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MSWOA: Multi-strategy Whale Optimization Algorithm for Engineering Applications.

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Title: MSWOA: Multi-strategy Whale Optimization Algorithm for Engineering Applications.
Authors: Ronghe Zhou1 ronghezhou@ustl.edu.cn, Yong Zhang2 zy9091@163.com, Xiaodong Sun3 sunle1020@163.com, Haining Liu4 13841262060@163.com, Yingying Cai1 AC18364996132@163.com
Source: Engineering Letters. Aug2024, Vol. 32 Issue 8, p1603-1615. 13p.
Subjects: Metaheuristic algorithms, Humpback whale behavior, Learning strategies, Algorithms
Abstract: The Whale Optimization Algorithm (WOA) is a novel algorithm that was motivated by the prey behavior of humpback whales. WOA has attracted a lot of interest due to its few parameters and easy implementation, but it also has sluggish convergence speed, poor convergence accuracy, and is is easy to get a local optima. In this paper, a multi-strategyWOA called MSWOA is constructed to address these drawbacks. It includes dimensional updating, nonlinear convergence factor, global perturbation factor, firefly perturbation, and vertical and horizontal crossover learning strategy. First, a strategy was developed to update each dimension differently to avoid MSWOA from reaching a local optima. Second, a nonlinear convergence factor is devised to better balance the MSWOA's search ability. Third, a global perturbation factor is considered during the exploration phase, to enrich the whale population. Fourth, a firefly perturbation strategy is employed to increase convergence accuracy. Fifth, a vertical and horizontal strategy is applied to accelerate convergence. Finally, twelve CEC2022 benchmark functions and three engineering cases are adopted to evaluate the performance of MSWOA. The results confirm that MSWOA is superior and competitive. [ABSTRACT FROM AUTHOR]
Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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  Data: MSWOA: Multi-strategy Whale Optimization Algorithm for Engineering Applications.
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  Data: <searchLink fieldCode="AR" term="%22Ronghe+Zhou%22">Ronghe Zhou</searchLink><relatesTo>1</relatesTo><i> ronghezhou@ustl.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yong+Zhang%22">Yong Zhang</searchLink><relatesTo>2</relatesTo><i> zy9091@163.com</i><br /><searchLink fieldCode="AR" term="%22Xiaodong+Sun%22">Xiaodong Sun</searchLink><relatesTo>3</relatesTo><i> sunle1020@163.com</i><br /><searchLink fieldCode="AR" term="%22Haining+Liu%22">Haining Liu</searchLink><relatesTo>4</relatesTo><i> 13841262060@163.com</i><br /><searchLink fieldCode="AR" term="%22Yingying+Cai%22">Yingying Cai</searchLink><relatesTo>1</relatesTo><i> AC18364996132@163.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. Aug2024, Vol. 32 Issue 8, p1603-1615. 13p.
– Name: Subject
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  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Humpback+whale+behavior%22">Humpback whale behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+strategies%22">Learning strategies</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The Whale Optimization Algorithm (WOA) is a novel algorithm that was motivated by the prey behavior of humpback whales. WOA has attracted a lot of interest due to its few parameters and easy implementation, but it also has sluggish convergence speed, poor convergence accuracy, and is is easy to get a local optima. In this paper, a multi-strategyWOA called MSWOA is constructed to address these drawbacks. It includes dimensional updating, nonlinear convergence factor, global perturbation factor, firefly perturbation, and vertical and horizontal crossover learning strategy. First, a strategy was developed to update each dimension differently to avoid MSWOA from reaching a local optima. Second, a nonlinear convergence factor is devised to better balance the MSWOA's search ability. Third, a global perturbation factor is considered during the exploration phase, to enrich the whale population. Fourth, a firefly perturbation strategy is employed to increase convergence accuracy. Fifth, a vertical and horizontal strategy is applied to accelerate convergence. Finally, twelve CEC2022 benchmark functions and three engineering cases are adopted to evaluate the performance of MSWOA. The results confirm that MSWOA is superior and competitive. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 1603
    Subjects:
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Humpback whale behavior
        Type: general
      – SubjectFull: Learning strategies
        Type: general
      – SubjectFull: Algorithms
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      – TitleFull: MSWOA: Multi-strategy Whale Optimization Algorithm for Engineering Applications.
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            NameFull: Ronghe Zhou
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            NameFull: Yong Zhang
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            NameFull: Xiaodong Sun
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            NameFull: Haining Liu
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            NameFull: Yingying Cai
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            – D: 01
              M: 08
              Text: Aug2024
              Type: published
              Y: 2024
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