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The Whale Optimization Algorithm (WOA) is a meta-heuristic algorithm that draws inspiration from the bubble-net hunting tactic employed by humpback whales. Widely embraced in engineering, WOA is renowned for its simplicity, minimal operator requirements, rapid convergence, and effective balance between exploration and exploitation. This research study examines the utilization, adaptations, and combinations of the Whale Optimization Algorithm (WOA) within several engineering disciplines. Conducting a Systematic Literature Review of 80 articles from 2018 to 2022, the study uncovers WOA's prevalence in 5 fields and 17 subfields of engineering. The review identifies strengths, weaknesses, and opportunities, indicating a growing research trend in WOA. With an anticipation of continued exploration, the paper aims to provide insights into WOA-based algorithm development, inspiring both experienced researchers and novices in the field.

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A Systematic Review of Utilization of Whale Optimization Algorithm (WOA) in the Context of Electrical Systems and Machine Learning Domains

  • 会议论文

  • 发表日期：

    2026年

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  • Amandeep Gill,
  • Pradeep Kumar Verma,
  • A. Prabhu,
  • Trapty Agarwal,
  • Vimal Kumar Bisht

摘要

The Whale Optimization Algorithm (WOA) is a meta-heuristic algorithm that draws inspiration from the bubble-net hunting tactic employed by humpback whales. Widely embraced in engineering, WOA is renowned for its simplicity, minimal operator requirements, rapid convergence, and effective balance between exploration and exploitation. This research study examines the utilization, adaptations, and combinations of the Whale Optimization Algorithm (WOA) within several engineering disciplines. Conducting a Systematic Literature Review of 80 articles from 2018 to 2022, the study uncovers WOA's prevalence in 5 fields and 17 subfields of engineering. The review identifies strengths, weaknesses, and opportunities, indicating a growing research trend in WOA. With an anticipation of continued exploration, the paper aims to provide insights into WOA-based algorithm development, inspiring both experienced researchers and novices in the field.