Gotowe bibliografie tematyczne / Combinatorial optimisation problems

Gotowa bibliografia na temat „Combinatorial optimisation problems”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 20 lutego 2023

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Artykuły w czasopismach na temat "Combinatorial optimisation problems"

Walshaw, Chris. "Multilevel Refinement for Combinatorial Optimisation Problems". Annals of Operations Research 131, nr 1-4 (październik 2004): 325–72. http://dx.doi.org/10.1023/b:anor.0000039525.80601.15.

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Burgess, N., i M. A. Moore. "Cost distributions in large combinatorial optimisation problems". Journal of Physics A: Mathematical and General 22, nr 21 (7.11.1989): 4599–609. http://dx.doi.org/10.1088/0305-4470/22/21/022.

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DE FARIAS, I. R., E. L. JOHNSON i G. L. NEMHAUSER. "Branch-and-cut for combinatorial optimization problems without auxiliary binary variables". Knowledge Engineering Review 16, nr 1 (marzec 2001): 25–39. http://dx.doi.org/10.1017/s0269888901000030.

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Many optimisation problems involve combinatorial constraints on continuous variables. An example of a combinatorial constraint is that at most one variable in a group of nonnegative variables may be positive. Traditionally, in the mathematical programming community, such problems have been modeled as mixed-integer programs by introducing auxiliary binary variables and additional constraints. Because the number of variables and constraints becomes larger and the combinatorial structure is not used to advantage, these mixed-integer programming models may not be solved satisfactorily, except for small instances. Traditionally, constraint programming approaches to such problems keep and use the combinatorial structure, but do not use linear programming bounds in the search for an optimal solution. Here we present a branch-and-cut approach that considers the combinatorial constraints without the introduction of binary variables. We review the development of this approach and show how strong constraints can be derived using ideas from polyhedral combinatorics. To illustrate the ideas, we present a production scheduling model that arises in the manufacture of fibre optic cables.

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Turky, Ayad, Nasser R. Sabar, Simon Dunstall i Andy Song. "Hyper-heuristic local search for combinatorial optimisation problems". Knowledge-Based Systems 205 (październik 2020): 106264. http://dx.doi.org/10.1016/j.knosys.2020.106264.

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Demirovi?, Emir, Peter J. Stuckey, Tias Guns, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao i Jeffrey Chan. "Dynamic Programming for Predict+Optimise". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 02 (3.04.2020): 1444–51. http://dx.doi.org/10.1609/aaai.v34i02.5502.

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We study the predict+optimise problem, where machine learning and combinatorial optimisation must interact to achieve a common goal. These problems are important when optimisation needs to be performed on input parameters that are not fully observed but must instead be estimated using machine learning. We provide a novel learning technique for predict+optimise to directly reason about the underlying combinatorial optimisation problem, offering a meaningful integration of machine learning and optimisation. This is done by representing the combinatorial problem as a piecewise linear function parameterised by the coefficients of the learning model and then iteratively performing coordinate descent on the learning coefficients. Our approach is applicable to linear learning functions and any optimisation problem solvable by dynamic programming. We illustrate the effectiveness of our approach on benchmarks from the literature.

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Ceberio, Josu, Borja Calvo, Alexander Mendiburu i Jose A. Lozano. "Multi-Objectivising Combinatorial Optimisation Problems by Means of Elementary Landscape Decompositions". Evolutionary Computation 27, nr 2 (czerwiec 2019): 291–311. http://dx.doi.org/10.1162/evco_a_00219.

