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An improved Wolf pack algorithm for optimization problems: Design and evaluation.

Xuan Chen1, Feng Cheng2, Cong Liu3

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Summary
This summary is machine-generated.

This study introduces OGL-WPA, an enhanced Wolf Pack Algorithm (WPA) using opposition-based learning and genetic algorithms. The improved method enhances optimization performance by addressing convergence speed and local optimum issues.

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Area of Science:

  • Artificial Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • Wolf Pack Algorithm (WPA) is a robust swarm intelligence method for engineering optimization.
  • WPA suffers from slow convergence and susceptibility to local optima.
  • Existing metaheuristic algorithms require improvements for complex optimization tasks.

Purpose of the Study:

  • To enhance the Wolf Pack Algorithm (WPA) by integrating opposition-based learning and genetic algorithms with Levy's flight.
  • To improve convergence speed and global search capability.
  • To mitigate the tendency of falling into local optima in complex optimization problems.

Main Methods:

  • Introduced Opposition-based learning for initializing the wolf population to ensure diversity.
  • Employed a genetic algorithm for leader wolf selection to prevent local optima.
  • Optimized the round-up behavior using Levy's flight for balanced exploration and development.

Main Results:

  • The proposed OGL-WPA demonstrated superior global and local search capabilities compared to other nature-inspired algorithms.
  • Effectiveness was particularly noted on multi-peak and high-dimensional test functions.
  • The algorithm showed improved convergence speed and robustness.

Conclusions:

  • OGL-WPA effectively addresses the limitations of the standard Wolf Pack Algorithm.
  • The integration of opposition-based learning, genetic algorithms, and Levy's flight significantly enhances optimization performance.
  • The proposed algorithm is a promising tool for complex engineering optimization problems.