Academic literature on the topic 'Greedy heuristic algorithms'
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Journal articles on the topic "Greedy heuristic algorithms"
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Wilt, Christopher, and Wheeler Ruml. "Building a Heuristic for Greedy Search." Proceedings of the International Symposium on Combinatorial Search 6, no. 1 (September 1, 2021): 131–40. http://dx.doi.org/10.1609/socs.v6i1.18352.
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Suboptimal heuristic search algorithms such as greedy best-first search allow us to find solutions when constraints of either time, memory, or both prevent the application of optimal algorithms such as A*. Guidelines for building an effective heuristic for A* are well established in the literature, but we show that if those rules are applied for greedy best-first search, performance can actually degrade. Observing what went wrong for greedy best-first search leads us to a quantitative metric appropriate for greedy heuristics, called Goal Distance Rank Correlation (GDRC). We demonstrate that GDRC can be used to build effective heuristics for greedy best-first search automatically.
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Wilt, Christopher, and Wheeler Ruml. "Effective Heuristics for Suboptimal Best-First Search." Journal of Artificial Intelligence Research 57 (October 31, 2016): 273–306. http://dx.doi.org/10.1613/jair.5036.
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Suboptimal heuristic search algorithms such as weighted A* and greedy best-first search are widely used to solve problems for which guaranteed optimal solutions are too expensive to obtain. These algorithms crucially rely on a heuristic function to guide their search. However, most research on building heuristics addresses optimal solving. In this paper, we illustrate how established wisdom for constructing heuristics for optimal search can fail when considering suboptimal search. We consider the behavior of greedy best-first search in detail and we test several hypotheses for predicting when a heuristic will be effective for it. Our results suggest that a predictive characteristic is a heuristic's goal distance rank correlation (GDRC), a robust measure of whether it orders nodes according to distance to a goal. We demonstrate that GDRC can be used to automatically construct abstraction-based heuristics for greedy best-first search that are more effective than those built by methods oriented toward optimal search. These results reinforce the point that suboptimal search deserves sustained attention and specialized methods of its own.
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Hignasari, L. Virginayoga. "Komparasi Algoritma Cheapest Insertion Heuristic (CIH) Dan Greedy Dalam Optimasi Rute Pendistribusian Barang." Jurnal Ilmiah Vastuwidya 2, no. 2 (June 16, 2020): 31–39. http://dx.doi.org/10.47532/jiv.v2i2.87.
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This study was aimed to compare algorithms that can effectively provide better solutions related to the problem of determining the shortest route in the distribution of goods. This research was a qualitative research. The object of research was the route of shipping goods of a business that is engaged in printing and convection. The algorithms compared in this study were Cheapest Insertion Heuristic (CIH) and Greedy algorithms. Both algorithms have advantages and disadvantages in finding the shortest route. From the results of the analysis using these two algorithms, the Cheapest Insertion Heuristic (CIH) and Greedy algorithm can provide almost the same optimization results. The difference was only the selection of the journey. The strength of the Greedy algorithm was that the calculation steps are simpler than the Cheapest Insertion Heuristic (CIH) algorithm. While the disadvantage of the Greedy algorithm was that it is inappropriate to find the shortest route with a relatively large number of places visited. The advantage of the Cheapest Insertion Heuristic (CIH) algorithm was that this algorithm is still stable, used for the relatively large number of places visited. While the lack of Cheapest Insertion Heuristic (CIH) algorithm was a complicated principle of calculation and was relatively longer than the Greedy algorithm.
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Tian, Heng, Fuhai Duan, Yong Sang, and Liang Fan. "Novel algorithms for sequential fault diagnosis based on greedy method." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234, no. 6 (May 2, 2020): 779–92. http://dx.doi.org/10.1177/1748006x20914498.
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Test sequencing for binary systems is a nondeterministic polynomial-complete problem, where greedy algorithms have been proposed to find the solution. The traditional greedy algorithms only extract a single kind of information from the D-matrix to search the optimal test sequence, so their application scope is limited. In this study, two novel greedy algorithms that combine the weight index for fault detection with the information entropy are introduced for this problem, which are defined as the Mix1 algorithm and the Mix2 algorithm. First, the application scope for the traditional greedy algorithms is demonstrated in detail by stochastic simulation experiments. Second, two new heuristic formulas are presented, and their scale factors are determined. Third, an example is used to show how the two new algorithms work, and four real-world D-matrices are employed to validate their universality and stability. Finally, the application scope of the Mix1 and Mix2 algorithms is determined based on stochastic simulation experiments, and the two greedy algorithms are also used to improve a multistep look-ahead heuristic algorithm. The Mix1 and Mix2 algorithms can obtain good results in a reasonable time and have a wide application scope, which also can be used to improve the multistep look-ahead heuristic algorithm.
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Panggabean, Jonas Franky R. "Hybrid Ant Colony Optimization-Genetics Algorithm to Minimize Makespan Flow Shop Scheduling." International Journal of Engineering & Technology 7, no. 2.2 (March 5, 2018): 40. http://dx.doi.org/10.14419/ijet.v7i2.2.11868.
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Flow shop scheduling is a scheduling model in which the job to be processed entirely flows in the same product direction / path. In other words, jobs have routing work together. Scheduling problems often arise if there is n jobs to be processed on the machine m, which must be specified which must be done first and how to allocate jobs on the machine to obtain a scheduled production process. In research of Zini, H and ElBernoussi, S. (2016) NEH Heuristic and Stochastic Greedy Heuristic (SG) algorithms. This paper presents modified harmony search (HS) for flow shop scheduling problems with the aim of minimizing the maximum completion time of all jobs (makespan). To validate the proposed algorithm this computational test was performed using a sample dataset of 60 from the Taillard Benchmark. The HS algorithm is compared with two constructive heuristics of the literature namely the NEH heuristic and stochastic greedy heuristic (SG). The experimental results were obtained on average for the dataset size of 20 x 5 to 50 x 10, that the ACO-GA algorithm has a smaller makespan than the other two algorithms, but for large-size datasets the ACO-GA algorithm has a greater makespan of both algorithms with difference of 1.4 units of time.
