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PECSO: An Improved Chicken Swarm Optimization Algorithm with Performance-Enhanced Strategy and Its Application.

Yufei Zhang1, Limin Wang2, Jianping Zhao1

  • 1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.

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Summary
This summary is machine-generated.

A new Chicken Swarm Optimization Algorithm (CSO) strategy, PECSO, improves convergence speed and accuracy. This enhanced algorithm effectively balances exploration and exploitation, outperforming others in benchmark tests and engineering applications.

Keywords:
chicken swarm optimizationfree grouping mechanismniche technologyoptimization problemsspiral learning strategysynchronous update

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • The standard Chicken Swarm Optimization Algorithm (CSO) suffers from low convergence accuracy, slow speed, and a tendency to fall into local optima.
  • Addressing these limitations is crucial for improving the applicability of CSO in complex optimization tasks.

Purpose of the Study:

  • To propose a performance enhancement strategy for the CSO algorithm (PECSO) to overcome its inherent deficiencies.
  • To improve the diversity, exploration range, and balance between exploration and exploitation in the CSO algorithm.

Main Methods:

  • Implemented a free grouping mechanism to establish a hierarchy, enhancing individual diversity and search space exploration.
  • Introduced niche division with hens as centers, employing synchronous updating and spiral learning for better exploration-exploitation balance.
  • Validated PECSO using the CEC2017 benchmark function and applied it to engineering optimization cases and robot inverse kinematics.

Main Results:

  • PECSO demonstrated faster convergence, higher precision, and stronger stability compared to other algorithms on benchmark functions.
  • The algorithm successfully obtained good solutions for three engineering optimization problems.
  • PECSO showed a competitive effect in solving the inverse kinematics of robots.

Conclusions:

  • The proposed PECSO algorithm effectively enhances CSO performance, offering significant improvements in convergence speed, accuracy, and stability.
  • PECSO shows strong potential for practical applications, including engineering optimization and robot kinematics.