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An improved chicken swarm optimization (ICSO) algorithm addresses premature convergence in multimodal problems. By integrating bacterial foraging and particle swarm optimization, ICSO enhances search depth and global optimization capabilities.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • Standard chicken swarm optimization (CSO) suffers from premature convergence on multimodal optimization problems.
  • Existing algorithms like bacterial foraging algorithm (BFA) and particle swarm optimization (PSO) have limitations in addressing these issues.
  • There is a need for enhanced swarm intelligence algorithms that improve both search depth and population diversity.

Purpose of the Study:

  • To propose an improved chicken swarm optimization (ICSO) algorithm to overcome the premature convergence problem of the standard CSO.
  • To enhance the depth search and global optimization capabilities of the CSO algorithm.
  • To evaluate the performance of the proposed ICSO algorithm against other established optimization techniques.

Main Methods:

  • The improved chicken swarm optimization (ICSO) algorithm integrates concepts from the bacterial foraging algorithm (BFA) and particle swarm optimization (PSO).
  • Incorporates BFA's replication operation: chicks are replaced by stronger chickens to improve depth search.
  • Introduces BFA's elimination-dispersal operation to maintain population diversity and integrates PSO for improved global optimization.

Main Results:

  • The ICSO algorithm demonstrated superior performance on the CEC2014 benchmark function test suite compared to BFA, artificial fish swarm algorithm (AFSA), genetic algorithm (GA), and PSO.
  • Experimental results show improved optimization accuracy and convergence performance for ICSO.
  • Application to the welded beam design problem confirmed ICSO's advantages over other comparison algorithms.

Conclusions:

  • The proposed ICSO algorithm effectively addresses the premature convergence issue in multimodal optimization problems.
  • ICSO exhibits enhanced optimization accuracy and convergence performance, outperforming several established algorithms.
  • While effective for many problems, ICSO is not suitable for large-scale optimization tasks.