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Hybrid Sine Cosine Algorithm for Solving Engineering Optimization Problems

Ivona Brajević Orcid Logo, Predrag S. Stanimirović Orcid Logo, Shuai Li Orcid Logo, Xinwei Cao, Ameer Tamoor Khan Orcid Logo, Lev A. Kazakovtsev Orcid Logo

Mathematics, Volume: 10, Issue: 23, Start page: 4555

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.3390/math10234555

Abstract

Engineering design optimization problems are difficult to solve because the objective function is often complex, with a mix of continuous and discrete design variables and various design constraints. Our research presents a novel hybrid algorithm that integrates the benefits of the sine cosine algor...

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Published in: Mathematics
ISSN: 2227-7390
Published: MDPI AG 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa62184
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spelling 2023-01-11T16:53:17.9798062 v2 62184 2022-12-19 Hybrid Sine Cosine Algorithm for Solving Engineering Optimization Problems 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2022-12-19 MECH Engineering design optimization problems are difficult to solve because the objective function is often complex, with a mix of continuous and discrete design variables and various design constraints. Our research presents a novel hybrid algorithm that integrates the benefits of the sine cosine algorithm (SCA) and artificial bee colony (ABC) to address engineering design optimization problems. The SCA is a recently developed metaheuristic algorithm with many advantages, such as good search ability and reasonable execution time, but it may suffer from premature convergence. The enhanced SCA search equation is proposed to avoid this drawback and reach a preferable balance between exploitation and exploration abilities. In the proposed hybrid method, named HSCA, the SCA with improved search strategy and the ABC algorithm with two distinct search equations are run alternately during working on the same population. The ABC with multiple search equations can provide proper diversity in the population so that both algorithms complement each other to create beneficial cooperation from their merger. Certain feasibility rules are incorporated in the HSCA to steer the search towards feasible areas of the search space. The HSCA is applied to fifteen demanding engineering design problems to investigate its performance. The presented experimental results indicate that the developed method performs better than the basic SCA and ABC. The HSCA accomplishes pretty competitive results compared to other recent state-of-the-art methods. Journal Article Mathematics 10 23 4555 MDPI AG 2227-7390 sine cosine algorithm; artificial bee colony; hybrid algorithm; constrained design optimization 1 12 2022 2022-12-01 10.3390/math10234555 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University This research received no external funding. 2023-01-11T16:53:17.9798062 2022-12-19T08:48:48.0337749 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Ivona Brajević 0000-0002-2999-3187 1 Predrag S. Stanimirović 0000-0003-0655-3741 2 Shuai Li 0000-0001-8316-5289 3 Xinwei Cao 4 Ameer Tamoor Khan 0000-0001-6838-992x 5 Lev A. Kazakovtsev 0000-0002-0667-4001 6 62184__26103__f6be4ea9fed243eebcf0b4fa3730e096.pdf 62184.pdf 2022-12-19T08:52:01.8929394 Output 586082 application/pdf Version of Record true Copyright: © 2022 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng https://creativecommons.org/licenses/by/4.0/
title Hybrid Sine Cosine Algorithm for Solving Engineering Optimization Problems
spellingShingle Hybrid Sine Cosine Algorithm for Solving Engineering Optimization Problems
Shuai Li
title_short Hybrid Sine Cosine Algorithm for Solving Engineering Optimization Problems
title_full Hybrid Sine Cosine Algorithm for Solving Engineering Optimization Problems
title_fullStr Hybrid Sine Cosine Algorithm for Solving Engineering Optimization Problems
title_full_unstemmed Hybrid Sine Cosine Algorithm for Solving Engineering Optimization Problems
title_sort Hybrid Sine Cosine Algorithm for Solving Engineering Optimization Problems
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Ivona Brajević
Predrag S. Stanimirović
Shuai Li
Xinwei Cao
Ameer Tamoor Khan
Lev A. Kazakovtsev
format Journal article
container_title Mathematics
container_volume 10
container_issue 23
container_start_page 4555
publishDate 2022
institution Swansea University
issn 2227-7390
doi_str_mv 10.3390/math10234555
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
active_str 0
description Engineering design optimization problems are difficult to solve because the objective function is often complex, with a mix of continuous and discrete design variables and various design constraints. Our research presents a novel hybrid algorithm that integrates the benefits of the sine cosine algorithm (SCA) and artificial bee colony (ABC) to address engineering design optimization problems. The SCA is a recently developed metaheuristic algorithm with many advantages, such as good search ability and reasonable execution time, but it may suffer from premature convergence. The enhanced SCA search equation is proposed to avoid this drawback and reach a preferable balance between exploitation and exploration abilities. In the proposed hybrid method, named HSCA, the SCA with improved search strategy and the ABC algorithm with two distinct search equations are run alternately during working on the same population. The ABC with multiple search equations can provide proper diversity in the population so that both algorithms complement each other to create beneficial cooperation from their merger. Certain feasibility rules are incorporated in the HSCA to steer the search towards feasible areas of the search space. The HSCA is applied to fifteen demanding engineering design problems to investigate its performance. The presented experimental results indicate that the developed method performs better than the basic SCA and ABC. The HSCA accomplishes pretty competitive results compared to other recent state-of-the-art methods.
published_date 2022-12-01T04:21:36Z
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score 11.012678

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