Hybridization of modified sine cosine algorithm with tabu search for solving quadratic assignment problem

Sine Cosine Algorithm (SCA) is a population-based metaheuristic method that widely used to solve various optimization problem due to its ability in stabilizing between exploration and exploitation. However, SCA is rarely used to solve discrete optimization problem such as Quadratic Assignment Proble...

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Main Author: Nurdiyana, Jamil
Format: Thesis
Language:eng
eng
Published: 2022
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Online Access:https://etd.uum.edu.my/10448/1/s825969_01.pdf
https://etd.uum.edu.my/10448/2/s825969_02.pdf
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spelling my-uum-etd.104482023-03-30T01:22:36Z Hybridization of modified sine cosine algorithm with tabu search for solving quadratic assignment problem 2022 Nurdiyana, Jamil Abdul Rahman, Syariza Benjamin, Aida Mauziah Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduates School of Arts & Sciences Q Science (General) Sine Cosine Algorithm (SCA) is a population-based metaheuristic method that widely used to solve various optimization problem due to its ability in stabilizing between exploration and exploitation. However, SCA is rarely used to solve discrete optimization problem such as Quadratic Assignment Problem (QAP) due to the nature of its solution which produce continuous values and makes it challenging in solving discrete optimization problem. The SCA is also found to be trapped in local optima since its lacking in memorizing the moves. Besides, local search strategy is required in attaining superior results and it is usually designed based on the problem under study. Hence, this study aims to develop a hybrid modified SCA with Tabu Search (MSCA-TS) model to solve QAP. In QAP, a set of facilities is assigned to a set of locations to form a one-to-one assignment with minimum assignment cost. Firstly, the modified SCA (MSCA) model with cost-based local search strategy is developed. Then, the MSCA is hybridized with TS to prohibit revisiting the previous solutions. Finally, both designated models (MSCA and MSCA-TS) were tested on 60 QAP instances from QAPLIB. A sensitivity analysis is also performed to identify suitable parameter settings for both models. Comparison of results shows that MSCA-TS performs better than MSCA. The percentage of error and standard deviation for MSCA-TS are lower than the MSCA which are 2.4574 and 0.2968 respectively. The computational results also shows that the MSCA-TS is an effective and superior method in solving QAP when compared to the best-known solutions presented in the literature. The developed models may assist decision makers in searching the most suitable assignment for facilities and locations while minimizing cost. 2022 Thesis https://etd.uum.edu.my/10448/ https://etd.uum.edu.my/10448/1/s825969_01.pdf text eng 2025-08-16 staffonly https://etd.uum.edu.my/10448/2/s825969_02.pdf text eng public other masters Universiti Utara Malaysia
institution Universiti Utara Malaysia
collection UUM ETD
language eng
eng
advisor Abdul Rahman, Syariza
Benjamin, Aida Mauziah
topic Q Science (General)
spellingShingle Q Science (General)
Nurdiyana, Jamil
Hybridization of modified sine cosine algorithm with tabu search for solving quadratic assignment problem
description Sine Cosine Algorithm (SCA) is a population-based metaheuristic method that widely used to solve various optimization problem due to its ability in stabilizing between exploration and exploitation. However, SCA is rarely used to solve discrete optimization problem such as Quadratic Assignment Problem (QAP) due to the nature of its solution which produce continuous values and makes it challenging in solving discrete optimization problem. The SCA is also found to be trapped in local optima since its lacking in memorizing the moves. Besides, local search strategy is required in attaining superior results and it is usually designed based on the problem under study. Hence, this study aims to develop a hybrid modified SCA with Tabu Search (MSCA-TS) model to solve QAP. In QAP, a set of facilities is assigned to a set of locations to form a one-to-one assignment with minimum assignment cost. Firstly, the modified SCA (MSCA) model with cost-based local search strategy is developed. Then, the MSCA is hybridized with TS to prohibit revisiting the previous solutions. Finally, both designated models (MSCA and MSCA-TS) were tested on 60 QAP instances from QAPLIB. A sensitivity analysis is also performed to identify suitable parameter settings for both models. Comparison of results shows that MSCA-TS performs better than MSCA. The percentage of error and standard deviation for MSCA-TS are lower than the MSCA which are 2.4574 and 0.2968 respectively. The computational results also shows that the MSCA-TS is an effective and superior method in solving QAP when compared to the best-known solutions presented in the literature. The developed models may assist decision makers in searching the most suitable assignment for facilities and locations while minimizing cost.
format Thesis
qualification_name other
qualification_level Master's degree
author Nurdiyana, Jamil
author_facet Nurdiyana, Jamil
author_sort Nurdiyana, Jamil
title Hybridization of modified sine cosine algorithm with tabu search for solving quadratic assignment problem
title_short Hybridization of modified sine cosine algorithm with tabu search for solving quadratic assignment problem
title_full Hybridization of modified sine cosine algorithm with tabu search for solving quadratic assignment problem
title_fullStr Hybridization of modified sine cosine algorithm with tabu search for solving quadratic assignment problem
title_full_unstemmed Hybridization of modified sine cosine algorithm with tabu search for solving quadratic assignment problem
title_sort hybridization of modified sine cosine algorithm with tabu search for solving quadratic assignment problem
granting_institution Universiti Utara Malaysia
granting_department Awang Had Salleh Graduate School of Arts & Sciences
publishDate 2022
url https://etd.uum.edu.my/10448/1/s825969_01.pdf
https://etd.uum.edu.my/10448/2/s825969_02.pdf
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