Whale Optimization Algorithm: A Critical Survey of Fundamental Principles, Modern Adaptations, Benchmarking Practices, and Applications

Authors

  • Saman M Almufti * Department of Information Technology, Technical College of Informatics-Akre, Akre University for Applied Sciences, Duhok, Iraq.
  • Renas Rajab Asaad Department of Information Technology, Technical College of Informatics-Akre, Akre University for Applied Sciences, Duhok, Iraq.
  • Awaz Ahmed Shaban Department of Information Technology, Technical College of Informatics-Akre, Akre University for Applied Sciences, Duhok, Iraq.
  • Ridwan Boya Marqas Department of Computer Science, College of Science, Knowledge University, Erbil, Iraq. https://orcid.org/0000-0002-1071-3771

https://doi.org/10.48313/maa.v3i1.110

Abstract

The Whale Optimization Algorithm (WOA) is a compact swarm-intelligence optimizer inspired by the bubble-net feeding strategy of humpback whales. Since its introduction, WOA has been adopted in continuous, discrete, constrained, and multi-objective optimization because it offers a simple mathematical structure and a natural alternation between global exploration and local exploitation. Nevertheless, the expansion of WOA research has also revealed persistent limitations, including premature convergence, rapid diversity loss, sensitivity to control schedules, weak theoretical guarantees, and inconsistent empirical comparison. This expanded survey provides a critical and mechanism-oriented synthesis of WOA from its biological inspiration and canonical equations to modern adaptive, chaotic, hybrid, discrete, multi-objective, learning-assisted, and parallel variants. Unlike descriptive summaries that list modifications without explaining their search role, this paper classifies adaptations according to the algorithmic component they modify: initialization, parameter control, position updating, perturbation, representation, archiving, learning-based strategy selection, and computational acceleration. The survey further links these mechanisms to practical domains such as structural design, energy systems, Machine Learning (ML), biomedical image analysis, Wireless Sensor Networks (WSN), cloud/edge computing, cybersecurity, scheduling, and intelligent transportation. A dedicated benchmarking section proposes reproducible evaluation practices, including benchmark diversity, statistical testing, ablation analysis, constraint handling, and transparent reporting. Finally, the paper identifies open challenges in convergence theory, large-scale optimization, dynamic environments, many-objective search, and trustworthy integration with Deep Learning (DL) and surrogate modeling. The resulting discussion is intended to serve as a stronger foundation for researchers who wish to select, compare, or develop WOA-based optimizers for rigorous scientific and engineering use.

Keywords:

Whale optimization algorithm, Swarm intelligence, Metaheuristic optimization, Hybrid optimization, Multi-objective optimization

References

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Published

2026-03-01

Issue

Vol. 3 No. 1 (2026): Metaheuristic Algorithms with Applications (MAA)

Section

Articles

How to Cite

Almufti, S. M., Asaad, R. R. ., Shaban, A. A. ., & Marqas, R. B. . (2026). Whale Optimization Algorithm: A Critical Survey of Fundamental Principles, Modern Adaptations, Benchmarking Practices, and Applications. Metaheuristic Algorithms With Applications, 3(1), 19-50. https://doi.org/10.48313/maa.v3i1.110

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