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Relevant bibliographies by topics / Combinatorial Optimization Algorithm

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Academic literature on the topic 'Combinatorial Optimization Algorithm'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 September 2023

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Journal articles on the topic "Combinatorial Optimization Algorithm"

1

Md Yusof, Zulkifli, Zuwairie Ibrahim, Asrul Adam, et al. "Distance Evaluated Simulated Kalman Filter with State Encoding for Combinatorial Optimization Problems." International Journal of Engineering & Technology 7, no. 4.27 (2018): 22. http://dx.doi.org/10.14419/ijet.v7i4.27.22431.

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Simulated Kalman Filter (SKF) is a population-based optimization algorithm which exploits the estimation capability of Kalman filter to search for a solution in a continuous search space. The SKF algorithm only capable to solve numerical optimization problems which involve continuous search space. Some problems, such as routing and scheduling, involve binary or discrete search space. At present, there are three modifications to the original SKF algorithm in solving combinatorial optimization problems. Those modified algorithms are binary SKF (BSKF), angle modulated SKF (AMSKF), and distance ev

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Gao, Yong Chao, Li Mei Liu, Heng Qian, and Ding Wang. "Space Contraction and Partition Algorithm for Combinatorial Optimization." Advanced Materials Research 421 (December 2011): 559–63. http://dx.doi.org/10.4028/www.scientific.net/amr.421.559.

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The scale and complexity of search space are important factors deciding the solving difficulty of an optimization problem. The information of solution space may lead searching to optimal solutions. Based on this, an algorithm for combinatorial optimization is proposed. This algorithm makes use of the good solutions found by intelligent algorithms, contracts the search space and partitions it into one or several optimal regions by backbones of combinatorial optimization solutions. And optimization of small-scale problems is carried out in optimal regions. Statistical analysis is not necessary b

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Li, Jianying, Zhe Zhou, and Liying Wang. "A Multi-task Combinatorial Optimization Model Based on Genetic Algorithm and its Application in College Education Curriculum Planning." International Journal of Emerging Technologies in Learning (iJET) 10, no. 8 (2015): 38. http://dx.doi.org/10.3991/ijet.v10i8.5218.

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Multi-task combinatorial optimization of a complex system is an important aspect of multi-task planning. To address the existing defects and limitations of the existing multi-task combinatorial optimization methods, the paper proposes a multi-task combinatorial model based on genetic algorithm. As a complex multi-task combinatorial optimization, the curriculum planning for higher education applies to itself the multi-task combinatorial model, which is based on genetic algorithm. Having fully considered such factors as teaching resources distribution, students’ intention and teachers’ intention

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Shan, Mi-Yuan, Ren-Long Zhang, and Li-Hong Zhang. "Combinatorial Clustering Algorithm of Quantum-Behaved Particle Swarm Optimization and Cloud Model." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/406047.

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We propose a combinatorial clustering algorithm of cloud model and quantum-behaved particle swarm optimization (COCQPSO) to solve the stochastic problem. The algorithm employs a novel probability model as well as a permutation-based local search method. We are setting the parameters of COCQPSO based on the design of experiment. In the comprehensive computational study, we scrutinize the performance of COCQPSO on a set of widely used benchmark instances. By benchmarking combinatorial clustering algorithm with state-of-the-art algorithms, we can show that its performance compares very favorably.

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Cao, Yuxiao, and Zhen Wang. "Combinatorial Optimization-Based Clustering Algorithm for Wireless Sensor Networks." Mathematical Problems in Engineering 2020 (July 3, 2020): 1–13. http://dx.doi.org/10.1155/2020/6139704.

