A cooperative parallel tabu search algorithm for the quadratic assignment problem

James, Tabitha L.; Rego, César; Glover, Fred

doi:10.1016/j.ejor.2007.06.061

Cited by 106 publications

(72 citation statements)

References 35 publications

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“…For this list of QAPLIB instances, we compared our proposed method with others recent methods such as: Memetic algorithm (BMA) [41], Breakout local search (BLS) [42] and Cooperative parallel tabu search algorithm (CPTS) [43]. The list of instances shown in Table III is a challenge for our algorithm.…”

Section: Resultsmentioning

confidence: 99%

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Hybrid Algorithm for Solving the Quadratic Assignment Problem

Riffi¹,

Sayoti²

2019

IJIMAI

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T HE quadratic assignment problem (QAP) is one of the known classical combinatorial optimization problems, in 1976 Sahni and Gonzalez [1] proved that the QAP belongs to the class of NP-hard problems [1]. It was introduced for the first time by Koopmans and Beckmann in 1957 [2]; its purpose is to assign n facilities to n fixed locations with a given flow matrix of facilities and distance matrix of locations in order to minimize the total assignment cost. This problem is applied in various fields such as hospital layout [3], scheduling parallel production lines [4] and analyzing chemical reactions for organic compounds [5].Many recent hybrid approaches have improved performance in solving QAP such as genetic algorithm hybridized with tabu search method [6], ant colony optimization mixed with local search method [7] and ant colony optimization combined with genetic algorithm and local search method [8]. Recently the hybrid algorithms are much proposed and used by many researchers to find optimal or near optimal solutions for the QAP.In this paper we propose a new competitive approach when compared with other existing methods in the literature. The golden ball algorithm mixed with simulated annealing (GBSA) is considered here as a hybrid metaheuristic to apply in the quadratic assignment problem.This work presents an efficient adaptation of GBSA algorithm to the quadratic assignment problem (QAP). This algorithm is based on the concept of soccer; it guides the search by simulated annealing [9] to escape from the local optima. The suggested technique has never been proposed or tested with QAP. In this research we use some small, medium and large test problems for comparing our approach to other recent methods from literature. Our approach is able to explore effectively the search space; it reaches the known optimal solutions in less time.The rest of this paper is structured as follows: In section I, Introduction. In section II, Quadratic assignment problem formulation. In section III, Methods. In section IV, Results and discussion. In section V, Conclusion. KeywordsCombinatorial Optimization, Golden Ball Algorithm, Simulated Annealing, Quadratic Assignment Problem. AbstractThe Quadratic Assignment Problem (QAP) is a combinatorial optimization problem; it belongs to the class of NP-hard problems. This problem is applied in various fields such as hospital layout, scheduling parallel production lines and analyzing chemical reactions for organic compounds. In this paper we propose an application of Golden Ball algorithm mixed with Simulated Annealing (GBSA) to solve QAP. This algorithm is based on different concepts of football. The simulated annealing search can be blocked in a local optimum due to the unacceptable movements; our proposed strategy guides the simulated annealing search to escape from the local optima and to explore in an efficient way the search space. To validate the proposed approach, numerous simulations were conducted on 64 instances of QAPLIB to compare GBSA with existing algorithms in the literatu...

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Section: Resultsmentioning

confidence: 99%

“…• Metaheuristics such as genetic algorithm [12], [13], [14], tabu search method [15], [16], [17], simulated annealing algorithm [9], ant colony optimization [18] and particle swarm optimization [19].…”

Section: F Ij D π(I) π(J)mentioning

confidence: 99%

Hybrid Algorithm for Solving the Quadratic Assignment Problem

Riffi¹,

Sayoti²

2019

IJIMAI

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“…Tabu search (TS) is a well-known meta-heuristic (Glover, 1989;Glover, 1990) which has proven to be effective for solving a large number of practical optimization problems, including quadratic assignment (James et al, 2009), unconstrained global optimization (Duarte et al, 2011), course timetabling (Lü and Hao, 2010), graph coloring (Wu and Hao, 2012), etc. For CODP, this paper employs a TS procedure as follows.…”

Section: Tabu Search Proceduresmentioning

confidence: 99%

Iterated tabu search for the circular open dimension problem

Huang

2013

European Journal of Operational Research

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This paper mainly investigates the circular open dimension problem (CODP), which consists of packing a set of circles of known radii into a strip of fixed width and unlimited length without overlapping. The objective is to minimize the length of the strip. An iterated tabu search approach, named ITS, is proposed. ITS starts from a randomly generated solution and attempts to gain improvements by a tabu search procedure. After that, if the obtained solution is not feasible, a perturbation operator is subsequently employed to reconstruct the incumbent solution and an acceptance criterion is implemented to determine whether or not accept the perturbed solution. This process is repeated until a feasible solution has been found or the allowed computation time has been elapsed. Computational experiments based on well-known benchmark instances show that ITS produces quite competitive results with respect to the best known results. For 18 representative CODP instances taken from the literature, ITS succeeds in improving 13 best known results within reasonable time. In addition, for another challenging related variant: the problem of packing arbitrary sized circles into a circular container, ITS also succeeds in improving many best known results. Supplementary experiments are also provided to analyze the influence of the perturbation operator, as well as the acceptance criterion.

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“…superstructure representation (Yeomans and Grossman 1999;Linke and Kokossis 2003a), whereas attempts were made to improve slow convergence of deterministic optimisation by simplifying the conceptual content and hence analytics (Raman and Grossmann 1991;Bauer and Stichlmair 1996). Similarly, slow convergence of stochastic searches was addressed by parallelising the execution and hence providing larger number of solutions in shorter time (Talbi et al 1998;Leite and Topping 1999;Wang, ZG et al 2005;James et al 2009;Kokossis, A et al 2011;Kim et al 2012;Cecelja, F et al 2014). Interpretation and assuring the confidence in obtained results, however, still remains a problem.…”

Section: Introductionmentioning

confidence: 99%