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This is a Quantitative Study study.

October 2, 2025

Enhanced Grey Wolf Optimizer With Adaptive Control And Chaotic Initialization For Global Optimization

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Authors

MAMichael Oluwaseun AyansijiDelta State University NONewton I. OkposoDelta Air Lines (United States)JAJoshua Sarduana ApanapudorDelta Air Lines (United States)

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Implication

This approach demonstrates improved accuracy and faster convergence in global optimization, highlighting the roles of chaotic initialization and adaptive strategies.

Key Points

The enhanced grey wolf optimizer achieves up to a 30% faster convergence compared to standard GWO, indicating improved performance.
Statistical tests confirm the enhanced optimizer's consistent superiority in robustness and solution accuracy over benchmark functions.
Chaotic initialization promotes diversity in early stages, while adaptive parameter control balances exploration and exploitation effectively.
Evaluations were conducted across fifteen benchmark functions in a complex 30-dimensional search space, demonstrating practical applicability.

Cite This Study

Ayansiji et al. (2025) studied this question.

synapsesocial.com/papers/68de84bf5b556a9128e1bb40 https://doi.org/10.63561/jmns.v2i3.861

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Implication

This approach demonstrates improved accuracy and faster convergence in global optimization, highlighting the roles of chaotic initialization and adaptive strategies.

Enhanced Grey Wolf Optimizer With Adaptive Control And Chaotic Initialization For Global Optimization

Discussion

Member takes

Implication

Key Points

Cite This Study

Enhanced Grey Wolf Optimizer With Adaptive Control And Chaotic Initialization For Global Optimization

Discussion

Member takes

Implication

Key Points

Cite This Study

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