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Breakout local search for the quadratic assignment problem
- Applied Mathematics and Computation
"... The quadratic assignment problem (QAP) is one of the most studied combinatorial optimization problems with various practical applications. In this paper, we present Breakout Local Search (BLS) for solving QAP. BLS explores the search space by a joint use of local search and adaptive perturbation str ..."
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Cited by 8 (3 self)
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The quadratic assignment problem (QAP) is one of the most studied combinatorial optimization problems with various practical applications. In this paper, we present Breakout Local Search (BLS) for solving QAP. BLS explores the search space by a joint use of local search and adaptive perturbation strategies. Experimental evalua-tions on the set of QAPLIB benchmark instances show that the proposed approach is able to attain current best-known results for all but two instances with an average computing time of less than 4.5 hours. Comparisons are also provided to show the competitiveness of the proposed approach with respect to the best-performing QAP algorithms from the literature.
Comparative performance of tabu search and simulated annealing heuristics for the quadratic assignment problem
- Operations Research Letters
, 2010
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An efficient implementation of the robust tabu search heuristic for sparse quadratic assignment problems
- European Journal of Operational Research
, 2011
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Efficient Decision Makings for Dynamic Weapon-Target Assignment by Virtual Permutation and Tabu Search Heuristics
- IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
, 2010
"... Abstract—The dynamic weapon-target assignment (DWTA) problem is a typical constrained combinatorial optimization prob-lem with the objective of maximizing the total value of surviving assets threatened by hostile targets through all defense stages. A generic asset-based DWTA model is established, es ..."
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Cited by 2 (0 self)
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Abstract—The dynamic weapon-target assignment (DWTA) problem is a typical constrained combinatorial optimization prob-lem with the objective of maximizing the total value of surviving assets threatened by hostile targets through all defense stages. A generic asset-based DWTA model is established, especially for the warfare scenario of force coordination, to formulate this problem. Four categories of constraints, involving capability constraints, strategy constraints, resource constraints (i.e., ammunition con-straints), and engagement feasibility constraints, are taken into account in the DWTA model. The concept of virtual permutation (VP) is proposed to facilitate the generation of feasible decisions. A construction procedure (CP) converts VPs into feasible DWTA decisions. With constraint satisfaction guaranteed by the synergy of VPs and the CP, an elaborate local search (LS) operator, namely move-to-head operator, is constructed to avoid repeatedly gener-ating the same decisions. The operator is integrated into two tabu search (TS) algorithms to solve DWTA problems. Comparative ex-periments involving a random sampling method, an LS method, a hybrid genetic algorithm, a hybrid ant-colony optimization algo-rithm, and our TS algorithms show that the proposed TS heuristics for DWTA outperform their competitors in most test cases and they are competent for high-quality real-time DWTA decision makings. Index Terms—Combinatorial optimization, constraint handling, Dynamic weapon-target assignment (DWTA), metaheuristics, mil-itary decision making, tabu search (TS), virtual permutation (VP). I.
unknown title
, 2014
"... The quadratic assignment problem (QAP) is one of the most studied NP-hard problems with various practical applications. In this work, we propose a powerful population-based memetic algorithm (called BMA) for QAP. BMA in-tegrates an effective local optimization algorithm called Breakout Local Search ..."
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The quadratic assignment problem (QAP) is one of the most studied NP-hard problems with various practical applications. In this work, we propose a powerful population-based memetic algorithm (called BMA) for QAP. BMA in-tegrates an effective local optimization algorithm called Breakout Local Search (BLS) within the evolutionary computing framework which itself is based on a uniform crossover, a fitness-based pool updating strategy and an adaptive mu-tation procedure. Extensive computational studies on the set of 135 well-known benchmark instances from the QAPLIB revealed that the proposed algorithm is able to attain the best-known results for 133 instances and thus competes very favorably with the current most effective QAP approaches. A study of the search landscape and crossover operators is also proposed to shed light on the behavior of the algorithm.