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In the last decade, many works in combinatorial optimisation have shown that, due to the advances in multi-objective optimisation, the algorithms from this field could be used for solving single-objective problems as well. In this sense, a number of papers have proposed multi-objectivising single-objective problems in order to use multi-objective algorithms in their optimisation. In this article, we follow up this idea by presenting a methodology for multi-objectivising combinatorial optimisation problems based on elementary landscape decompositions of their objective function. Under this framework, each of the elementary landscapes obtained from the decomposition is considered as an independent objective function to optimise. In order to illustrate this general methodology, we consider four problems from different domains: the quadratic assignment problem and the linear ordering problem (permutation domain), the 0-1 unconstrained quadratic optimisation problem (binary domain), and the frequency assignment problem (integer domain). We implemented two widely known multi-objective algorithms, NSGA-II and SPEA2, and compared their performance with that of a single-objective GA. The experiments conducted on a large benchmark of instances of the four problems show that the multi-objective algorithms clearly outperform the single-objective approaches. Furthermore, a discussion on the results suggests that the multi-objective space generated by this decomposition enhances the exploration ability, thus permitting NSGA-II and SPEA2 to obtain better results in the majority of the tested instances.

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Cabrera-Guerrero, Guillermo, Carolina Lagos, Carolina Castañeda, Franklin Johnson, Fernando Paredes i Enrique Cabrera. "Parameter Tuning for Local-Search-Based Matheuristic Methods". Complexity 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/1702506.

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Algorithms that aim to solve optimisation problems by combining heuristics and mathematical programming have attracted researchers’ attention. These methods, also known as matheuristics, have been shown to perform especially well for large, complex optimisation problems that include both integer and continuous decision variables. One common strategy used by matheuristic methods to solve such optimisation problems is to divide the main optimisation problem into several subproblems. While heuristics are used to seek for promising subproblems, exact methods are used to solve them to optimality. In general, we say that both mixed integer (non)linear programming problems and combinatorial optimisation problems can be addressed using this strategy. Beside the number of parameters researchers need to adjust when using heuristic methods, additional parameters arise when using matheuristic methods. In this paper we focus on one particular parameter, which determines the size of the subproblem. We show how matheuristic performance varies as this parameter is modified. We considered a well-known NP-hard combinatorial optimisation problem, namely, the capacitated facility location problem for our experiments. Based on the obtained results, we discuss the effects of adjusting the size of subproblems that are generated when using matheuristics methods such as the one considered in this paper.

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Wang, Tianshi, Leon Wu, Parth Nobel i Jaijeet Roychowdhury. "Solving combinatorial optimisation problems using oscillator based Ising machines". Natural Computing 20, nr 2 (5.05.2021): 287–306. http://dx.doi.org/10.1007/s11047-021-09845-3.

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Nahas, Nabil, i Mustapha Nourelfath. "Nonlinear threshold accepting meta-heuristic for combinatorial optimisation problems". International Journal of Metaheuristics 3, nr 4 (2014): 265. http://dx.doi.org/10.1504/ijmheur.2014.068904.

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Kamrani, Ali K., i Ricardo Gonzalez. "Genetic algorithms-baes solution approach to combinatorial optimisation problems". International Journal of Knowledge Management Studies 2, nr 4 (2008): 499. http://dx.doi.org/10.1504/ijkms.2008.019754.

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Rozprawy doktorskie na temat "Combinatorial optimisation problems"

Namazi, Majid. "Learning in Combinatorial Constraint Optimisation". Thesis, Griffith University, 2022. http://hdl.handle.net/10072/419082.