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Seeja, K. R. "HybridHAM: A Novel Hybrid Heuristic for Finding Hamiltonian Cycle." Journal of Optimization 2018 (October 16, 2018): 1–10. http://dx.doi.org/10.1155/2018/9328103.
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Hamiltonian Cycle Problem is one of the most explored combinatorial problems. Being an NP-complete problem, heuristic approaches are found to be more powerful than exponential time exact algorithms. This paper presents an efficient hybrid heuristic that sits in between the complex reliable approaches and simple faster approaches. The proposed algorithm is a combination of greedy, rotational transformation and unreachable vertex heuristics that works in three phases. In the first phase, an initial path is created by using greedy depth first search. This initial path is then extended to a Hamiltonian path in second phase by using rotational transformation and greedy depth first search. Third phase converts the Hamiltonian path into a Hamiltonian cycle by using rotational transformation. The proposed approach could find Hamiltonian cycles from a set of hard graphs collected from the literature, all the Hamiltonian instances (1000 to 5000 vertices) given in TSPLIB, and some instances of FHCP Challenge Set. Moreover, the algorithm has O(n3) worst case time complexity. The performance of the algorithm has been compared with the state-of-the-art algorithms and it was found that HybridHAM outperforms others in terms of running time.
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Selvi, V. "Clustering Analysis of Greedy Heuristic Method in Zero_One Knapsack Problem." International Journal of Emerging Research in Management and Technology 6, no. 7 (June 29, 2018): 39. http://dx.doi.org/10.23956/ijermt.v6i7.181.
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Knapsack problem is a surely understood class of optimization problems, which tries to expand the profit of items in a knapsack without surpassing its capacity, Knapsack can be solved by several algorithms such like Greedy, dynamic programming, Branch & bound etc. The solution to the zero_one knapsack problem (KP) can be viewed as the result of a sequence of decision. Clustering is the process of resolving that type of applications. Different clustering application for grouping elements with equal priority. In this paper we are introducing greedy heuristic algorithm for solving zero_one knapsack problem. We will exhibit a relative investigation of the Greedy, dynamic programming, B&B and Genetic algorithms regarding of the complexity of time requirements, and the required programming efforts and compare the total value for each of them. Greedy and Genetic algorithms can be used to solve the 0-1 Knapsack problem within a reasonable time complexity. The worst-case time complexity (Big-O) of both algorithms is O(N). Using the greedy method, the algorithm can produce high quality clusters while reduce time the best partioning avoid the memory confinement problem during the process.
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Silva Arantes, Jesimar da, Márcio da Silva Arantes, Claudio Fabiano Motta Toledo, Onofre Trindade Júnior, and Brian Charles Williams. "Heuristic and Genetic Algorithm Approaches for UAV Path Planning under Critical Situation." International Journal on Artificial Intelligence Tools 26, no. 01 (February 2017): 1760008. http://dx.doi.org/10.1142/s0218213017600089.
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The present paper applies a heuristic and genetic algorithms approaches to the path planning problem for Unmanned Aerial Vehicles (UAVs), during an emergency landing, without putting at risk people and properties. The path re-planning can be caused by critical situations such as equipment failures or extreme environmental events, which lead the current UAV mission to be aborted by executing an emergency landing. This path planning problem is introduced through a mathematical formulation, where all problem constraints are properly described. Planner algorithms must define a new path to land the UAV following problem constraints. Three path planning approaches are introduced: greedy heuristic, genetic algorithm and multi-population genetic algorithm. The greedy heuristic aims at quickly find feasible paths, while the genetic algorithms are able to return better quality solutions within a reasonable computational time. These methods are evaluated over a large set of scenarios with different levels of diffculty. Simulations are also conducted by using FlightGear simulator, where the UAV’s behaviour is evaluated for different wind velocities and wind directions. Statistical analysis reveal that combining the greedy heuristic with the genetic algorithms is a good strategy for this problem.
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K, Gayathri Devi. "A Hybrid Firefly Algorithm Approach for Job Shop Scheduling Problem." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 1436–44. http://dx.doi.org/10.22214/ijraset.2021.39536.
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Abstract: Job shop scheduling has always been one of the most sought out research problems in Combinatorial optimization. Job Shop Scheduling problems (JSSP) are categorized under NP hard problems. In recent years the meta heuristic algorithms have been proved effective to solve hardcore NP problem. Firefly Algorithm is one of such meta heuristic techniques which is nature inspired from firefly characteristic. Its potential can be enhanced further by hybridizing it with other known evolutionary algorithms and thereby getting improved results in less computational time. In this paper we have proposed a new hybrid technique christened as HyFA, by hybridizing Firefly algorithm(FA) with simulated annealing (SA) and Greedy heuristics approach (GHA). The hybrid technique has the advantages of all three algorithms and are combined in such a way that a quicker and better optimal solution is obtained. Our proposed HyFA is coded in Matlab with an objective to minimize the makespan (Cm). The novel hybrid technique is then used to evaluate 1-25 Lawrence problems taken from literature. The results show the proposed technique is more effective not only in getting optimal results but has significantly reduced computational time. Keywords: Scheduling, Optimisation, Job shop scheduling, Meta-heuristics, Firefly, Simulated Annealing, Greedy heuristics Approach.
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Kazakovtsev, Lev, Dmitry Stashkov, Mikhail Gudyma, and Vladimir Kazakovtsev. "Algorithms with greedy heuristic procedures for mixture probability distribution separation." Yugoslav Journal of Operations Research 29, no. 1 (2019): 51–67. http://dx.doi.org/10.2298/yjor171107030k.