Phuoc Nguyen TRAN Soutenue le 17/09/2010 devant le jury composé de: Pr. André-Luc BEYLOT (ENSEEIHT) Rapporteur
, 2011
"... Modèles de décision pour la sélection d’interface et l’association flux/interface pour les terminaux mobiles multiinterfaces Présentée par ..."
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Modèles de décision pour la sélection d’interface et l’association flux/interface pour les terminaux mobiles multiinterfaces Présentée par
Tabu Search for the Cyclic Bandwidth ProblemI
"... The Cyclic Bandwidth problem (CB) for graphs consists in labeling the vertices of a guest graph G by distinct vertices of a host cycle Cn (both of order n) in such a way that the maximum distance in the cycle between adjacent vertices in G is minimized. To the best of our knowledge, this is the firs ..."
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The Cyclic Bandwidth problem (CB) for graphs consists in labeling the vertices of a guest graph G by distinct vertices of a host cycle Cn (both of order n) in such a way that the maximum distance in the cycle between adjacent vertices in G is minimized. To the best of our knowledge, this is the first research work investigating the use of metaheuristic algorithms for solving this challenging combinatorial optimization problem in the case of general graphs. In this paper a new carefully devised Tabu Search algorithm, called TScb, for finding near-optimal solutions for the CB problem is proposed. Different possibilities for its key components and input parameter values were carefully analyzed and tuned, in order to find the combination of them offering the best quality solutions to the problem at a reasonable computational effort. Extensive experimentation was carried out, using 113 standard benchmark instances, for assessing its performance with respect to a Simulated Annealing (SAcb) implementation. The experimental results show that there exists a statis-tically significant performance amelioration achieved by TScb with respect to SAcb in 90 out of 113 graphs (79.646%). It was also found that our TScb algorithm attains 56 optimal solutions and establishes new better upper bounds for the other 57 instances. Furthermore, these competitive results were obtained expending reasonable computational times. Key words: cyclic bandwidth problem, tabu search, best-known bounds 1.
Multistart Strategy Using Delta Test for Variable Selection
"... Abstract. Proper selection of variables is necessary when dealing with large number of input dimensions in regression problems. In the paper, we investigate the behaviour of landscape that is formed when using Delta test as the optimization criterion. We show that simple and greedy Forward-backward ..."
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Abstract. Proper selection of variables is necessary when dealing with large number of input dimensions in regression problems. In the paper, we investigate the behaviour of landscape that is formed when using Delta test as the optimization criterion. We show that simple and greedy Forward-backward selection procedure with multiple restarts gives optimal results for data sets with large number of samples. An improvement to multistart Forward-backward selection is presented that uses information from previous iterations in the form of long-term memory.
Survey of Metaheuristic Algorithms for Combinatorial Optimization
"... This paper is intended to give a review of metaheuristic and their application to combinatorial optimization problems. This paper comprises a snapshot of the rapid evolution of metaheuristic concepts, their convergence towards a unified framework and the richness of potential application in combinat ..."
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This paper is intended to give a review of metaheuristic and their application to combinatorial optimization problems. This paper comprises a snapshot of the rapid evolution of metaheuristic concepts, their convergence towards a unified framework and the richness of potential application in combinatorial optimization problems. Over the years, combinatorial optimization problems are gaining awareness of the researchers both in scientific as well as industrial world. This paper aims to present a brief survey of different metaheuristic algorithms for solving the combinatorial optimization problems. Basically we have divided the metaheuristic into three broad categories namely trajectory methods, population based methods and hybrid methods. Trajectory methods are those that deal with a single solution. These include simulated annealing, tabu search, variable neighborhood search and greedy randomized adaptive search procedure. Population based methods deal with a set of solutions. These include genetic algorithm, ant colony optimization and particle swarm optimization. Hybrid methods deal with the hybridization of single point search methods and population based methods. These are further categorized into five different types. Finally we conclude the paper by giving some issues which are needed to develop a well performed metaheuristic algorithm.