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As node energy of wireless sensor networks (WSN) is limited and cannot be supplemented after exhaustion, clustering algorithm is frequently taken as an effective method to prolong the lifetime of WSN. However, the existing clustering algorithms have some drawbacks, either consuming excessive energy as a result of exchanging too much controlling information between nodes, or lacking a comprehensive perspective in terms of the balance among several conflicting objectives. In order to overcome these shortcomings, a novel combinatorial optimization-based clustering algorithm (COCA) for WSN is prop

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Amaro, David, Carlo Modica, Matthias Rosenkranz, Mattia Fiorentini, Marcello Benedetti, and Michael Lubasch. "Filtering variational quantum algorithms for combinatorial optimization." Quantum Science and Technology 7, no. 1 (2022): 015021. http://dx.doi.org/10.1088/2058-9565/ac3e54.

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Abstract Current gate-based quantum computers have the potential to provide a computational advantage if algorithms use quantum hardware efficiently. To make combinatorial optimization more efficient, we introduce the filtering variational quantum eigensolver which utilizes filtering operators to achieve faster and more reliable convergence to the optimal solution. Additionally we explore the use of causal cones to reduce the number of qubits required on a quantum computer. Using random weighted MaxCut problems, we numerically analyze our methods and show that they perform better than the orig

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Wu, Husheng, and Renbin Xiao. "Flexible Wolf Pack Algorithm for Dynamic Multidimensional Knapsack Problems." Research 2020 (February 18, 2020): 1–13. http://dx.doi.org/10.34133/2020/1762107.

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Optimization problems especially in a dynamic environment is a hot research area that has attracted notable attention in the past decades. It is clear from the dynamic optimization literatures that most of the efforts have been devoted to continuous dynamic optimization problems although the majority of the real-life problems are combinatorial. Moreover, many algorithms shown to be successful in stationary combinatorial optimization problems commonly have mediocre performance in a dynamic environment. In this study, based on binary wolf pack algorithm (BWPA), combining with flexible population

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Jamieson, Peter, Farnaz Gharibian, Lesley Shannon, and Steve Wilton. "Using data-mining techniques to improve combinatorial optimization algorithms." Journal of Algorithms & Computational Technology 16 (January 2022): 174830262211306. http://dx.doi.org/10.1177/17483026221130680.

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In this work, we show how data-mining can be used to cluster algorithmic generated data and use that data to improve algorithms that solve combinatorial optimization problems for a real-world application—the field-programmable gate array placement problem. Our methodology is a means for other algorithm engineers to improve their own algorithms for specific real-world problems that are hard to improve. In our case, the placement algorithms are difficult to improve, and to find better heuristics we analyze the results of placement solutions to find clustered information which can then be used to

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Zhang, Xu, Pan Guo, Hua Zhang, and Jin Yao. "Hybrid Particle Swarm Optimization Algorithm for Process Planning." Mathematics 8, no. 10 (2020): 1745. http://dx.doi.org/10.3390/math8101745.

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Process planning is a typical combinatorial optimization problem. When the scale of the problem increases, combinatorial explosion occurs, which makes it difficult for traditional precise algorithms to solve the problem. A hybrid particle swarm optimization (HPSO) algorithm is proposed in this paper to solve problems of process planning. A hierarchical coding method including operation layer, machine layer and logic layer is designed in this algorithm. Each layer of coding corresponds to the decision of a sub-problem of process planning. Several genetic operators of the genetic algorithm are d

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Perez-Rodriguez, Ricardo. "An estimation of distribution algorithm for combinatorial optimization problems." International Journal of Industrial Optimization 3, no. 1 (2022): 47–67. http://dx.doi.org/10.12928/ijio.v3i1.5862.

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This paper considers solving more than one combinatorial problem considered some of the most difficult to solve in the combinatorial optimization field, such as the job shop scheduling problem (JSSP), the vehicle routing problem with time windows (VRPTW), and the quay crane scheduling problem (QCSP). A hybrid metaheuristic algorithm that integrates the Mallows model and the Moth-flame algorithm solves these problems. Through an exponential function, the Mallows model emulates the solution space distribution for the problems; meanwhile, the Moth-flame algorithm is in charge of determining how t

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Dissertations / Theses on the topic "Combinatorial Optimization Algorithm"

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D'Souza, Sammy Raymond. "Parallelizing a nondeterministic optimization algorithm." CSUSB ScholarWorks, 2007. https://scholarworks.lib.csusb.edu/etd-project/3084.