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For clustering problems based on the model of mixture probability distribution separation, we propose new Variable Neighbourhood Search algorithms (VNS) and evolutionary genetic algorithms (GA) with greedy agglomerative heuristic procedures and compare them with known algorithms. New genetic algorithms implement a global search strategy with the use of a special crossover operator based on greedy agglomerative heuristic procedures in combination with the EM algorithm (Expectation Maximization). In our new VNS algorithms, this combination is used for forming randomized neighbourhoods to search for better solutions. The results of computational experiments made on classical data sets and the testings of production batches of semiconductor devices shipped for the space industry demonstrate that new algorithms allow us to obtain better results, higher values of the log likelihood objective function, in comparison with the EM algorithm and its modifications.
Dissertations / Theses on the topic "Greedy heuristic algorithms"
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Mathirajan, M. "Heuristic Scheduling Algorithms For Parallel Heterogeneous Batch Processors." Thesis, Indian Institute of Science, 2000. http://hdl.handle.net/2005/196.
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In the last decade, market pressures for greater variety of products forced a gradual shift from continuous manufacturing to batch manufacturing in various industries. Consequently batch scheduling problems have attracted the attention of researchers in production and operations management.
This thesis addresses the scheduling of parallel non-identical batch processors in the presence of dynamic job arrivals, incompatible job-families and non-identical job sizes. This problem abstracts the scheduling of heat-treatment furnace operations of castings in a steel foundry. The problem is of considerable interest in this sector as a large proportion of the total production time is spent in heat treatment processing. This problem is also encountered in other industrial settings such as burn-in operation in the final testing stage of semiconductor manufacturing, and manufacturing of steel, ceramics, aircraft parts, footwear, etc. A detailed literature review and personal communications with experts revealed that this class of batch scheduling problems have not been addressed hitherto.
A major concern in the management of foundries is to maximize throughput and reduce flow time and work-in-process inventories. Therefore we have chosen the primary scheduling objective to be the utilization of batch processors and as secondary objectives the minimization of overall flow time and weighted average waiting time per job. This formulation can be considered as an extension of problems studied by DOBSON AND NAMBINADOM (1992), UZSOY (1995), ZEE et a/. (1997) and MEHTA AND UZSOY (1998). Our effort to carefully catalogue the large number of variants of deterministic batch scheduling problems led us to the development of a taxonomy and notation.
Not surprisingly, we are able to show that our problem is NP-hard and is therefore in the company of many scheduling problems that are difficult to solve. Initially two heuristic algorithms, one a mathematical programming based heuristic algorithm (MPHA) and the other a greedy heuristic algorithm were developed. Due to the computational overheads in the implementation of MPHA when compared with the greedy heuristic, we chose to focus on the latter as the primary scheduling methodology.
Preliminary experimentation led us to the observation that the performance of greedy heuristics depends critically on the selection of job-families. So eight variants of the greedy heuristic that differ mainly in the decision on "job-family selection" were proposed. These eight heuristics are basically two sets {Al, A2, A3, A4} and the modified (MAI, MA2, MA3, MA4}, which differ only on how the "job-family" index, weighted shortest processing time, is computed.
For evaluating the performance of the eight heuristics, computational experiments were carried out. The analysis of the experimental data is presented in two perspectives. The goal of the first perspective was to evaluate the absolute quality of the solutions obtained by the proposed heuristic algorithms when compared with estimated optimal solutions. The second perspective was to compare the relative performance of the proposed heuristics.
The test problems generated were designed to reflect real-world scheduling problems that we have observed in the steel-casting industry. Three important problem parameters for the test set generation are the number of jobs [n], job-priority [P], and job-family [F]. We considered 5 different levels for n, 2 different levels for P and 2 different levels for F. The test set reflects (i) the size of the jobs vary uniformly (ii) there are two batch processors and (iii) five incompatible job-families with different processing times. 15 problem instances were generated for each level of (n, P, and F).
Out of many procedures available in the literature for estimating optimal value for combinatorial optimization problems, we used the procedure based on Weibull distribution as discussed in Rardin and Uzsoy (2001). For each problem instance of the randomly generated 300 problem instances, 15 feasible solutions (i.e., the average utilization of batch processors (AUBP)) were obtained using "random decision rule for first two stages and using a "best-fit heuristic' for the last stage of the scheduling problem. These 15 feasible solutions were used to estimate the optimal value. The generated 15 feasible solutions are expected to provide the estimated optimal value of the problem instance with a very high probability.
Both average performance and worst-case performance of the heuristics indicated that, the heuristic algorithms A3 and A4, on the average yielded better utilization than the estimated optimal value. This indicates that the Weibull-based technique may have yielded conservative estimates of the optimal value. Further, the other heuristic algorithms found inferior solutions when compared with the estimated optimal value. But the deviations were very small. From this, we may infer that all the proposed heuristic algorithms are acceptable.
The relative evaluation of heuristics was in terms of both computational effort and the quality of the solution. For the heuristics, it was clear that the computational burden is low enough on the average to run all the proposed heuristics on each problem instance and select the best solution. Also, it is observed that any algorithm from the first set of {Al, A2, A3, and A4} takes more computational time than any one from the second set {MAI, MA2, MA3, and MA4}. Regarding solution quality, the following inferences were made:
٭ In general the heuristic algorithms are sensitive to the choice of problem factors with
respect to all the scheduling objectives.
٭ The three algorithms A3, MA4 and MAI are observed to be superior with respect to the scheduling objectives: maximizing average utilization of batch processors (AUBP),
minimization of overall flow time (OFT) and minimizing weighted average waiting time
(WAWT) respectively. Further, the heuristic algorithm MAI turns out to be the best
choice if we trade-off all three objectives AUBP, OFT and WAWT.
Finally we carried out simple sensitivity analyses experiments in order to understand the influence of some parameters of the scheduling on the performance of the heuristic algorithms. These were related to one at a time changes in (1) job-size distribution, (2) capacities of batch processors and (3) processing time of job-families. From the analyses it appears that there is an influence of changes in these input parameters. The results of the sensitivity analyses can be used to guide the selection of a heuristic for a particular combination of input parameters. For example, if we have to pick a single heuristic algorithm, then MAI is the best choice when considering the performance and the robustness indicated by the sensitivity analysis.