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Many real-world problems can be modelled as constraint optimisation problems (COPs). Each COP includes a set of variables with domains of values, constraints on the assignments to the variables, and an objective function, which should be minimised or maximised. In this thesis, we consider only combinatorial COPs, where domains of the variables are discrete. A component is a subproblem of a COP with specific variables, assignable values or constraints. Most practical COPs, including waste collection, mail delivery, supply chain management, and travelling thief problem (TTP), have more than one component. Existing methods for solving COPs, especially multi-component COPs, repeatedly solve the same problem or subproblem but do not take advantage of learning during the search. This research aimed to apply memorising and online or adaptive machine learning models. The memory buffers and the ML models are built, deployed, and updated during the search to improve search efficacy and efficiency in solving COPs, especially multi-component ones. In this research, we have developed a history memorising method to enhance diversity and effectiveness in solving COPs. Also, we have developed three online machine learning-based methods, one coordination learning for improving efficacy and two surrogate models for enhancing the efficiency of TTP solving. Our proposed solver, CoCo, is currently the state-of-the-art solver for solving TTP. History memorising is an online low-level learning method to keep previously visited solutions or their objective values to avoid or escape from local optima during the search. The Late Acceptance Hill Climbing (LAHC) is a history memorising metaheuristic with promising performance on some COP domains. It aims to overcome the main downside of the traditional Hill Climbing (HC) search, which is often quickly trapped in a local optimum due to strictly accepting only non-worsening moves within each iteration. In contrast, LAHC also accepts worsening moves by keeping the objective values of the previously visited solutions in a limited-size circular memory. It compares the fitness values of candidate solutions against the least recent element in the circular memory to decide on accepting or rejecting them. However, we have realised that whenever all values in memory become the same, LAHC behaves like HC and gets stuck in local optima. We propose an improved form of LAHC called Diversified Late Acceptance Search (DLAS) for solving COPs in general, which usually uses much smaller memory, converges much faster than LAHC and escapes local optima much better than LAHC. The proposed DLAS approach outperforms LAHC on benchmark sets of Travelling Salesman Problem (TSP) and Quadratic Assignment Problem (QAP) instances. TTP is an academic proxy for the waste collection and mail delivery real-world optimisation problems composed of TSP and Knapsack Problem (KP). In TTP, a thief makes a cyclic tour through a set of cities while collecting profitable items scattered over the cities into a rented capacitated knapsack. As the weight of the knapsack increases, the thief’s speed decreases; hence the renting cost increases. Solving TTP aims to maximise profit while minimising the renting cost simultaneously, which means maximising the difference between profit and renting cost. Existing TTP solvers typically employ interleaving and solve one component at a time while keeping the solution of the other component unchanged. This form of interleaving essentially means poor coordination in solving TTP. In this thesis, we first show that a simple local search based coordination approach does not work in TTP. Then, to adequately address interdependence between TSP and KP components, we propose a human-designed coordination heuristic that adjusts collection plans during the exploration of cyclic tours. We further propose another human-designed coordination heuristic that explicitly exploits the cyclic tours in selecting items during collection plan exploration. Lastly, we propose an online machine learning-based coordination heuristic that captures the characteristics of the two human-designed coordination heuristics while solving any TTP instance. Our proposed coordination-based approaches help our TTP solver, cooperative coordination (CoCo), significantly outperform existing state-of-the-art TTP solvers on a set of benchmark TTP instances. Our proposed CoCo solver modifies a TTP instance’s underlying TSP and KP solutions in an iterative interleaved fashion. The TSP solution as a cyclic tour is typically changed in a deterministic way using the steepest-ascent Hill-Climbing (HC) search similar to other cooperative solvers. In contrast, changes to the KP solution typically involve a random HC search, effectively resulting in a quasi-meandering exploration of the TTP solution space. Once CoCo reaches a plateau, it restarts the iterative search of the TTP solution space by using a new initial cyclic tour. We have noticed that the final objective value remains almost the same if the same or similar initial cyclic tour is tried several times by CoCo or the other cooperative TTP solvers. Considering this semideterministic nature of the state-of-the-art cooperative TTP solvers, we propose two adaptive and online surrogate models to filter out non-promising initial cyclic tours to improve search efficiency. These surrogate models are automatically built, updated and deployed while solving any TTP instance. The first model is a Support Vector Regression (SVR)-based black-box model, and the second is a K Nearest Neighbour (KNN)-based white-box simulation model. Both models help to filter out non-promising initial cyclic tours while losing a small number of the cyclic tours leading to the best overall solutions. However, the KNN-based white-box simulation model is more accurate and efficient.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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Zverovitch, Alexei E. "Construction heuristics for hard combinatorial optimisation problems". Thesis, Royal Holloway, University of London, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.405253.

