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In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
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RWOA: A novel enhanced whale optimization algorithm with multi-strategy for numerical optimization and engineering

Junhao Wei1, Yanzhao Gu1, Baili Lu2

  • 1Faculty of Applied Sciences, Macao Polytechnic University, Macao, China.

Plos One
|April 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces the enhanced Whale Optimization Algorithm (RWOA) to overcome limitations of the original Whale Optimization Algorithm (WOA). RWOA demonstrates superior performance in optimization tasks and engineering applications.

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Area of Science:

  • Computational Intelligence
  • Metaheuristic Optimization
  • Swarm Intelligence

Background:

  • The Whale Optimization Algorithm (WOA) is a popular metaheuristic algorithm inspired by whale behavior.
  • Canonical WOA faces challenges including slow convergence, low accuracy, reduced diversity, and exploration-exploitation imbalance.

Purpose of the Study:

  • To enhance the Whale Optimization Algorithm (WOA) to address its inherent limitations.
  • To improve convergence speed, accuracy, population diversity, and the balance between exploration and exploitation.

Main Methods:

  • Proposed the enhanced Whale Optimization Algorithm (RWOA) incorporating six novel strategies.
  • Utilized Good Nodes Set, Hybrid Collaborative Exploration, Spiral Encircling Prey, and Levy flight-integrated Spiral Updating.
  • Implemented Enhanced Cauchy Mutation and redesigned parameter 'a' update for better exploration-exploitation balance.

Main Results:

  • RWOA demonstrated significant improvements over the standard WOA across 23 benchmark functions.
  • Quantitative analysis and comparison with state-of-the-art algorithms confirmed RWOA's superior performance.
  • The impact of each of the six enhancement strategies was individually analyzed.

Conclusions:

  • The enhanced Whale Optimization Algorithm (RWOA) effectively overcomes the shortcomings of the original WOA.
  • RWOA shows strong potential for solving complex real-world engineering design optimization problems.
  • The proposed enhancements lead to improved convergence, accuracy, and diversity in metaheuristic optimization.