Academic literature on the topic 'Combinatorial optimisation problems'
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Journal articles on the topic "Combinatorial optimisation problems"
Walshaw, Chris. "Multilevel Refinement for Combinatorial Optimisation Problems." Annals of Operations Research 131, no. 1-4 (October 2004): 325–72. http://dx.doi.org/10.1023/b:anor.0000039525.80601.15.
Full textBurgess, N., and M. A. Moore. "Cost distributions in large combinatorial optimisation problems." Journal of Physics A: Mathematical and General 22, no. 21 (November 7, 1989): 4599–609. http://dx.doi.org/10.1088/0305-4470/22/21/022.
Full textDE FARIAS, I. R., E. L. JOHNSON, and G. L. NEMHAUSER. "Branch-and-cut for combinatorial optimization problems without auxiliary binary variables." Knowledge Engineering Review 16, no. 1 (March 2001): 25–39. http://dx.doi.org/10.1017/s0269888901000030.
Full textTurky, Ayad, Nasser R. Sabar, Simon Dunstall, and Andy Song. "Hyper-heuristic local search for combinatorial optimisation problems." Knowledge-Based Systems 205 (October 2020): 106264. http://dx.doi.org/10.1016/j.knosys.2020.106264.
Full textDemirovi?, Emir, Peter J. Stuckey, Tias Guns, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, and Jeffrey Chan. "Dynamic Programming for Predict+Optimise." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (April 3, 2020): 1444–51. http://dx.doi.org/10.1609/aaai.v34i02.5502.
Full textCeberio, Josu, Borja Calvo, Alexander Mendiburu, and Jose A. Lozano. "Multi-Objectivising Combinatorial Optimisation Problems by Means of Elementary Landscape Decompositions." Evolutionary Computation 27, no. 2 (June 2019): 291–311. http://dx.doi.org/10.1162/evco_a_00219.
Full textCabrera-Guerrero, Guillermo, Carolina Lagos, Carolina Castañeda, Franklin Johnson, Fernando Paredes, and Enrique Cabrera. "Parameter Tuning for Local-Search-Based Matheuristic Methods." Complexity 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/1702506.
Full textWang, Tianshi, Leon Wu, Parth Nobel, and Jaijeet Roychowdhury. "Solving combinatorial optimisation problems using oscillator based Ising machines." Natural Computing 20, no. 2 (May 5, 2021): 287–306. http://dx.doi.org/10.1007/s11047-021-09845-3.
Full textNahas, Nabil, and Mustapha Nourelfath. "Nonlinear threshold accepting meta-heuristic for combinatorial optimisation problems." International Journal of Metaheuristics 3, no. 4 (2014): 265. http://dx.doi.org/10.1504/ijmheur.2014.068904.
Full textKamrani, Ali K., and Ricardo Gonzalez. "Genetic algorithms-baes solution approach to combinatorial optimisation problems." International Journal of Knowledge Management Studies 2, no. 4 (2008): 499. http://dx.doi.org/10.1504/ijkms.2008.019754.
Full textDissertations / Theses on the topic "Combinatorial optimisation problems"
Namazi, Majid. "Learning in Combinatorial Constraint Optimisation." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/419082.
Full textThesis (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.
Full textVoudouris, Christos. "Guided local search for combinatorial optimisation problems." Thesis, University of Essex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361019.
Full textLee, 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.
Full textDu, Plessis Andre. "On two combinatorial optimisation problems involving lotteries." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/4137.
Full textENGLISH 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.
Moser, Irene. "Applying external optimisation to dynamic optimisation problems." Swinburne Research Bank, 2008. http://hdl.handle.net/1959.3/22526.
Full text[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.
Chu, Paul C. H. "A genetic algorithm approach for combinatorial optimisation problems." Thesis, Imperial College London, 1997. http://hdl.handle.net/10044/1/11491.
Full textBarake, 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.
Full textWhite, Bradley Michael. "Experimental Development of Automated Search Techniques for Discrete Combinatorial Optimisation." Thesis, Griffith University, 2009. http://hdl.handle.net/10072/365420.
Full textThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Griffith Business School
Griffith Business School
Full Text
Buljubasic, Mirsad. "Efficient local search for several combinatorial optimization problems." Thesis, Montpellier, 2015. http://www.theses.fr/2015MONTS010/document.
Full textThis 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
Books on the topic "Combinatorial optimisation problems"
Evripidis, Bampis, Jansen Klaus, and Kenyon Claire, eds. Efficient approximation and online algorithms: Recent progress on classical combinatorical optimization problems and new applications. New York: Springer, 2006.
Find full textPaschos, Vangelis Th. Paradigms of Combinatorial Optimization: Problems and New Approaches. Wiley & Sons, Incorporated, John, 2014.
Find full textParadigms of Combinatorial Optimization: Problems and New Approaches. Wiley & Sons, Incorporated, John, 2014.