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This research explores the idea that for certain optimization problems there is a way to parallelize the algorithm such that the parallel efficiency can exceed one hundred percent. Specifically, a parallel compiler, PC, is used to apply shortcutting techniquest to a metaheuristic Ant Colony Optimization (ACO), to solve the well-known Traveling Salesman Problem (TSP) on a cluster running Message Passing Interface (MPI). The results of both serial and parallel execution are compared using test datasets from the TSPLIB.

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Cheng, Jianqiang. "Stochastic Combinatorial Optimization." Thesis, Paris 11, 2013. http://www.theses.fr/2013PA112261.

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Dans cette thèse, nous étudions trois types de problèmes stochastiques : les problèmes avec contraintes probabilistes, les problèmes distributionnellement robustes et les problèmes avec recours. Les difficultés des problèmes stochastiques sont essentiellement liées aux problèmes de convexité du domaine des solutions, et du calcul de l’espérance mathématique ou des probabilités qui nécessitent le calcul complexe d’intégrales multiples. A cause de ces difficultés majeures, nous avons résolu les problèmes étudiées à l’aide d’approximations efficaces.Nous avons étudié deux types de problèmes stoch

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Cooklis, John T. "CPGA : a two-dimensional order-based algorithm for cell placement /." Online version of thesis, 1991. http://hdl.handle.net/1850/10709.

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Rava, Andrea Basilio. "Quantum approximate optimization algorithm: combinatorial problems and classical statistical models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23113/.

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The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm for solving combinatorial optimization problems. Since most of combinatorial optimization problems may be thought as particular instances of Ising Hamiltonians, the study of the QAOA is very relevant from the physical point of view for its potential applications in describing physical systems. In the QAOA a quantum state is prepared and, through 2p parameterized quantum evolutions, a final state which represents an extreme of cost function and encodes the approximate solution of the problem is obtaine

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Hong, Chyi-Fu. "O(n) planar network shortest path algorithm." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/24841.

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Ozpeynirci, Nail Ozgur. "Approaches For Multiobjective Combinatorial Optimization Problems." Phd thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12609216/index.pdf.

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In this thesis, we consider multiobjective combinatorial optimization problems. We address two main topics. We first address the polynomially solvable cases of the Traveling Salesperson Problem and the Bottleneck Traveling Salesperson Problem. We consider multiobjective versions of these problems with different combinations of objective functions, analyze their computational complexities and develop exact algorithms where possible. We next consider generating extreme supported nondominated points of multiobjective integer programming problems for any number of objective functions. We develop

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Gliesch, Alex Zoch. "A genetic algorithm for fair land allocation." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/174950.

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O objetivo de projetos de reforma agrária é redistribuir terras de grandes latifúndios para terrenos menores, com destino à agricultura familiar. Um dos principais problemas do Instituto Nacional de Colonização e Reforma Agrária (INCRA) é subdividir uma parcela grande de terra em lotes menores que são balanceados com relação a certos atributos. Este problema é difícil por que precisa considerar diversas restrições legais e éticas. As soluções atuais são auxiliadas por computador, mas manuais, demoradas e suscetíveis a erros, tipicamente produzindo lotes retangulares de áreas similares mas que

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Coward, Bob. "Genroute : a genetic algorithm (printed wire board (PWB) router) /." Online version of thesis, 1991. http://hdl.handle.net/1850/10711.

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Soylu, Banu. "An Evolutionary Algorithm For Multiple Criteria Problems." Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608134/index.pdf.