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Voudouris, Christos. "Guided local search for combinatorial optimisation problems". Thesis, University of Essex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361019.

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Lee, Lai Soon. "Multicrossover genetic algorithms for combinatorial optimisation problems". Thesis, University of Southampton, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.431202.

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Du, Plessis Andre. "On two combinatorial optimisation problems involving lotteries". Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/4137.

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Thesis (MComm (Logistics))--University of Stellenbosch, 2010.
ENGLISH ABSTRACT: Suppose a lottery draw consists of forming a winning ticket by randomly choosing t m distinct numbers from a universal set Um = f1; : : : ;mg. Each lottery participant forms a set of tickets prior to the draw, each ticket consisting of n m distinct numbers from Um, and is awarded a prize if k minfn; tg or more numbers in at least one of his/her tickets matches those of the winning ticket. A lottery of this form is denoted by the quadruple hm; n; t; ki, and the prize is known as a k-prize. The participant's set of tickets is also known as a playing set. The participant may wish to form a playing set in such a way that the probability of winning a k-prize is at least 0 < 1. Naturally, the participant will want to minimise the cost of forming such a playing set, which means that the cardinality of the playing set should be as small as possible. This combinatorial minimisation problem is known as the incomplete lottery problem and was introduced by Gr undlingh [16], who also formulated a related problem called the resource utilisation problem. In this problem one attempts to select a playing set of pre-speci ed cardinality ` in such a way that the probability of winning a k-prize is maximised. Gr undlingh [16] studied the incomplete lottery problem and the resource utilisation problem in the special case where n = t. In this thesis both problems are considered in the general case where n 6= t. Exact and approximate solution methods are presented and compared to each other in terms of solution quality achieved, execution time and practical feasibility. The rst solution method involves a mathematical programming formulation of both problems. Using this solution method, both problems are solved for small lottery instances. An exhaustive enumeration solution method, which uses the concept of overlapping playing set structures [5, 16], is reviewed and used to solve both combinatorial optimisation problems for the same small lottery instances. The concept of an overlapping playing set structure is further explored and incorporated in an attempt to solve both combinatorial optimisation problems approximately by means of various metaheuristic solution approaches, including a simulated annealing algorithm, a tabu search and a genetic algorithm. The focus of the thesis nally shifts to a di erent problem involving lotteries. An investigation is conducted into the probability, P(N; ), of participants sharing a k-prize if a total of N tickets are purchased by participants of the lottery hm; n; t; ki. Special attention is a orded in this problem to the jackpot prize of the South African national lottery, Lotto, represented by the quadruple h49; 6; 6; 6i and how the value of P(N; ) is a ected by the way that participants select their playing sets.