In summary, this thesis examined a problem arising in the scheduling of heat-treatment operations in the steel-casting industry. This problem was abstracted to a class of deterministic batch scheduling problems. We analyzed the computational complexity of this problem and showed that it is NP-hard and therefore unlikely to admit a scalable exact method. Eight variants of a fast greedy heuristic were designed to solve the scheduling problem of interest.
Extensive computational experiments were carried out to compare the performance of the heuristics with estimated optimal values (using the Weibull technique) and also for relative effectiveness and this showed that the heuristics are capable of consistently obtaining near-estimated) optimal solutions with very low computational burden for the solution of large scale problems. Finally, a comprehensive sensitivity analysis was carried out to study the influence of a few parameters, by changing them one at a time, on the performance of the heuristic algorithms. This type of analysis gives users some confidence in the robustness of the proposed heuristics.
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Turlapaty, Sandhya. "Implementation and Performance Comparison of Some Heuristic Algorithms for Block Sorting." UNF Digital Commons, 2018. https://digitalcommons.unf.edu/etd/816.
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An implementation framework has been developed in this thesis for a well-known APX-hard combinatorial optimization problem known as Block Sorting. The motivation for the study of this problem comes from applications such as computational biology and optical character recognition. While existing Block Sorting research has been theoretically focused on the development and analysis of several approximation algorithms for Block Sorting, little or no work has been carried out thus far on the implementation of the proposed approximation algorithms. The conceptualization of an implementation framework and illustrating its use by experimenting with the existing approximation algorithms will provide means for discovering newer approaches to handling this important problem. As the main contribution, the research in this thesis provides a new greedy algorithm for Block Sorting in which each block move either reduces the number of blocks by two or three blocks, or reduces by one the number of reversals or inversions in the orig- inal permutation. Experimental results for all algorithms are also provided along with a comparison of their performance using the number of block moves and approximation ratios as performance metrics when sorting permutations of a given order, and as the order of permutations is varied. Preliminary results from the experimentation were shared at the 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) [1]. To the best of our knowledge, this is the first work that has been focused on the implementation and experimental performance analysis of any algorithm for Block Sorting. We believe the results presented in this thesis will be useful for researchers and practitioners working in this area.
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Neas, Charles Bennett. "A Greedy Search Algorithm for Maneuver-Based Motion Planning of Agile Vehicles." Thesis, Virginia Tech, 2010. http://hdl.handle.net/10919/36213.
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This thesis presents a greedy search algorithm for maneuver-based motion planning of agile vehicles. In maneuver-based motion planning, vehicle maneuvers are solved offline and saved in a library to be used during motion planning. From this library, a tree of possible vehicle states can be generated through the search space. A depth-first, library-based algorithm called AD-Lib is developed and used to quickly provide feasible trajectories along the tree. AD-Lib combines greedy search techniques with hill climbing and effective backtracking to guide the search process rapidly towards the goal. Using simulations of a four-thruster hovercraft, AD-Lib is compared to existing suboptimal search algorithms in both known and unknown environments with static obstacles. AD-Lib is shown to be faster than existing techniques, at the expense of increased path cost. The motion planning strategy of AD-Lib along with a switching controller is also tested in an environment with dynamic obstacles.Master of Science
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TAKADA, Hiroaki, Hiroyuki TOMIYAMA, Gang ZENG, and Tetsuo YOKOYAMA. "Static Task Scheduling Algorithms Based on Greedy Heuristics for Battery-Powered DVS Systems." Institute of Electronics, Information and Communication Engineers, 2010. http://hdl.handle.net/2237/15037.
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Hossain, Mohammad Forhad. "Spanning Tree Approach On The Snow Cleaning Problem." Thesis, Högskolan Dalarna, Datateknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4847.
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Snow cleaning is one of the important tasks in the winter time in Sweden. Every year government spends huge amount money for snow cleaning purpose. In this thesis we generate a shortest road network of the city and put the depots in different place of the city for snow cleaning. We generate shortest road network using minimum spanning tree algorithm and find the depots position using greedy heuristic. When snow is falling, vehicles start work from the depots and clean the snow all the road network of the city. We generate two types of model. Models are economic model and efficient model. Economic model provide good economical solution of the problem and it use less number of vehicles. Efficient model generate good efficient solution and it take less amount of time to clean the entire road network.
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Althoby, Haeder Younis Ghawi. "Theoritical and numerical studies on the graph partitioning problem." Thesis, Normandie, 2017. http://www.theses.fr/2017NORMC233/document.
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Étant donné G = (V, E) un graphe non orienté connexe et un entier positif β (n), où n est le nombrede sommets de G, le problème du séparateur (VSP) consiste à trouver une partition de V en troisclasses A, B et C de sorte qu'il n'y a pas d'arêtes entre A et B, max {| A |, | B |} est inférieur ou égal àβ (n) et | C | est minimum. Dans cette thèse, nous considérons une modélisation du problème sous laforme d'un programme linéaire en nombres entiers. Nous décrivons certaines inégalités valides et etdéveloppons des algorithmes basés sur un schéma de voisinage.Nous étudions également le problème du st-séparateur connexe. Soient s et t deux sommets de Vnon adjacents. Un st-séparateur connexe dans le graphe G est un sous-ensemble S de V \ {s, t} quiinduit un sous-graphe connexe et dont la suppression déconnecte s de t. Il s'agit de déterminer un stséparateur de cardinalité minimum. Nous proposons trois formulations pour ce problème et donnonsdes inégalités valides du polyèdre associé à ce problème. Nous présentons aussi une heuristiqueefficace pour résoudre ce problèmeGiven G=(V,E) a connected undirected graph and a positive integer β(n), where n is number ofvertices, the vertex separator problem (VSP) is to find a partition of V into three classes A,B and Csuch that there is no edge between A and B, max{|A|,|B|}less than or equal β(n), and |C| isminimum. In this thesis, we consider aninteger programming formulation for this problem. Wedescribe some valid inequalties and using these results to develop algorithms based onneighborhood scheme.We also study st-connected vertex separator problem. Let s and tbe two disjoint vertices of V, notadjacent. A st-connected separator in the graph G is a subset S of V\{s,t} such that there are no morepaths between sand tin G[G\S] and the graph G[S] is connected . The st-connected vertex speratorproblem consists in finding such subset with minimum cardinality. We propose three formulationsfor this problem and give some valid inequalities for the polyhedron associated with this problem.We develop also an efficient heuristic to solve this problem
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Berger, Karl-Eduard. "Placement de graphes de tâches de grande taille sur architectures massivement multicoeurs." Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLV026/document.