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In this thesis, we develop an evolutionary algorithm for approximating the Pareto frontier of multi-objective continuous and combinatorial optimization problems. The algorithm tries to evolve the population of solutions towards the Pareto frontier and distribute it over the frontier in order to maintain a well-spread representation. The fitness score of each solution is computed with a Tchebycheff distance function and non-dominating sorting approach. Each solution chooses its own favorable weights according to the Tchebycheff distance function. Some seed solutions at initial population and a

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Ma, Tao. "Genetic algorithm-based combinatorial parametric optimization for the calibration of traffic microscopic simulation models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ58769.pdf.

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Books on the topic "Combinatorial Optimization Algorithm"

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Pardalos, P. M. (Panos M.), 1954- and SpringerLink (Online service), eds. Data Correcting Approaches in Combinatorial Optimization. Springer New York, 2012.

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2

Gen, Mitsuo. Network models and optimization: Multiobjective genetic algorithm approach. Springer, 2008.

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Ma, Tao. Genetic algorithm-based combinatorial parametric optimization for the calibration of traffic microscopic simulation models. National Library of Canada, 2001.

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Vangelis, Markakis, Milis Ioannis, Paschos Vangelis Th, and SpringerLink (Online service), eds. Combinatorial Optimization: Second International Symposium, ISCO 2012, Athens, Greece, April 19-21, 2012, Revised Selected Papers. Springer Berlin Heidelberg, 2012.

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service), SpringerLink (Online, ed. Combinatorial Optimization and Applications: 6th International Conference, COCOA 2012, Banff, AB, Canada, August 5-9, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

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Christian, Blum, ed. Hybrid metaheuristics: An emerging approach to optimization. Springer, 2008.

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7

Du, Ding-Zhu. Connected Dominating Set: Theory and Applications. Springer New York, 2013.

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Martin, Middendorf, and SpringerLink (Online service), eds. Evolutionary Computation in Combinatorial Optimization: 12th European Conference, EvoCOP 2012, Málaga, Spain, April 11-13, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

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Middendorf, Martin. Evolutionary Computation in Combinatorial Optimization: 13th European Conference, EvoCOP 2013, Vienna, Austria, April 3-5, 2013. Proceedings. Springer Berlin Heidelberg, 2013.

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Pardalos, P. M. (Panos M.), 1954- and SpringerLink (Online service), eds. Mathematical Aspects of Network Routing Optimization. Springer Science+Business Media, LLC, 2011.

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Book chapters on the topic "Combinatorial Optimization Algorithm"

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Chakraborty, Goutam. "Genetic Algorithm Approaches to Solve Various Steiner Tree Problems." In Combinatorial Optimization. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4613-0255-1_2.

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Desrochers, Martin, Jacques Desrosiers, and Marius Solomon. "A Column Generation Algorithm for the Vehicle Routing Problem with Time Windows." In Combinatorial Optimization. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77489-8_17.

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Sepil, Canan A., and Ayşegül Altaban. "A Polynomially Bounded Dual Simplex Algorithm for Capacitated Minimum Cost Flow Problem." In Combinatorial Optimization. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77489-8_32.

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Nemhauser, G. L., and M. W. P. Savelsbergh. "A Cutting Plane Algorithm for the Single Machine Scheduling Problem with Release Times." In Combinatorial Optimization. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77489-8_4.

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Csirik, J., and E. Máté. "The Probabilistic Behavior of the Generalized HARMONIC Algorithm for the On-Line Multi-Dimensional Bin Packing." In Combinatorial Optimization. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77489-8_29.

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Du, Ding-Zhu, Panos Pardalos, Xiaodong Hu, and Weili Wu. "Greedy Algorithm and Spanning Tree." In Introduction to Combinatorial Optimization. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10596-8_4.

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Tan, Ying. "Discrete Firework Algorithm for Combinatorial Optimization Problem." In Fireworks Algorithm. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46353-6_13.

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Janson, Stefan, Enrique Alba, Bernabé Dorronsoro, and Martin Middendorf. "Hierarchical Cellular Genetic Algorithm." In Evolutionary Computation in Combinatorial Optimization. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11730095_10.

