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Evolving the Whale Optimization Algorithm: The Development and Analysis of MISWOA.

Chunfang Li1,2, Yuqi Yao1, Mingyi Jiang3

  • 1School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.

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Summary
This summary is machine-generated.

This study introduces the Multi-Swarm Improved Spiral Whale Optimization Algorithm (MISWOA) to enhance global search and convergence speed. MISWOA significantly outperforms the traditional Whale Optimization Algorithm (WOA) and its variants in optimization tasks.

Keywords:
Multi-Swarm OptimizationWhale Optimization Algorithmadaptive spiral indentation strategyglobal search capabilityoptimization algorithm robustness

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Area of Science:

  • Computational Intelligence
  • Swarm Intelligence Algorithms
  • Metaheuristic Optimization

Background:

  • Traditional Whale Optimization Algorithm (WOA) faces limitations in global search capability and convergence velocity.
  • Existing WOA variants often struggle to balance exploration and exploitation effectively.
  • Need for robust optimization algorithms applicable to complex engineering problems.

Purpose of the Study:

  • To introduce an enhanced Whale Optimization Algorithm, the Multi-Swarm Improved Spiral Whale Optimization Algorithm (MISWOA).
  • To address the shortcomings of the traditional WOA regarding global search and convergence speed.
  • To improve the overall efficiency, robustness, and precision of the optimization process.

Main Methods:

  • Integration of an adaptive nonlinear convergence factor.
  • Incorporation of a variable gain compensation mechanism and adaptive weights.
  • Implementation of an advanced spiral convergence strategy and a multi-population mechanism.

Main Results:

  • MISWOA demonstrates significantly enhanced global search capability and convergence velocity.
  • The algorithm shows superior precision and robustness compared to WOA and its variants.
  • Extensive validation through simulation and experimentation confirms MISWOA's effectiveness.

Conclusions:

  • MISWOA represents a substantial improvement over the traditional WOA and its derivatives.
  • The proposed algorithm offers exceptional performance and practical potential for engineering applications.
  • This work provides a systematic framework for advancing swarm intelligence algorithm research.