A Hybrid Metaheuristic for the Quadratic Assignment Problem

“…Tseng and Liang [65] proposed a hybrid approach that combines (ACO), the genetic algorithm (GA) and a Local Search (LS) method. The algorithm is applied for solving the Quadratic Assignment Problem (QAP).…”

Section: Evolutionary Algorithms Assisted By Ant Colony Optimizationmentioning

confidence: 99%

“…For several problems a simple Evolutionary algorithm might be good enough to find the desired solution. As reported in the literature, there are several types of problems where a direct evolutionary algorithm could fail to obtain a convenient (optimal) solution [37,40,61,65]. This clearly paves way to the need for hybridization of evolutionary algorithms with other optimization algorithms, machine learning techniques, heuristics etc.…”

Section: Introductionmentioning

confidence: 98%

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

Groşan

Abraham

2007

Studies in Computational Intelligence

150

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Summary. Evolutionary computation has become an important problem solving methodology among many researchers. The population-based collective learning process, selfadaptation, and robustness are some of the key features of evolutionary algorithms when compared to other global optimization techniques. Even though evolutionary computation has been widely accepted for solving several important practical applications in engineering, business, commerce, etc., yet in practice sometimes they deliver only marginal performance. Inappropriate selection of various parameters, representation, etc. are frequently blamed. There is little reason to expect that one can find a uniformly best algorithm for solving all optimization problems. This is in accordance with the No Free Lunch theorem, which explains that for any algorithm, any elevated performance over one class of problems is exactly paid for in performance over another class. Evolutionary algorithm behavior is determined by the exploitation and exploration relationship kept throughout the run. All these clearly illustrates the need for hybrid evolutionary approaches where the main task is to optimize the performance of the direct evolutionary approach. Recently, hybridization of evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty, and vagueness. In this chapter, first we emphasize the need for hybrid evolutionary algorithms and then we illustrate the various possibilities for hybridization of an evolutionary algorithm and also present some of the generic hybrid evolutionary architectures that has evolved during the last couple of decades. We also provide a review of some of the interesting hybrid frameworks reported in the literature.

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“…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.…”

mentioning

confidence: 99%

“…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.…”

mentioning

confidence: 99%

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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A Hybrid Metaheuristic for the Quadratic Assignment Problem

Cited by 74 publications

References 34 publications

Hybridization of Evolutionary Algorithms

Hybridization of Evolutionary Algorithms

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

Hybrid Algorithm for Solving the Quadratic Assignment Problem

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