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An Adaptive Shrinking Grid Search Chaotic Wolf Optimization Algorithm Using Standard Deviation Updating Amount.

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This study introduces an improved wolf pack algorithm (ASGS-CWOA) for faster, more stable optimization. The enhanced algorithm demonstrates superior global search accuracy and robustness compared to existing methods.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • Traditional optimization algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) face challenges in convergence speed and global search accuracy.
  • Existing wolf pack algorithms, including Leading Wolf Pack Algorithm (LWPS) and Chaos Wolf Optimization Algorithm (CWOA), have limitations in optimization quality, stability, and convergence speed.

Purpose of the Study:

  • To enhance the optimization quality, stability, and convergence speed of the wolf pack algorithm.
  • To propose a novel algorithm, Adaptive Shrinking Grid Search Chaotic Wolf Optimization Algorithm (ASGS-CWOA), addressing the limitations of existing methods.

Main Methods:

  • Implemented an Adaptive Shrinking Grid Search (ASGS) strategy allowing all wolves to compete as leaders, improving global optimization probability.
  • Incorporated the Opposite-Middle Raid (OMR) method to accelerate convergence rate.
  • Utilized Standard Deviation Updating Amount (SDUA) for population regeneration to increase population biodiversity.

Main Results:

  • ASGS-CWOA exhibited a faster convergence speed compared to GA, PSO, LWPS, and CWOA.
  • The proposed algorithm demonstrated superior global search accuracy under identical conditions.
  • ASGS-CWOA showed high robustness in experimental evaluations.

Conclusions:

  • The ASGS-CWOA algorithm significantly improves upon existing wolf pack optimization techniques.
  • The combination of ASGS, OMR, and SDUA effectively enhances search capability, convergence rate, and population diversity.
  • ASGS-CWOA represents a robust and accurate optimization method suitable for complex problems.