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Ce travail de thèse de doctorat est dédié à l'étude d'un problème de placement de tâches dans le domaine de la compilation d'applications pour des architectures massivement parallèles. Ce problème vient en réponse à certains besoins industriels tels que l'économie d'énergie, la demande de performances pour les applications de type flots de données synchrones. Ce problème de placement doit être résolu dans le respect de trois critères: les algorithmes doivent être capable de traiter des applications de tailles variables, ils doivent répondre aux contraintes de capacités des processeurs et prendre en compte la topologie des architectures cibles. Dans cette thèse, les tâches sont organisées en réseaux de communication, modélisés sous forme de graphes. Pour évaluer la qualité des solutions produites par les algorithmes, les placements obtenus sont comparés avec un placement aléatoire. Cette comparaison sert de métrique d'évaluation des placements des différentes méthodes proposées. Afin de résoudre à ce problème, deux algorithmes de placement de réseaux de tâches de grande taille sur des architectures clusterisées de processeurs de type many-coeurs ont été développés. Ils s'appliquent dans des cas où les poids des tâches et des arêtes sont unitaires. Le premier algorithme, nommé Task-wise Placement, place les tâches une par une en se servant d'une notion d'affinité entre les tâches. Le second, intitulé Subgraph-wise Placement, rassemble les tâches en groupes puis place les groupes de tâches sur les processeurs en se servant d'une relation d'affinité entre les groupes et les tâches déjà affectées. Ces algorithmes ont été testés sur des graphes, représentants des applications, possédant des topologies de types grilles ou de réseaux de portes logiques. Les résultats des placements sont comparés avec un algorithme de placement, présent dans la littérature qui place des graphes de tailles modérée et ce à l'aide de la métrique définie précédemment. Les cas d'application des algorithmes de placement sont ensuite orientés vers des graphes dans lesquels les poids des tâches et des arêtes sont variables similairement aux valeurs qu'on peut retrouver dans des cas industriels. Une heuristique de construction progressive basée sur la théorie des jeux a été développée. Cet algorithme, nommé Regret Based Approach, place les tâches une par une. Le coût de placement de chaque tâche en fonction des autres tâches déjà placées est calculée. La phase de sélection de la tâche se base sur une notion de regret présente dans la théorie des jeux. La tâche qu'on regrettera le plus de ne pas avoir placée est déterminée et placée en priorité. Afin de vérifier la robustesse de l'algorithme, différents types de graphes de tâches (grilles, logic gate networks, series-parallèles, aléatoires, matrices creuses) de tailles variables ont été générés. Les poids des tâches et des arêtes ont été générés aléatoirement en utilisant une loi bimodale paramétrée de manière à obtenir des valeurs similaires à celles des applications industrielles. Les résultats de l'algorithme ont également été comparés avec l'algorithme Task-Wise Placement, qui a été spécialement adapté pour les valeurs non unitaires. Les résultats sont également évalués en utilisant la métrique de placement aléatoireThis Ph.D thesis is devoted to the study of the mapping problem related to massively parallel embedded architectures. This problem arises from industrial needs like energy savings, performance demands for synchronous dataflow applications. This problem has to be solved considering three criteria: heuristics should be able to deal with applications with various sizes, they must meet the constraints of capacities of processors and they have to take into account the target architecture topologies. In this thesis, tasks are organized in communication networks, modeled as graphs. In order to determine a way of evaluating the efficiency of the developed heuristics, mappings, obtained by the heuristics, are compared to a random mapping. This comparison is used as an evaluation metric throughout this thesis. The existence of this metric is motivated by the fact that no comparative heuristics can be found in the literature at the time of writing of this thesis. In order to address this problem, two heuristics are proposed. They are able to solve a dataflow process network mapping problem, where a network of communicating tasks is placed into a set of processors with limited resource capacities, while minimizing the overall communication bandwidth between processors. They are applied on task graphs where weights of tasks and edges are unitary set. The first heuristic, denoted as Task-wise Placement, places tasks one after another using a notion of task affinities. The second algorithm, named Subgraph-wise Placement, gathers tasks in small groups then place the different groups on processors using a notion of affinities between groups and processors. These algorithms are tested on tasks graphs with grid or logic gates network topologies. Obtained results are then compared to an algorithm present in the literature. This algorithm maps task graphs with moderated size on massively parallel architectures. In addition, the random based mapping metric is used in order to evaluate results of both heuristics. Then, in a will to address problems that can be found in industrial cases, application cases are widen to tasks graphs with tasks and edges weights values similar to those that can be found in the industry. A progressive construction heuristic named Regret Based Approach, based on game theory, is proposed. This heuristic maps tasks one after another. The costs of mapping tasks according to already mapped tasks are computed. The process of task selection is based on a notion of regret, present in game theory. The task with the highest value of regret for not placing it, is pointed out and is placed in priority. In order to check the strength of the algorithm, many types of task graphs (grids, logic gates networks, series-parallel, random, sparse matrices) with various size are generated. Tasks and edges weights are randomly chosen using a bimodal law parameterized in order to have similar values than industrial applications. Obtained results are compared to the Task Wise placement, especially adapted for non-unitary values. Moreover, results are evaluated using the metric defined above
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Lundquist, Josefin, and Linnéa O'Hara. "An optimization model using the Assignment Problem to manage the location of parts : Master Thesis at the engine assembly at Scania CV AB." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-137825.
