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An Adaptive Dual-Population Collaborative Chicken Swarm Optimization Algorithm for High-Dimensional Optimization.

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A new adaptive dual-population collaborative chicken swarm optimization (ADPCCSO) algorithm enhances high-dimensional problem-solving. This meta-heuristic approach improves accuracy and convergence speed, outperforming existing methods.

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Swarm intelligence

Background:

  • High-dimensional optimization problems are prevalent in science and technology.
  • Traditional meta-heuristic algorithms struggle with low accuracy and slow convergence in high dimensions.

Purpose of the Study:

  • To propose an adaptive dual-population collaborative chicken swarm optimization (ADPCCSO) algorithm.
  • To address the limitations of existing meta-heuristic algorithms for high-dimensional problems.

Main Methods:

  • Adaptive dynamic adjustment of parameter G for balanced search.
  • Foraging behavior improvement strategy to enhance solution accuracy.
  • Dual-population collaboration between chicken swarm and artificial fish swarm algorithms to escape local extrema.

Main Results:

  • ADPCCSO demonstrates superior solution accuracy and convergence performance compared to AFSA, ABC, and PSO on 17 benchmark functions.
  • The algorithm was successfully applied to the parameter estimation of the Richards model, validating its practical performance.

Conclusions:

  • The ADPCCSO algorithm offers a novel and effective approach for solving high-dimensional optimization problems.
  • The proposed methods significantly improve search accuracy, convergence speed, and the ability to escape local optima.