). Para lidar com as interrupções da programação inicial, é preciso realizar outra programação, a qual é denominada de programação reativa da produção. Sendo um problema de recursos combinatórios, é amplamente utilizado metaheurísticas em sua resolução. Neste trabalho é proposto um método que faz uso de uma metaheurística evolutiva Algoritmo Genético em conjunto com um operador intitulado Operador de Transgenia, no qual possibilita manipular o material genético dos indivíduos acrescentando características das quais se acredita serem importantes, com a proposta de direcionar alguns indivíduos da população para uma solução mais favorável para o problema sem tirar a diversidade da população com um custo menor de tempo. O Objetivo deste trabalho é utilizando o Algoritmo Genético com Operador de Transgenia obter uma programação reativa em tempo de resposta aceitável, visando minimizar o valor de makespan. Resultados experimentais mostraram que algoritmo proposto foi capaz de trazer resultados de makespan melhores que os algoritmos comparados e em um menor tempo de processamento, devido ao direcionamento na busca que operador de transgenia proporciona.
49
Norman, Susan K. "HEURISTIC APPROACHES TO BATCHING JOBS IN PRINTED CIRCUIT BOARD ASSEMBLY." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin997881019.
Full text
50
Remde, Stephen M., Peter I. Cowling, Keshav P. Dahal, and N. J. Colledge. "Exact/heuristic hybrids using rVNS and hyperheuristics for workforce scheduling." Springer-Verlag, 2007. http://hdl.handle.net/10454/2510.
Full textAbstract:
In this paper we study a complex real-world workforce scheduling
problem. We propose a method of splitting the problem into smaller parts and
solving each part using exhaustive search. These smaller parts comprise a
combination of choosing a method to select a task to be scheduled and a method
to allocate resources, including time, to the selected task. We use reduced
Variable Neighbourhood Search (rVNS) and hyperheuristic approaches to
decide which sub problems to tackle. The resulting methods are compared to
local search and Genetic Algorithm approaches. Parallelisation is used to
perform nearly one CPU-year of experiments. The results show that the new
methods can produce results fitter than the Genetic Algorithm in less time and
that they are far superior to any of their component techniques. The method
used to split up the problem is generalisable and could be applied to a wide
range of optimisation problems.