AFRIKAANSE OPSOMMING: Gestel 'n lotery-trekking bestaan uit die ewekansige seleksie van 'n wenkaartjie bestaande uit t m verskillende getalle uit 'n universele versameling Um = f1; : : : ;mg. Elke lotery-deelnemer vorm 'n versameling kaartjies voor die trekking, wat elk uit n m verskillende getalle in Um bestaan, en wen 'n prys indien k minfn; tg of meer getalle in minstens een van sy/haar kaartjies ooreenstem met di e in die wenkaartjie. 'n Lotery van hierdie vorm word deur die viertal hm; n; t; ki aangedui, en die prys staan as 'n k-prys bekend. 'n Deelnemer se kaartjies staan ook as a spelversameling bekend. 'n Lotery-deelnemer mag poog om sy spelversameling s o te selekteer dat die waarskynlikheid om 'n k-prys te wen, minstens 0 < 1 is. Die deelnemer sal natuurlik die koste wat met so 'n spelversameling gepaard gaan, wil minimeer, wat beteken dat die kardinaliteit van sy spelversameling so klein as moontlik moet wees. Hierdie kombinatoriese minimeringsprobleem staan as die onvolledige lottery-probleem bekend en is vir die eerste keer deur Gr undlingh [16] bestudeer, wat ook die verwante hulpbronbenuttingsprobleem geformuleer het. In laasgenoemde probleem word daar gesoek na 'n spelversameling van vooraf-gespesi seerde kardinaliteit wat die waarskynlikheid om 'n k-prys te wen, maksimeer. Gr undlingh [16] het die onvolledige lottery-probleem en die hulpbronbenuttingsprobleem in die spesiale geval oorweeg waar n = t. In hierdie tesis word beide probleme in die algemeen oorweeg waar n 6= t. Eksakte en heuristiese oplossingstegnieke word vir beide probleme daargestel en met mekaar in terme van oplossingskwaliteit, oplossingstyd en praktiese haalbaarheid vergelyk. Die eerste oplossingstegniek behels 'n wiskundige programmeringsformulering van beide probleme. Die probleme word deur middel van hierdie benadering vir klein loterye opgelos. 'n Uitputtende enumerasietegniek, wat gebruik maak van die konsep van spelversameling oorvleuelingstrukture [5, 16], word daarna in o enskou geneem en beide kombinatoriese optimeringsprobleme word vir dieselfde klein loterye met behulp van hierdie tegniek opgelos. Die konsep van 'n spelversameling oorvleuelingstruktuur word verder ondersoek en in 'n benaderde oplossingstegniek vir beide kombinatoriese optimeringsprobleme ge nkorporeer deur gebruik te maak van verskeie metaheuristiese oplossingsbenaderings, insluitende 'n gesimuleerde afkoelingsalgoritme, 'n tabu-soektog en 'n genetiese algoritme. Die fokus in die tesis verskuif laastens na 'n ander probleem oor loterye. 'n Ondersoek word geloots na die waarskynlikheid, P(N; ), dat lottery-deelnemers 'n k-prys sal deel indien 'n totaal van N kaartjies in die lotery hm; n; t; ki gekoop word. Spesiale aandag word aan hierdie probleem geskenk in die geval van die boerpot-prys in die Suid-Afrikaanse nasionale lotery, Lotto, wat deur die viertal h49; 6; 6; 6i voorgestel word, en hoe die waarde van P(N; ) be nvloed word deur die manier waarop deelnmers hul spelversamelings selekteer.

