Comparison of Two Diversification Methods to Solve the Quadratic Assignment Problem

Abdelkafi, Omar; Idoumghar, Lhassane; Lepagnot, Julien

doi:10.1016/j.procs.2015.05.392

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…The QAP has several real-life applications, which makes it an interesting area of study for researchers since its inception (Czapiński, 2013;Abdelkafi et al, 2015;Çela et al, 2017). The QAP mathematical model has been presented as follows:…”

Section: Quadratic Assignment Problem Qapmentioning

confidence: 99%

A new hybrid approach based on discrete differential evolution algorithm to enhancement solutions of quadratic assignment problem

Hameed¹,

Aboobaider²,

Mutar³

et al. 2020

10.5267/j.ijiec

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The Combinatorial Optimization Problem (COPs) is one of the branches of applied mathematics and computer sciences, which is accompanied by many problems such as Facility Layout Problem (FLP), Vehicle Routing Problem (VRP), etc. Even though the use of several mathematical formulations is employed for FLP, Quadratic Assignment Problem (QAP) is one of the most commonly used. One of the major problems of Combinatorial NP-hard Optimization Problem is QAP mathematical model. Consequently, many approaches have been introduced to solve this problem, and these approaches are classified as Approximate and Exact methods. With QAP, each facility is allocated to just one location, thereby reducing cost in terms of aggregate distances weighted by flow values. The primary aim of this study is to propose a hybrid approach which combines Discrete Differential Evolution (DDE) algorithm and Tabu Search (TS) algorithm to enhance solutions of QAP model, to reduce the distances between the locations by finding the best distribution of N facilities to N locations, and to implement hybrid approach based on discrete differential evolution (HDDETS) on many instances of QAP from the benchmark. The performance of the proposed approach has been tested on several sets of instances from the data set of QAP and the results obtained have shown the effective performance of the proposed algorithm in improving several solutions of QAP in reasonable time. Afterwards, the proposed approach is compared with other recent methods in the literature review. Based on the computation results, the proposed hybrid approach outperforms the other methods.

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Section: Quadratic Assignment Problem Qapmentioning

confidence: 99%

A new hybrid approach based on discrete differential evolution algorithm to enhancement solutions of quadratic assignment problem

Hameed¹,

Aboobaider²,

Mutar³

et al. 2020

10.5267/j.ijiec

View full text Add to dashboard Cite

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“…Finally, the global best solution obtained with the first two steps is used as a starting solution for the Ro-Ts. In [14], a cooperative variant of Iterative Tabu Search (ITS) is proposed. Each process performs an ITS in which a Ro-Ts is executed at each generation.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Tabu Search for the Large-scale Quadratic Assignment Problem

Abdelkafi¹,

Derbel²,

Liefooghe³

2019

2019 IEEE Congress on Evolutionary Computation (CEC)

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Parallelization is an important paradigm for solving massive optimization problems. Understanding how to fully benefit form the aggregated computing power and what makes a parallel strategy successful is a difficult issue. In this study, we propose a simple parallel iterative tabu search (PITS) and study its effectiveness with respect to different experimental settings. Using the quadratic assignment problem (QAP) as a case study, we first consider different small-and medium-size instances from the literature and then tackle a large-size instance that was rarely considered due the its inherent solving difficulty. In particular, we show that a balance between the number of function evaluations each parallel process is allowed to perform before resuming the search is a critical issue to obtain an improved quality.

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