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An Improved Grey Wolf Optimization Algorithm with Variable Weights.

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This study introduces an improved grey wolf optimization (GWO) algorithm, the variable weights GWO (VW-GWO), to enhance search efficiency and avoid local optima. The VW-GWO algorithm demonstrates superior performance compared to other optimization methods.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • The grey wolf optimization (GWO) algorithm is a metaheuristic inspired by wolf pack social hierarchy.
  • Standard GWO can be prone to getting trapped in local optima, limiting its effectiveness in complex search spaces.
  • Existing optimization algorithms like Ant Lion Optimization (ALO), Particle Swarm Optimization (PSO), and Bat Algorithm (BA) have their own limitations.

Purpose of the Study:

  • To propose an enhanced grey wolf optimization (GWO) algorithm, termed variable weights GWO (VW-GWO).
  • To improve the convergence speed and global search capability of the GWO algorithm.
  • To reduce the likelihood of the algorithm becoming stuck in local optima.

Main Methods:

  • A novel governing equation for the controlling parameter was developed to mitigate local optima entrapment.
  • Variable weights were incorporated into the GWO algorithm to refine its social hierarchy-based search strategy.
  • Simulation experiments were conducted to evaluate the performance of the proposed VW-GWO algorithm.

Main Results:

  • The proposed VW-GWO algorithm significantly outperformed the standard GWO algorithm.
  • VW-GWO showed better performance compared to other metaheuristic algorithms including ALO, PSO, and BA.
  • The effectiveness of the novel VW-GWO algorithm was validated on high-dimensional problems.

Conclusions:

  • The developed VW-GWO algorithm offers an improved approach to optimization problems.
  • The enhancements effectively address the limitations of the standard GWO, particularly in avoiding local optima.
  • VW-GWO shows promise for application in complex and high-dimensional optimization tasks.