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<p>The white shark optimizer (WSO), a novel meta-heuristic algorithm proposed in recent years, is inspired by the predatory behaviors of great white sharks. While WSO has demonstrated superior performance over other algorithms in numerous optimization problems, the no free lunch theorem asserts that no single swarm intelligence optimization algorithm can universally address all optimization challenges, as each method inherently possesses limitations. Specifically, WSO requires excessive iterations to converge to optimal solutions for complex problems and exhibits susceptibility to local optima. To address these deficiencies, this study proposes an enhanced white shark optimization algorithm (EWSO) incorporating multi-strategy enhancements across distinct evolutionary phases. First, an ICMIC chaotic mapping strategy is employed for population initialization to enhance diversity and mitigate premature convergence. Second, a Cauchy noise escape mechanism is introduced during the iteration phase to strengthen local optima avoidance capabilities. Finally, the Levy flight strategy is integrated into the velocity update phase to amplify global exploration potential. Comprehensive evaluations using CEC-2022 benchmark functions and practical engineering optimization problems demonstrate EWSO’s efficacy through comparisons with classical and state-of-the-art algorithms. Experimental results confirm that EWSO achieves significant improvements in convergence speed, solution accuracy, and global search performance.</p>

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Hybrid strategy collaborative enhancement of white shark optimization algorithm

  • 期刊论文

  • 发表日期：

    2025年3月26日

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  • Junchang Liu,
  • Yu Liu,
  • Yahao Yang,
  • Zhenlong Zhao

摘要

The white shark optimizer (WSO), a novel meta-heuristic algorithm proposed in recent years, is inspired by the predatory behaviors of great white sharks. While WSO has demonstrated superior performance over other algorithms in numerous optimization problems, the no free lunch theorem asserts that no single swarm intelligence optimization algorithm can universally address all optimization challenges, as each method inherently possesses limitations. Specifically, WSO requires excessive iterations to converge to optimal solutions for complex problems and exhibits susceptibility to local optima. To address these deficiencies, this study proposes an enhanced white shark optimization algorithm (EWSO) incorporating multi-strategy enhancements across distinct evolutionary phases. First, an ICMIC chaotic mapping strategy is employed for population initialization to enhance diversity and mitigate premature convergence. Second, a Cauchy noise escape mechanism is introduced during the iteration phase to strengthen local optima avoidance capabilities. Finally, the Levy flight strategy is integrated into the velocity update phase to amplify global exploration potential. Comprehensive evaluations using CEC-2022 benchmark functions and practical engineering optimization problems demonstrate EWSO’s efficacy through comparisons with classical and state-of-the-art algorithms. Experimental results confirm that EWSO achieves significant improvements in convergence speed, solution accuracy, and global search performance.