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A key challenge for manufacturing companies is to store parts in an efficient way atthe lowest cost possible. As the demand of differentiated products increases, togetherwith the fact that old products are not phased out at the same pace, the need of usingstorage space as dynamically as possible becomes vital.Scania’s engine assembly manufactures engines for various automotive vehicles andmarine & industry applications. The variation in engine range in Scania’s offeringleads to the need of holding a vast, and increasing, assortment of parts in the produc-tion. As a consequence, this puts more pressure on the logistics and furnishing withinthe engine assembly.This master thesis aims to facilitate the process of assigning parts’ storage locationsin the most profitable manner through an optimization model, the Location Model, inExcel VBA. Together with the model, suggestions of work methods are presented.By implementing the Location Model at Scania’s engine assembly, 4,98 % of all keptparts are recommended location changes, while resulting in cost savings, for the chosen30-day period. These location changes result in a cost saving of 6,73 % of the totallogistic costs for the same time period.
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Combier, Camille. "Mesures de similarité pour cartes généralisées." Phd thesis, Université Claude Bernard - Lyon I, 2012. http://tel.archives-ouvertes.fr/tel-00995382.
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Une carte généralisée est un modèle topologique permettant de représenter implicitementun ensemble de cellules (sommets, arêtes, faces , volumes, . . .) ainsi que l'ensemblede leurs relations d'incidence et d'adjacence au moyen de brins et d'involutions. Les cartes généralisées sont notamment utilisées pour modéliser des images et objets3D. A ce jour il existe peu d'outils permettant l'analyse et la comparaison de cartes généralisées.Notre objectif est de définir un ensemble d'outils permettant la comparaisonde cartes généralisées.Nous définissons tout d'abord une mesure de similarité basée sur la taille de la partiecommune entre deux cartes généralisées, appelée plus grande sous-carte commune.Nous définissons deux types de sous-cartes, partielles et induites, la sous-carte induitedoit conserver toutes les involutions tandis que la sous-carte partielle autorise certaines involutions à ne pas être conservées. La sous-carte partielle autorise que les involutionsne soient pas toutes conservées en analogie au sous-graphe partiel pour lequelles arêtes peuvent ne pas être toutes présentes. Ensuite nous définissons un ensembled'opérations de modification de brins et de coutures pour les cartes généralisées ainsiqu'une distance d'édition. La distance d'édition est égale au coût minimal engendrépar toutes les successions d'opérations transformant une carte généralisée en une autrecarte généralisée. Cette distance permet la prise en compte d'étiquettes, grâce à l'opérationde substitution. Les étiquettes sont posées sur les brins et permettent d'ajouter del'information aux cartes généralisées. Nous montrons ensuite, que pour certains coûtsnotre distance d'édition peut être calculée directement à partir de la plus grande souscartecommune.Le calcul de la distance d'édition est un problème NP-difficile. Nous proposons unalgorithme glouton permettant de calculer en temps polynomial une approximation denotre distance d'édition de cartes. Nous proposons un ensemble d'heuristiques baséessur des descripteurs du voisinage des brins de la carte généralisée permettant de guiderl'algorithme glouton, et nous évaluons ces heuristiques sur des jeux de test générésaléatoirement, pour lesquels nous connaissons une borne de la distance.Nous proposons des pistes d'utilisation de nos mesures de similarités dans le domainede l'analyse d'image et de maillages. Nous comparons notre distance d'éditionde cartes généralisées avec la distance d'édition de graphes, souvent utilisée en reconnaissancede formes structurelles. Nous définissons également un ensemble d'heuristiquesprenant en compte les étiquettes de cartes généralisées modélisant des images etdes maillages. Nous mettons en évidence l'aspect qualitatif de notre appariement, permettantde mettre en correspondance des zones de l'image et des points du maillages.
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De, Souza Bento Da Silva Pedro Paulo. "On the mapping of distributed applications onto multiple Clouds." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEN089/document.