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Moser, Irene. "Applying external optimisation to dynamic optimisation problems". Swinburne Research Bank, 2008. http://hdl.handle.net/1959.3/22526.

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Thesis (Ph.D) - Swinburne University of Technology, Faculty of Information & Communication Technologies, 2008.
[A thesis submitted in total fulfillment of the requirements of for the degree of Doctor of Philosophy, Faculty of Information and Communication Technologies, Swinburne University of Technology, 2008]. Typescript. Includes bibliographical references p. 193-201.

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Chu, Paul C. H. "A genetic algorithm approach for combinatorial optimisation problems". Thesis, Imperial College London, 1997. http://hdl.handle.net/10044/1/11491.

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Barake, M. A. "PROBE : a meta-heuristic for combinatorial optimisation problems". Thesis, University of East Anglia, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368139.

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White, Bradley Michael. "Experimental Development of Automated Search Techniques for Discrete Combinatorial Optimisation". Thesis, Griffith University, 2009. http://hdl.handle.net/10072/365420.

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A suite of techniques for finding the optimal solutions for a set of discrete combinatorial problems was developed. An experimental approach was used, with a suitable test-bed found in a class of word-puzzles. The crux of such research is that seeking optimal solutions to discrete combinatorial problems requires the use of deterministic algorithms. Attention was focused on the development of new techniques capable of exhausting the search space more efficiently. Although research was restricted to tractable problems, exhaustion of the search space was recognised to be practically infeasible for all but small problem instances. Thus the size and complexity of the problems examined was necessarily restricted. On these grounds the selection of an appropriate test-bed was fundamental to the research. Complex word problems were used because they encompass a wide range of discrete combinatorial problems, but have only a small literature. The specific puzzle examples employed as test-beds had all been used in public competitions with solutions submitted by thousands of humans, with the winning solutions and scores published. This allowed a simple and independent initial benchmark of success. The techniques developed could be judged to be at least partially successful in that they were able to at least equal and in some cases beat the highest recorded scores. The general problem of benchmarking is discussed. It was observed that small changes to the test bed puzzles or to the techniques would often impact dramatically on the results. In an attempt to isolate the reasons for this, a focused view of the search algorithms was adopted. Complex holistic algorithms were broken into smaller sub-algorithmic categories, such as: node selection, domain maintenance, forward tracking, backtracking, branch-and-bound, primary slot selection, variable ordering, value ordering, and constraint ordering. Within each of these categories a range of variations is presented. Techniques for removing inconsistencies prior to search were also experimented with. These consistency pre-processors were found to have a minimal and at times detrimental effect on search times when a good selection of search techniques was used. However, they were found to offer considerable benefits in instances where a poor selection of search techniques was chosen. As such these consistency pre-processors may be viewed as useful in terms of a risk management strategy for solving these problems. Whilst not the primary focus of this research experimentation with stochastic techniques within a deterministic framework was performed. The purpose of which was to gauge the impact of generating good solutions prior to an exhaustive search. A technique developed was observed to frequently improve the time taken to form an optimal solution, and improve the total time taken to exhaust the search space. While the major effort in the research was necessarily spent in developing and testing these algorithms and their implementations, specific attention was paid to the methodological problems inherent in experimental approaches to program development.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Griffith Business School
Griffith Business School
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Buljubasic, Mirsad. "Efficient local search for several combinatorial optimization problems". Thesis, Montpellier, 2015. http://www.theses.fr/2015MONTS010/document.

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Cette thèse porte sur la conception et l'implémentation d'algorithmes approchés pour l'optimisation en variables discrètes. Plus particulièrement, dans cette étude nous nous intéressons à la résolution de trois problèmes combinatoires difficiles : le « Bin-Packing », la « Réaffectation de machines » et la « Gestion des rames sur les sites ferroviaires ». Le premier est un problème d'optimisation classique et bien connu, tandis que les deux autres, issus du monde industriel, ont été proposés respectivement par Google et par la SNCF. Pour chaque problème, nous proposons une approche heuristique basée sur la recherche locale et nous comparons nos résultats avec les meilleurs résultats connus dans la littérature. En outre, en guise d'introduction aux méthodes de recherche locale mise en œuvre dans cette thèse, deux métaheuristiques, GRASP et Recherche Tabou, sont présentées à travers leur application au problème de la couverture minimale
This Ph.D. thesis concerns algorithms for Combinatorial Optimization Problems. In Combinatorial Optimization Problems the set of feasible solutions is discrete or can be reduced to a discrete one, and the goal is to find the best possible solution. Specifically, in this research we consider three different problems in the field of Combinatorial Optimization including One-dimensional Bin Packing (and two similar problems), Machine Reassignment Problem and Rolling Stock Problem. The first one is a classical and well known optimization problem, while the other two are real world and very large scale problems arising in industry and have been recently proposed by Google and French Railways (SNCF) respectively. For each problem we propose a local search based heuristic algorithm and we compare our results with the best known results in the literature. Additionally, as an introduction to local search methods, two metaheuristic approaches, GRASP and Tabu Search are explained through a computational study on Set Covering Problem

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Książki na temat "Combinatorial optimisation problems"

Evripidis, Bampis, Jansen Klaus i Kenyon Claire, red. Efficient approximation and online algorithms: Recent progress on classical combinatorical optimization problems and new applications. New York: Springer, 2006.

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Paschos, Vangelis Th. Paradigms of Combinatorial Optimization: Problems and New Approaches. Wiley & Sons, Incorporated, John, 2014.

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Paradigms of Combinatorial Optimization: Problems and New Approaches. Wiley & Sons, Incorporated, John, 2014.

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Paschos, Vangelis Th. Paradigms of Combinatorial Optimization: Problems and New Approaches. Wiley & Sons, Incorporated, John, 2013.

