A New Hybrid Approach Based On Discrete Differential Evolution Algorithm To Enhancement Solutions Of Quadratic Assignment Problem
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...
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my.utem.eprints.249652021-02-25T01:37:02Z http://eprints.utem.edu.my/id/eprint/24965/ A New Hybrid Approach Based On Discrete Differential Evolution Algorithm To Enhancement Solutions Of Quadratic Assignment Problem Asaad Shakir, Hameed Mohd Aboobaider, Burhanuddin Mutar, Modhi Lafta Ngo, Hea Choon 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 Growing Science Ltd. 2020 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/24965/2/ASAADSHAKIRHAMEEDMABURHANUDDINANEWHYBRIDAPPROACHBASED.PDF Asaad Shakir, Hameed and Mohd Aboobaider, Burhanuddin and Mutar, Modhi Lafta and Ngo, Hea Choon (2020) A New Hybrid Approach Based On Discrete Differential Evolution Algorithm To Enhancement Solutions Of Quadratic Assignment Problem. International Journal of Industrial Engineering Computations, 11 (1). pp. 51-72. ISSN 1923-2926 http://www.growingscience.com/ijiec/Vol11/IJIEC_2019_19.pdf 10.5267/j.ijiec.2019.6.005 |
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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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Article |
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Asaad Shakir, Hameed Mohd Aboobaider, Burhanuddin Mutar, Modhi Lafta Ngo, Hea Choon |
spellingShingle |
Asaad Shakir, Hameed Mohd Aboobaider, Burhanuddin Mutar, Modhi Lafta Ngo, Hea Choon A New Hybrid Approach Based On Discrete Differential Evolution Algorithm To Enhancement Solutions Of Quadratic Assignment Problem |
author_facet |
Asaad Shakir, Hameed Mohd Aboobaider, Burhanuddin Mutar, Modhi Lafta Ngo, Hea Choon |
author_sort |
Asaad Shakir, Hameed |
title |
A New Hybrid Approach Based On Discrete Differential Evolution Algorithm To Enhancement Solutions Of Quadratic Assignment Problem |
title_short |
A New Hybrid Approach Based On Discrete Differential Evolution Algorithm To Enhancement Solutions Of Quadratic Assignment Problem |
title_full |
A New Hybrid Approach Based On Discrete Differential Evolution Algorithm To Enhancement Solutions Of Quadratic Assignment Problem |
title_fullStr |
A New Hybrid Approach Based On Discrete Differential Evolution Algorithm To Enhancement Solutions Of Quadratic Assignment Problem |
title_full_unstemmed |
A New Hybrid Approach Based On Discrete Differential Evolution Algorithm To Enhancement Solutions Of Quadratic Assignment Problem |
title_sort |
new hybrid approach based on discrete differential evolution algorithm to enhancement solutions of quadratic assignment problem |
publisher |
Growing Science Ltd. |
publishDate |
2020 |
url |
http://eprints.utem.edu.my/id/eprint/24965/2/ASAADSHAKIRHAMEEDMABURHANUDDINANEWHYBRIDAPPROACHBASED.PDF http://eprints.utem.edu.my/id/eprint/24965/ http://www.growingscience.com/ijiec/Vol11/IJIEC_2019_19.pdf |
_version_ |
1692995284782546944 |