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

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Bibliographic Details
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]
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Database:	Engineering Source

Description
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]
ISSN:	1816093X