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In this chapter, most exciting characteristics of Grey Wolf Optimizer (GWO) algorithm has been presented in terms of simplicity, flexibility, scalability, capability to yielding good convergence through the balance of exploration and exploitation, tuning of few parameters, and minimum information requirements for the initial search. The strength of GWO is revealed through analysis of various research articles in this chapter. Additionally, the chapter also summarizes how GWO algorithm can be applied for solving various optimization problems at present as well as in near future.

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A Brief Review of Grey Wolf Optimizer: Variants and Applications

  • 章节

  • 发表日期：

    2025年

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  • Debashish Das,
  • Ali Safaa Sadiq,
  • Seyedali Mirjalili

摘要

In this chapter, most exciting characteristics of Grey Wolf Optimizer (GWO) algorithm has been presented in terms of simplicity, flexibility, scalability, capability to yielding good convergence through the balance of exploration and exploitation, tuning of few parameters, and minimum information requirements for the initial search. The strength of GWO is revealed through analysis of various research articles in this chapter. Additionally, the chapter also summarizes how GWO algorithm can be applied for solving various optimization problems at present as well as in near future.