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Kesh, Deepanjan, and Shashank K. Mehta. "A Saturation Algorithm for Homogeneous Binomial Ideals." In Combinatorial Optimization and Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22616-8_28.

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Song, Zeqi, Hongwei Du, Hejiao Huang, and Chuang Liu. "Indoor Localization via Candidate Fingerprints and Genetic Algorithm." In Combinatorial Optimization and Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26626-8_24.

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Conference papers on the topic "Combinatorial Optimization Algorithm"

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Cao, Wensheng, and Bin Chen. "Combinatorial optimization algorithm for workshop scheduling." In 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), edited by Samir Ladaci and Suresh Kaswan. SPIE, 2023. http://dx.doi.org/10.1117/12.2686706.

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Gadallah, M. H., and H. A. ElMaraghy. "A New Algorithm for Combinatorial Optimization." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0059.

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Abstract A new algorithm for combinatorial search optimization is developed. This algorithm is based on orthogonal arrays as planning schemes and search graph techniques as representation schemes. Based on the algorithm, a discrete formulation is given to model two search domains. As an application, the algorithm is used to deal with the problem of least cost tolerance allocation with optimum process selection. Studies are performed to compare between different orthogonal array and column assignment and number of design levels with respect to optimum. The proposed algorithm is capable of deali

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Ding, Hua-fu, Xiao-lu Liu, and Xue Liu. "An improved genetic algorithm for combinatorial optimization." In 2011 IEEE International Conference on Computer Science and Automation Engineering (CSAE). IEEE, 2011. http://dx.doi.org/10.1109/csae.2011.5953170.

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Sima, Ioan, and Bazil Parv. "Protein Folding Simulation Using Combinatorial Whale Optimization Algorithm." In 2019 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2019. http://dx.doi.org/10.1109/synasc49474.2019.00030.

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Rosendo, Matheus, and Aurora Pozo. "A hybrid Particle Swarm Optimization algorithm for combinatorial optimization problems." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586178.

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Bouzidi, Abdelhamid, and Mohammed Essaid Riffi. "Discrete cat swarm optimization algorithm applied to combinatorial optimization problems." In 2014 5th Workshop on Codes, Cryptography and Communication Systems (WCCCS). IEEE, 2014. http://dx.doi.org/10.1109/wcccs.2014.7107914.

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Wang, Jiahai, and Yanlan Zhou. "Hybrid quantum particle swarm optimization algorithm for combinatorial optimization problem." In the 9th annual conference. ACM Press, 2007. http://dx.doi.org/10.1145/1276958.1276999.

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Shuhan Shen and Yuncai Liu. "Probability evolutionary algorithm for functional and combinatorial optimization." In 2008 7th World Congress on Intelligent Control and Automation. IEEE, 2008. http://dx.doi.org/10.1109/wcica.2008.4594592.

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Zukhri, Z., and K. Omar. "Problem Difficulty for Genetic Algorithm in Combinatorial Optimization." In 2007 5th Student Conference on Research and Development. IEEE, 2007. http://dx.doi.org/10.1109/scored.2007.4451368.

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Aylaj, Bouchaib, Mostafa Belkasmi, Hamid Zouaki, and Ahlam Berkani. "Degeneration simulated annealing algorithm for combinatorial optimization problems." In 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2015. http://dx.doi.org/10.1109/isda.2015.7489177.

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Reports on the topic "Combinatorial Optimization Algorithm"

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Shepherd, Bruce, Peter Winkler, and Chandra Chekuri. Fundamentals of Combinatorial Optimization and Algorithm Design. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada423042.

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Plotkin, Serge. Research in Graph Algorithms and Combinatorial Optimization. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada292630.

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Shepherd, F. B. Fundamentals of Combinatorial Optimization and Algorithms Design: December Report. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada429923.

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