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The novel Cauchy-Gaussian Grey Wolf Optimization (CG-GWO) algorithm enhances optimization by improving convergence speed and global search capabilities. This CG-GWO method effectively addresses local optima issues in complex optimization projects.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • The Grey Wolf Optimization (GWO) algorithm is recognized for its convergence and ease of use.
  • However, GWO suffers from slow convergence and a tendency to get stuck in local optima.
  • Addressing these limitations is crucial for practical optimization applications.

Purpose of the Study:

  • To introduce a novel Grey Wolf Optimization algorithm, CG-GWO, designed to overcome the limitations of the traditional GWO.
  • To enhance both the global search capability and convergence speed of the GWO algorithm.
  • To validate the effectiveness of the proposed CG-GWO algorithm on a suite of benchmark functions.

Main Methods:

  • Incorporation of a Cauchy-Gaussian mutation operator to boost population diversity and global search.
  • Implementation of a greedy selection mechanism to preserve elite individuals and accelerate convergence.
  • Development of an improved search strategy to expand the search space and refine convergence accuracy.

Main Results:

  • The CG-GWO algorithm demonstrated superior convergence accuracy, speed, and global search ability compared to PSO, WOA, SSA, and FFA.
  • CG-GWO outperformed several improved GWO variants, including IGWO, mGWO, and GLF-GWO.
  • Experimental validation across 16 benchmark functions confirmed the algorithm's effectiveness.

Conclusions:

  • The proposed CG-GWO algorithm effectively addresses the slow convergence and local optima issues of the standard GWO.
  • CG-GWO offers enhanced performance in terms of convergence accuracy, speed, and global search capabilities.
  • This enhanced algorithm presents a promising alternative for complex optimization tasks.