Find full textPaschos, Vangelis Th. Paradigms of Combinatorial Optimization: Problems and New Approaches. Wiley & Sons, Incorporated, John, 2013.
Find full textPaschos, Vangelis Th. Paradigms of Combinatorial Optimization: Problems and New Approaches. Wiley & Sons, Incorporated, John, 2014.
Find full textPaschos, Vangelis Th. Paradigms of Combinatorial Optimization: Problems and New Approaches. Wiley & Sons, Incorporated, John, 2014.
Find full textPaschos, Vangelis Th. Paradigms of Combinatorial Optimization: Problems and New Approaches, Volume 2. Wiley-Interscience, 2010.
Find full textPaschos, Vangelis Th. Paradigms of Combinatorial Optimization: Problems and New Approaches, Volume 2. Wiley & Sons, Incorporated, John, 2010.
Find full textAfonso, Ferreira, and Pardalos P. M. 1954-, eds. Solving combinatorial optimization problems in parallel: Methods and techniques. Berlin: Springer, 1996.
Find full textOulasvirta, Antti, and Andreas Karrenbauer. Combinatorial Optimization for User Interface Design. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0005.
Full textBook chapters on the topic "Combinatorial optimisation problems"
Assimi, Hirad, Frank Neumann, Markus Wagner, and Xiaodong Li. "Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems." In Evolutionary Computation in Combinatorial Optimization, 111–26. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04148-8_8.
Full textDrugan, Madalina M., Pedro Isasi, and Bernard Manderick. "Schemata Bandits for Binary Encoded Combinatorial Optimisation Problems." In Lecture Notes in Computer Science, 299–310. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13563-2_26.
Full textHebrard, Emmanuel, Eoin O’Mahony, and Barry O’Sullivan. "Constraint Programming and Combinatorial Optimisation in Numberjack." In 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.
Full textTurky, Ayad, Nasser R. Sabar, Simon Dunstall, and Andy Song. "Hyper-heuristic Based Local Search for Combinatorial Optimisation Problems." In 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.
Full textSoak, Sang-Moon, David Corne, and Byung-Ha Ahn. "A Powerful New Encoding for Tree-Based Combinatorial Optimisation Problems." In 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.
Full textWang, Tianshi, and Jaijeet Roychowdhury. "OIM: Oscillator-Based Ising Machines for Solving Combinatorial Optimisation Problems." In Unconventional Computation and Natural Computation, 232–56. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19311-9_19.
Full textKlose, Andreas, and Andreas Drexl. "Combinatorial Optimisation Problems of the Assignment Type and a Partitioning Approach." In 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.
Full textvan Hemert, Jano I., and Christine Solnon. "A Study into Ant Colony Optimisation, Evolutionary Computation and Constraint Programming on Binary Constraint Satisfaction Problems." In 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.
Full textKnödler, K., J. Poland, A. Mitterer, and A. Zell. "Genetic Algorithms Solve Combinatorial Optimisation Problems in the Calibration of Combustion Engines." In Optimization in Industry, 45–56. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0675-3_5.
Full textTeng, Teck-Hou, Hoong Chuin Lau, and Aldy Gunawan. "Instance-Specific Selection of AOS Methods for Solving Combinatorial Optimisation Problems via Neural Networks." In Lecture Notes in Computer Science, 98–114. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05348-2_9.
Full textConference papers on the topic "Combinatorial optimisation problems"
Gomez-Meneses, Pedro, Marcus Randall, and Andrew Lewis. "A hybrid multi-objective extremal optimisation approach for multi-objective combinatorial optimisation problems." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586194.
Full textSim, Kevin, and Emma Hart. "An improved immune inspired hyper-heuristic for combinatorial optimisation problems." In GECCO '14: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2576768.2598241.
Full textHernando, Leticia, Alexander Mendiburu, and Jose A. Lozano. "Characterising the rankings produced by combinatorial optimisation problems and finding their intersections." In GECCO '19: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3321707.3321843.
Full textChieza, H. A., M. T. Khumalo, K. Prag, and M. Woolway. "On the Computational Performance of IBM Quantum Devices Applied to Combinatorial Optimisation Problems." In 2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI). IEEE, 2020. http://dx.doi.org/10.1109/iscmi51676.2020.9311605.
Full textDemirovic, Emir, Peter J. Stuckey, James Bailey, Jeffrey Chan, Christopher Leckie, Kotagiri Ramamohanarao, and Tias Guns. "Predict+Optimise with Ranking Objectives: Exhaustively Learning Linear Functions." 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.2019/151.
Full textHemmi, David. "Stochastic Constraint Programming." In 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.
Full text"On the Capacity of Hopfield Neural Networks as EDAs for Solving Combinatorial Optimisation Problems." In International Conference on Evolutionary Computation Theory and Applications. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0004113901520157.
Full textMinervini, Pasquale, Erik Arakelyan, Daniel Daza, and Michael Cochez. "Complex Query Answering with Neural Link Predictors (Extended Abstract)*." In 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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