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A Hybrid SSA and SMA with Mutation Opposition-Based Learning for Constrained Engineering Problems
Based on Salp Swarm Algorithm (SSA) and Slime Mould Algorithm (SMA), a novel hybrid optimization algorithm, named Hybrid Slime Mould Salp Swarm Algorithm (HSMSSA), is proposed to solve constrained engineering problems. SSA can obtain good results in solving some optimization problems. However, it is...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8440113/ https://www.ncbi.nlm.nih.gov/pubmed/34531910 http://dx.doi.org/10.1155/2021/6379469 |
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author | Wang, Shuang Liu, Qingxin Liu, Yuxiang Jia, Heming Abualigah, Laith Zheng, Rong Wu, Di |
author_facet | Wang, Shuang Liu, Qingxin Liu, Yuxiang Jia, Heming Abualigah, Laith Zheng, Rong Wu, Di |
author_sort | Wang, Shuang |
collection | PubMed |
description | Based on Salp Swarm Algorithm (SSA) and Slime Mould Algorithm (SMA), a novel hybrid optimization algorithm, named Hybrid Slime Mould Salp Swarm Algorithm (HSMSSA), is proposed to solve constrained engineering problems. SSA can obtain good results in solving some optimization problems. However, it is easy to suffer from local minima and lower density of population. SMA specializes in global exploration and good robustness, but its convergence rate is too slow to find satisfactory solutions efficiently. Thus, in this paper, considering the characteristics and advantages of both the above optimization algorithms, SMA is integrated into the leader position updating equations of SSA, which can share helpful information so that the proposed algorithm can utilize these two algorithms' advantages to enhance global optimization performance. Furthermore, Levy flight is utilized to enhance the exploration ability. It is worth noting that a novel strategy called mutation opposition-based learning is proposed to enhance the performance of the hybrid optimization algorithm on premature convergence avoidance, balance between exploration and exploitation phases, and finding satisfactory global optimum. To evaluate the efficiency of the proposed algorithm, HSMSSA is applied to 23 different benchmark functions of the unimodal and multimodal types. Additionally, five classical constrained engineering problems are utilized to evaluate the proposed technique's practicable abilities. The simulation results show that the HSMSSA method is more competitive and presents more engineering effectiveness for real-world constrained problems than SMA, SSA, and other comparative algorithms. In the end, we also provide some potential areas for future studies such as feature selection and multilevel threshold image segmentation. |
format | Online Article Text |
id | pubmed-8440113 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-84401132021-09-15 A Hybrid SSA and SMA with Mutation Opposition-Based Learning for Constrained Engineering Problems Wang, Shuang Liu, Qingxin Liu, Yuxiang Jia, Heming Abualigah, Laith Zheng, Rong Wu, Di Comput Intell Neurosci Research Article Based on Salp Swarm Algorithm (SSA) and Slime Mould Algorithm (SMA), a novel hybrid optimization algorithm, named Hybrid Slime Mould Salp Swarm Algorithm (HSMSSA), is proposed to solve constrained engineering problems. SSA can obtain good results in solving some optimization problems. However, it is easy to suffer from local minima and lower density of population. SMA specializes in global exploration and good robustness, but its convergence rate is too slow to find satisfactory solutions efficiently. Thus, in this paper, considering the characteristics and advantages of both the above optimization algorithms, SMA is integrated into the leader position updating equations of SSA, which can share helpful information so that the proposed algorithm can utilize these two algorithms' advantages to enhance global optimization performance. Furthermore, Levy flight is utilized to enhance the exploration ability. It is worth noting that a novel strategy called mutation opposition-based learning is proposed to enhance the performance of the hybrid optimization algorithm on premature convergence avoidance, balance between exploration and exploitation phases, and finding satisfactory global optimum. To evaluate the efficiency of the proposed algorithm, HSMSSA is applied to 23 different benchmark functions of the unimodal and multimodal types. Additionally, five classical constrained engineering problems are utilized to evaluate the proposed technique's practicable abilities. The simulation results show that the HSMSSA method is more competitive and presents more engineering effectiveness for real-world constrained problems than SMA, SSA, and other comparative algorithms. In the end, we also provide some potential areas for future studies such as feature selection and multilevel threshold image segmentation. Hindawi 2021-09-07 /pmc/articles/PMC8440113/ /pubmed/34531910 http://dx.doi.org/10.1155/2021/6379469 Text en Copyright © 2021 Shuang Wang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Wang, Shuang Liu, Qingxin Liu, Yuxiang Jia, Heming Abualigah, Laith Zheng, Rong Wu, Di A Hybrid SSA and SMA with Mutation Opposition-Based Learning for Constrained Engineering Problems |
title | A Hybrid SSA and SMA with Mutation Opposition-Based Learning for Constrained Engineering Problems |
title_full | A Hybrid SSA and SMA with Mutation Opposition-Based Learning for Constrained Engineering Problems |
title_fullStr | A Hybrid SSA and SMA with Mutation Opposition-Based Learning for Constrained Engineering Problems |
title_full_unstemmed | A Hybrid SSA and SMA with Mutation Opposition-Based Learning for Constrained Engineering Problems |
title_short | A Hybrid SSA and SMA with Mutation Opposition-Based Learning for Constrained Engineering Problems |
title_sort | hybrid ssa and sma with mutation opposition-based learning for constrained engineering problems |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8440113/ https://www.ncbi.nlm.nih.gov/pubmed/34531910 http://dx.doi.org/10.1155/2021/6379469 |
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