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Paschos, Vangelis Th. Paradigms of Combinatorial Optimization: Problems and New Approaches. Wiley & Sons, Incorporated, John, 2014.

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Paschos, Vangelis Th. Paradigms of Combinatorial Optimization: Problems and New Approaches. Wiley & Sons, Incorporated, John, 2014.

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Paschos, Vangelis Th. Paradigms of Combinatorial Optimization: Problems and New Approaches, Volume 2. Wiley-Interscience, 2010.

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Paschos, Vangelis Th. Paradigms of Combinatorial Optimization: Problems and New Approaches, Volume 2. Wiley & Sons, Incorporated, John, 2010.

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Afonso, Ferreira, i Pardalos P. M. 1954-, red. Solving combinatorial optimization problems in parallel: Methods and techniques. Berlin: Springer, 1996.

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Oulasvirta, Antti, i Andreas Karrenbauer. Combinatorial Optimization for User Interface Design. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0005.

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Combinatorial optimization offers a rigorous but powerful approach to user interface design problems, defining problems mathematically such that they can be algorithmically solved. Design is defined as algorithmic combination of design decisions to obtain an optimal solution defined by an objective function. There are strong rationale for this method. First, core concepts such as ’design task’, ’design objective’, and ’optimal design’ become explicit and actionable. Second, solutions work well in practice, even for some problems traditionally out of reach of manual solutions. The method can assist in the generation, refinement, and adaptation of design. However, mathematical expression of HCI problems has been challenging and curbed applications. This chapter introduces combinatorial optimisation from user interface design point of view, and addresses two core challenges: 1) mathematical definition of design problems and 2) expression of evaluative knowledge such as design heuristics and predictive models of interaction.

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Części książek na temat "Combinatorial optimisation problems"

Assimi, Hirad, Frank Neumann, Markus Wagner i Xiaodong Li. "Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems". W Evolutionary Computation in Combinatorial Optimization, 111–26. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04148-8_8.

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Drugan, Madalina M., Pedro Isasi i Bernard Manderick. "Schemata Bandits for Binary Encoded Combinatorial Optimisation Problems". W Lecture Notes in Computer Science, 299–310. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13563-2_26.

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Hebrard, Emmanuel, Eoin O’Mahony i Barry O’Sullivan. "Constraint Programming and Combinatorial Optimisation in Numberjack". W Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, 181–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13520-0_22.

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Turky, Ayad, Nasser R. Sabar, Simon Dunstall i Andy Song. "Hyper-heuristic Based Local Search for Combinatorial Optimisation Problems". W AI 2018: Advances in Artificial Intelligence, 312–17. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03991-2_30.

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Soak, Sang-Moon, David Corne i Byung-Ha Ahn. "A Powerful New Encoding for Tree-Based Combinatorial Optimisation Problems". W Lecture Notes in Computer Science, 430–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30217-9_44.

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Wang, Tianshi, i Jaijeet Roychowdhury. "OIM: Oscillator-Based Ising Machines for Solving Combinatorial Optimisation Problems". W Unconventional Computation and Natural Computation, 232–56. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19311-9_19.

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Klose, Andreas, i Andreas Drexl. "Combinatorial Optimisation Problems of the Assignment Type and a Partitioning Approach". W Lecture Notes in Economics and Mathematical Systems, 215–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56183-2_14.

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van Hemert, Jano I., i Christine Solnon. "A Study into Ant Colony Optimisation, Evolutionary Computation and Constraint Programming on Binary Constraint Satisfaction Problems". W Evolutionary Computation in Combinatorial Optimization, 114–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24652-7_12.

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Knödler, K., J. Poland, A. Mitterer i A. Zell. "Genetic Algorithms Solve Combinatorial Optimisation Problems in the Calibration of Combustion Engines". W Optimization in Industry, 45–56. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0675-3_5.

