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Global-best brain storm optimization algorithm based on chaotic difference step and opposition-based learning.

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This study introduces a new optimization algorithm, the chaotic opposition-based global-best brain storm optimization (COGBSO), to address slow convergence and local optima in traditional BSO. COGBSO enhances search space and population diversity for superior complex problem-solving.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • Traditional Brain Storm Optimization (BSO) suffers from slow convergence and a tendency to get trapped in local optima.
  • Existing global-best strategies and discussion mechanisms have limitations in addressing these core BSO issues.
  • The need for improved optimization techniques is critical for solving complex computational problems.

Purpose of the Study:

  • To develop an enhanced Brain Storm Optimization algorithm that overcomes the limitations of traditional BSO.
  • To improve convergence speed and optimization accuracy in complex problem-solving.
  • To introduce novel strategies for expanding search space and escaping local optima.

Main Methods:

  • Designed a chaotic difference step strategy incorporating four chaotic maps and a difference step to expand the search space.
  • Integrated opposition-based learning to generate an opposition-based population, increasing search density.
  • Proposed the COGBSO algorithm, combining chaotic difference step and opposition-based learning.

Main Results:

  • Simulation experiments conducted on the CEC2013 benchmark test suite with 15 benchmark functions.
  • Comparative analysis against recent competitive algorithms using the CEC2018 benchmark test suite.
  • Demonstrated superior performance of COGBSO in solving complex optimization problems compared to BSO and other improved algorithms.

Conclusions:

  • The proposed COGBSO algorithm effectively addresses the slow convergence and local optimum issues inherent in the traditional BSO.
  • The integration of chaotic difference step and opposition-based learning significantly enhances the algorithm's search capabilities.
  • COGBSO represents a promising advancement in metaheuristic optimization for complex problems.