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Le Cloud est devenu une plate-forme très répandue pour le déploiement d'applications distribuées. Beaucoup d'entreprises peuvent sous-traiter leurs infrastructures d'hébergement et, ainsi, éviter des dépenses provenant d'investissements initiaux en infrastructure et de maintenance.Des petites et moyennes entreprises, en particulier, attirés par le modèle de coûts sur demande du Cloud, ont désormais accès à des fonctionnalités comme le passage à l'échelle, la disponibilité et la fiabilité, qui avant le Cloud étaient presque réservées à de grandes entreprises.Les services du Cloud peuvent être offerts aux utilisateurs de plusieurs façons. Dans cette thèse, nous nous concentrons sur le modèle d'Infrastructure sous Forme de Service. Ce modèle permet aux utilisateurs d’accéder à des ressources de calcul virtualisés sous forme de machine virtuelles (MVs).Pour installer une application distribuée, un client du Cloud doit d'abord définir l'association entre son application et l'infrastructure. Il est nécessaire de prendre en considération des contraintesde coût, de ressource et de communication pour pouvoir choisir un ensemble de MVs provenant d'opérateurs de Cloud publiques et privés le plus adaptés. Cependant, étant donné la quantité exponentiel de configurations, la définition manuelle de l'association entre application et infrastructure peut être un challenge dans des scénarios à large échelle ou ayant des contraintes importantes de temps. En effet, ce problème est une généralisation du problème de calcul de homomorphisme de graphes, qui est NP-complet.Dans cette thèse, nous adressons le problème de calculer des placements initiaux et de reconfiguration pour des applications distribuées sur potentiellement de multiples Clouds. L'objectif est de minimiser les coûts de location et de migration en satisfaisant des contraintes de ressources et communications. Pour cela, nous proposons des heuristiques performantes capables de calculer des placements de bonne qualité très rapidement pour des scénarios à petite et large échelles. Ces heuristiques, qui sont basées sur des algorithmes de partition de graphes et de vector packing, ont été évaluées en les comparant avec des approches de l'état de l'art comme des solveurs exactes et des méta-heuristiques. Nous montrons en utilisant des simulations que les heuristiques proposées arrivent à calculer des solutions de bonne qualité en quelques secondes tandis que des autres approches prennent des heures ou jours pour les calculerThe Cloud has become a very popular platform for deploying distributed applications. Today, virtually any credit card holder can have access to Cloud services. There are many different ways of offering Cloud services to customers. In this thesis we especially focus on theInfrastructure as a Service (IaaS), a model that, usually, proposes virtualized computing resources to costumers in the form of virtual machines (VMs). Thanks to its attractive pay-as-you-use cost model, it is easier for customers, specially small and medium companies, to outsource hosting infrastructures and benefit of savings related to upfront investments and maintenance costs. Also, customers can have access to features such as scalability, availability, and reliability, which previously were almost exclusive for large companies. To deploy a distributed application, a Cloud customer must first consider the mapping between her application (or its parts) to the target infrastructure. She needs to take into consideration cost, resource, and communication constraints to select the most suitable set of VMs, from private and public Cloud providers. However, defining a mapping manually may be a challenge in large-scale or time constrained scenarios since the number of possible configuration explodes. Furthermore, when automating this process, scalability issues must be taken into account given that this mapping problem is a generalization of the graph homomorphism problem, which is NP-complete.In this thesis we address the problem of calculating initial and reconfiguration placements for distributed applications over possibly multiple Clouds. Our objective is to minimize renting and migration costs while satisfying applications' resource and communication constraints. We concentrate on the mapping between applications and Cloud infrastructure. Using an incremental approach, we split the problem into three different parts and propose efficient heuristics that can compute good quality placements very quickly for small and large scenarios. These heuristics are based on graph partition and vector packing heuristics and have been extensively evaluated against state of the art approaches such as MIP solvers and meta-heuristics. We show through simulations that the proposed heuristics manage to compute solutions in a few seconds that would take many hours or days for other approaches to compute
Book chapters on the topic "Greedy heuristic algorithms"
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Ma, Fuhua, Qianqian Ren, and Jun Li. "A Greedy Heuristic Based Beacons Selection for Localization." In Algorithms and Architectures for Parallel Processing, 710–18. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60245-1_48.
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Dietzfelbinger, Martin, Hendrik Peilke, and Michael Rink. "A More Reliable Greedy Heuristic for Maximum Matchings in Sparse Random Graphs." In Experimental Algorithms, 148–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30850-5_14.
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Taillard, Éric D. "Constructive Methods." In Design of Heuristic Algorithms for Hard Optimization, 85–101. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13714-3_4.
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AbstractThis chapter presents methods for constructing solutions. It starts with the branch and bound methods, widely used for the design of exact algorithms. Then two basic methods are presented, random and greedy constructions. The latter sequentially selects the elements to include to a partial solution, never changing the choices that have been made. This method can be improved by a deeper evaluation of the consequences of a choice. Beam search and the pilot method are part of it.
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Yang, Weiming. "Non-Greedy Heuristic Web Spiders Search Algorithm." In Lecture Notes in Electrical Engineering, 1728–33. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2386-6_236.
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Pranzo, Marco, Carlo Meloni, and Dario Pacciarelli. "A New Class of Greedy Heuristics for Job Shop Scheduling Problems." In Experimental and Efficient Algorithms, 223–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44867-5_19.
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Xu, Thomas Canhao, Pasi Liljeberg, and Hannu Tenhunen. "A Greedy Heuristic Approximation Scheduling Algorithm for 3D Multicore Processors." In Euro-Par 2011: Parallel Processing Workshops, 281–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29737-3_32.
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Barus, A. C., T. Y. Chen, D. Grant, F. C. Kuo, and M. F. Lau. "Testing of Heuristic Methods: A Case Study of Greedy Algorithm." In Software Engineering Techniques, 246–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22386-0_19.
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Gutin, Gregory, Boris Goldengorin, and Jing Huang. "Worst Case Analysis of Max-Regret, Greedy and Other Heuristics for Multidimensional Assignment and Traveling Salesman Problems." In Approximation and Online Algorithms, 214–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/11970125_17.
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Brahmachary, Shuvayan, Ganesh Natarajan, Vinayak Kulkarni, Niranjan Sahoo, and Soumya Ranjan Nanda. "Application of Greedy and Heuristic Algorithm-Based Optimisation Methods Towards Aerodynamic Shape Optimisation." In Advances in Intelligent Systems and Computing, 937–48. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1592-3_75.
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Potvin, Jean-Yves, and François Guertin. "Coupling a Greedy Route Construction Heuristic with a Genetic Algorithm for the Vehicle Routing Problem with Time Windows." In Operations Research/Computer Science Interfaces Series, 423–42. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-4102-8_19.
Full textConference papers on the topic "Greedy heuristic algorithms"
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Guo, Changjie, Zhe Xiang, Zhun Zhong, and Yuzhuo Zhong. "Greedy heuristic placement algorithms in distributed cooperative proxy system." In Electronic Imaging 2002, edited by C. C. Jay Kuo. SPIE, 2002. http://dx.doi.org/10.1117/12.453064.
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Wan, Guihong, and Haim Schweitzer. "Heuristic Search for Approximating One Matrix in Terms of Another Matrix." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/221.