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Teng, Teck-Hou, Hoong Chuin Lau i Aldy Gunawan. "Instance-Specific Selection of AOS Methods for Solving Combinatorial Optimisation Problems via Neural Networks". W Lecture Notes in Computer Science, 98–114. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05348-2_9.

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Streszczenia konferencji na temat "Combinatorial optimisation problems"

Gomez-Meneses, Pedro, Marcus Randall i Andrew Lewis. "A hybrid multi-objective extremal optimisation approach for multi-objective combinatorial optimisation problems". W 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586194.

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Sim, Kevin, i Emma Hart. "An improved immune inspired hyper-heuristic for combinatorial optimisation problems". W GECCO '14: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2576768.2598241.

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Hernando, Leticia, Alexander Mendiburu i Jose A. Lozano. "Characterising the rankings produced by combinatorial optimisation problems and finding their intersections". W GECCO '19: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3321707.3321843.

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Chieza, H. A., M. T. Khumalo, K. Prag i M. Woolway. "On the Computational Performance of IBM Quantum Devices Applied to Combinatorial Optimisation Problems". W 2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI). IEEE, 2020. http://dx.doi.org/10.1109/iscmi51676.2020.9311605.

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Demirovic, Emir, Peter J. Stuckey, James Bailey, Jeffrey Chan, Christopher Leckie, Kotagiri Ramamohanarao i Tias Guns. "Predict+Optimise with Ranking Objectives: Exhaustively Learning Linear Functions". W 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.2019/151.

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We study the predict+optimise problem, where machine learning and combinatorial optimisation must interact to achieve a common goal. These problems are important when optimisation needs to be performed on input parameters that are not fully observed but must instead be estimated using machine learning. Our contributions are two-fold: 1) we provide theoretical insight into the properties and computational complexity of predict+optimise problems in general, and 2) develop a novel framework that, in contrast to related work, guarantees to compute the optimal parameters for a linear learning function given any ranking optimisation problem. We illustrate the applicability of our framework for the particular case of the unit-weighted knapsack predict+optimise problem and evaluate on benchmarks from the literature.

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Hemmi, David. "Stochastic Constraint Programming". W Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/751.

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Combinatorial optimisation problems often contain uncertainty that has to be taken into account to pro- duce realistic solutions. One way of describing the uncertainty is using scenarios, where each sce- nario describes different potential sets of problem parameters based on random distributions or his- torical data. While efficient algorithmic techniques exist for specific problem classes such as linear pro- grams, there are very few approaches that can han- dle general Constraint Programming formulations with uncertainty. The goal of my PhD is to develop generic methods for solving stochastic combina- torial optimisation problems formulated in a Con- straint Programming framework.

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"On the Capacity of Hopfield Neural Networks as EDAs for Solving Combinatorial Optimisation Problems". W International Conference on Evolutionary Computation Theory and Applications. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0004113901520157.

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Minervini, Pasquale, Erik Arakelyan, Daniel Daza i Michael Cochez. "Complex Query Answering with Neural Link Predictors (Extended Abstract)*". W Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/741.

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Neural link predictors are useful for identifying missing edges in large scale Knowledge Graphs. However, it is still not clear how to use these models for answering more complex queries containing logical conjunctions (∧), disjunctions (∨), and existential quantifiers (∃). We propose a framework for efficiently answering complex queries on in- complete Knowledge Graphs. We translate each query into an end-to-end differentiable objective, where the truth value of each atom is computed by a pre-trained neural link predictor. We then analyse two solutions to the optimisation problem, including gradient-based and combinatorial search. In our experiments, the proposed approach produces more accurate results than state-of-the-art methods — black-box models trained on millions of generated queries — without the need for training on a large and diverse set of complex queries. Using orders of magnitude less training data, we obtain relative improvements ranging from 8% up to 40% in Hits@3 across multiple knowledge graphs. We find that it is possible to explain the outcome of our model in terms of the intermediate solutions identified for each of the complex query atoms. All our source code and datasets are available online (https://github.com/uclnlp/cqd).

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