Whale Optimization Algorithm with Chaos Strategy and Weight Factor
Abstract Whale optimization algorithm (WOA) is a novel optimization algorithm inspired by humpback whale hunting behavior. Due to the defect of unbalanced exploration and exploitation by using control parameter with linear change, WOA has slow convergence and is easy to fall into local optimum. Thus...
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crioppubl:10.1088/1742-6596/1213/3/032004 2024-06-02T08:07:56+00:00 Whale Optimization Algorithm with Chaos Strategy and Weight Factor Li, Yintong Han, Tong Han, Bangjie Zhao, Hui Wei, Zhenglei 2019 http://dx.doi.org/10.1088/1742-6596/1213/3/032004 https://iopscience.iop.org/article/10.1088/1742-6596/1213/3/032004/pdf https://iopscience.iop.org/article/10.1088/1742-6596/1213/3/032004 unknown IOP Publishing http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining Journal of Physics: Conference Series volume 1213, issue 3, page 032004 ISSN 1742-6588 1742-6596 journal-article 2019 crioppubl https://doi.org/10.1088/1742-6596/1213/3/032004 2024-05-07T13:57:08Z Abstract Whale optimization algorithm (WOA) is a novel optimization algorithm inspired by humpback whale hunting behavior. Due to the defect of unbalanced exploration and exploitation by using control parameter with linear change, WOA has slow convergence and is easy to fall into local optimum. Thus, a novel whale optimization algorithm with chaos strategy and weight factor (WOACW) is proposed to improve the convergence speed and accuracy. In this work, the chaos strategy is executed to initialize the population to enhance the diversity of the initial population. The weight factor is introduced to adjust the influence degree of the current optimal solution on the generation of new individuals in order to improve the convergence speed and accuracy. At the same time, the convergence factor is adjusted by cosine function to better balance the relationship between exploration and exploitation. In addition, using greedy strategy to fully retain the dominant individuals from the parents and the generated candidates to generate offspring, improves the convergence speed of the algorithm. To verify the performance of our approach, WOACW is benchmarked on 13 classical benchmark functions, and the statistical results are compared with the original WOA and three other WOA variants, IWOA, WOAWC, and two state-of-the-art algorithms, SSA, GWO. The experimental results and Wilcoxon signed ranks test show that WOACW has a higher convergence speed and precision than compared algorithms, which verifies the effectiveness of WOACW in this work. Article in Journal/Newspaper Humpback Whale IOP Publishing Journal of Physics: Conference Series 1213 3 032004 |
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Abstract Whale optimization algorithm (WOA) is a novel optimization algorithm inspired by humpback whale hunting behavior. Due to the defect of unbalanced exploration and exploitation by using control parameter with linear change, WOA has slow convergence and is easy to fall into local optimum. Thus, a novel whale optimization algorithm with chaos strategy and weight factor (WOACW) is proposed to improve the convergence speed and accuracy. In this work, the chaos strategy is executed to initialize the population to enhance the diversity of the initial population. The weight factor is introduced to adjust the influence degree of the current optimal solution on the generation of new individuals in order to improve the convergence speed and accuracy. At the same time, the convergence factor is adjusted by cosine function to better balance the relationship between exploration and exploitation. In addition, using greedy strategy to fully retain the dominant individuals from the parents and the generated candidates to generate offspring, improves the convergence speed of the algorithm. To verify the performance of our approach, WOACW is benchmarked on 13 classical benchmark functions, and the statistical results are compared with the original WOA and three other WOA variants, IWOA, WOAWC, and two state-of-the-art algorithms, SSA, GWO. The experimental results and Wilcoxon signed ranks test show that WOACW has a higher convergence speed and precision than compared algorithms, which verifies the effectiveness of WOACW in this work. |
format |
Article in Journal/Newspaper |
author |
Li, Yintong Han, Tong Han, Bangjie Zhao, Hui Wei, Zhenglei |
spellingShingle |
Li, Yintong Han, Tong Han, Bangjie Zhao, Hui Wei, Zhenglei Whale Optimization Algorithm with Chaos Strategy and Weight Factor |
author_facet |
Li, Yintong Han, Tong Han, Bangjie Zhao, Hui Wei, Zhenglei |
author_sort |
Li, Yintong |
title |
Whale Optimization Algorithm with Chaos Strategy and Weight Factor |
title_short |
Whale Optimization Algorithm with Chaos Strategy and Weight Factor |
title_full |
Whale Optimization Algorithm with Chaos Strategy and Weight Factor |
title_fullStr |
Whale Optimization Algorithm with Chaos Strategy and Weight Factor |
title_full_unstemmed |
Whale Optimization Algorithm with Chaos Strategy and Weight Factor |
title_sort |
whale optimization algorithm with chaos strategy and weight factor |
publisher |
IOP Publishing |
publishDate |
2019 |
url |
http://dx.doi.org/10.1088/1742-6596/1213/3/032004 https://iopscience.iop.org/article/10.1088/1742-6596/1213/3/032004/pdf https://iopscience.iop.org/article/10.1088/1742-6596/1213/3/032004 |
genre |
Humpback Whale |
genre_facet |
Humpback Whale |
op_source |
Journal of Physics: Conference Series volume 1213, issue 3, page 032004 ISSN 1742-6588 1742-6596 |
op_rights |
http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining |
op_doi |
https://doi.org/10.1088/1742-6596/1213/3/032004 |
container_title |
Journal of Physics: Conference Series |
container_volume |
1213 |
container_issue |
3 |
container_start_page |
032004 |
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1800753086897061888 |