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We study the approximation of a target matrix in terms of several selected columns of another matrix, sometimes called "a dictionary". This approximation problem arises in various domains, such as signal processing, computer vision, and machine learning. An optimal column selection algorithm for the special case where the target matrix has only one column is known since the 1970's, but most previously proposed column selection algorithms for the general case are greedy. We propose the first nontrivial optimal algorithm for the general case, using a heuristic search setting similar to the classical A* algorithm. We also propose practical sub-optimal algorithms in a setting similar to the classical Weighted A* algorithm. Experimental results show that our sub-optimal algorithms compare favorably with the current state-of-the-art greedy algorithms. They also provide bounds on how close their solutions are to the optimal solution.
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Wang, Ziyuan, Baowen Xu, and Changhai Nie. "Greedy Heuristic Algorithms to Generate Variable Strength Combinatorial Test Suite." In Eighth International Conference on Quality Software. QSIC 2008. IEEE, 2008. http://dx.doi.org/10.1109/qsic.2008.52.
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Cohen, Eldan, Richard Valenzano, and Sheila McIlraith. "Type-WA*: Using Exploration in Bounded Suboptimal Planning." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/557.
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Previous work on satisficing planning using greedy best-first search (GBFS) has shown that non-greedy, randomized exploration can help escape uninformative heuristic regions and solve hard problems faster. Despite their success when used with GBFS, such exploration techniques cannot be directly applied to bounded suboptimal algorithms like Weighted A* (WA*) without losing the solution-quality guarantees. In this work, we present Type-WA*, a novel bounded suboptimal planning algorithm that augments WA* with type-based exploration while still satisfying WA*'s theoretical solution-quality guarantee. Our empirical analysis shows that Type-WA* significantly increases the number of solved problems, when used in conjunction with each of three popular heuristics. Our analysis also provides insight into the runtime vs. solution cost trade-off.
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Ruichun Tang, Shuangle Guo, Hongying Ji, and Cunqun Gong. "A Heuristic Optimization algorithm for Geographic Greedy Hole-Bypassing routing algorithms in WMSNs." In Multimedia Technology (IC-BNMT 2010). IEEE, 2010. http://dx.doi.org/10.1109/icbnmt.2010.5705148.
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Joneja, Ajay, Zhu Shaoming, and Yuan-Shin Lee. "Greedy Tool Heuristic for Rough Milling of Complex Pockets." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/dfm-21179.
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Abstract The milling of complex pockets bounded by NURBS surfaces is usually broken into rough and finish milling, with the former taking up the bulk of the machining time. This time can be reduced if the proper combination of end-mills of different sizes are used to machine in different regions. The greedy tool heuristic presented in this paper is a new approach for determination of the machining volume that should be allocated to different tools selected from among a large set of available tools. Subsequent machining planning can then be performed by repeated application of standard 2D milling algorithms. This new approach in multiple-tool rough milling of complex shapes promises to reduce machining time of parts with complex shapes, such as modern moulds and casting patterns.
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Yang, Puyi, and Hamidreza Najafi. "Wind Farm Layout Optimization: A Multi-Stage Approach." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-71892.
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Abstract The Wind Farm Layout Optimization (WFLO) problem is a complex and non-convex optimization problem. Even though a lot of heuristic algorithms and mathematical programming methods have been tested and discussed, there is not a consensus about which algorithm is the most suitable one to solve the WFLO problems. Every algorithm has its own advantages and disadvantages on solving different problems, thus the multi-stage approaches have been picked up. One multi-stage approach applied in solving WFLO problems is to apply an algorithm in stage 1 to capture a coarse, initial optimized layout and import it to stage 2 as an initial condition for another algorithm for further refinement. This paper compared two types of multi-stage methods: The Heuristic-Gradient-based (H-G) model which consists of a heuristic algorithm in stage 1 and a gradient-based algorithm in stage 2; The Discrete-Continuous (D-C) model which consists of a heuristic algorithm in discrete scheme in stage 1 and an algorithm in continuous scheme in stage 2. Annual Energy Production (AEP) is used as the objective function while the computational time associated with each approach is documented. The results illustrate most of the multistage models can improve the optimization procedure both in terms of AEP and computational time. Overall, it is found that the D-C approach is better than the H-G approach. Particularly, the combination of Greedy+Random Search provides the highest AEP and the combination of Greedy and SLSQP provides the lowest computational time.
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Thanh, Pham Dinh, Huynh Thi Thanh Binh, Do Dinh Dac, Nguyen Binh Long, and Le Minh Hai Phong. "A Heuristic Based on Randomized Greedy Algorithms for the Clustered Shortest-Path Tree Problem." In 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019. http://dx.doi.org/10.1109/cec.2019.8790070.
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Heusner, Manuel, Thomas Keller, and Malte Helmert. "Search Progress and Potentially Expanded States in Greedy Best-First Search." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/735.
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A classical result in optimal search shows that A* with an admissible and consistent heuristic expands every state whose f-value is below the optimal solution cost and no state whose f-value is above the optimal solution cost. For satisficing search algorithms, a similarly clear understanding is currently lacking. We examine the search behavior of greedy best-first search (GBFS) in order to make progress towards such an understanding. We introduce the concept of high-water mark benches, which separate the search space into areas that are searched by a GBFS algorithm in sequence. High-water mark benches allow us to exactly determine the set of states that are expanded by at least one GBFS tie-breaking strategy and give us a clearer understanding of search progress.
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Mandros, Panagiotis, Mario Boley, and Jilles Vreeken. "Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/864.
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The reliable fraction of information is an attractive score for quantifying (functional) dependencies in high-dimensional data. In this paper, we systematically explore the algorithmic implications of using this measure for optimization. We show that the problem is NP-hard, justifying worst-case exponential-time as well as heuristic search methods. We then substantially improve the practical performance for both optimization styles by deriving a novel admissible bounding function that has an unbounded potential for additional pruning over the previously proposed one. Finally, we empirically investigate the approximation ratio of the greedy algorithm and show that it produces highly competitive results in a fraction of time needed for complete branch-and-bound style search.