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A clustering-based competitive particle swarm optimization with grid ranking for multi-objective optimization

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Summary
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A new algorithm, EGC-CMOPSO, efficiently finds optimal trade-off solutions for complex problems. It uses clustering and an enhanced grid to improve accuracy and performance in multi-objective optimization.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • Multi-objective optimization problems (MOPs) require algorithms to find a set of trade-off solutions efficiently and accurately.
  • Existing algorithms often struggle with diverse Pareto front (PF) shapes and identifying superior solutions.

Purpose of the Study:

  • To develop a novel clustering-based competitive multi-objective particle swarm optimizer (EGC-CMOPSO) for enhanced MOPs solving.
  • To improve the accuracy and speed of finding Pareto optimal solutions in MOPs.

Main Methods:

  • Development of EGC-CMOPSO incorporating an enhanced grid mechanism for superior Pareto optimal solution localization.
  • Implementation of hierarchical-based clustering on the grid to refine selection accuracy.
  • Utilizing adaptive clustering centers for applicability across various PF shapes.
  • Employing competitive learning among leading particles within clusters to guide updates.

Main Results:

  • EGC-CMOPSO demonstrated superior performance compared to eight state-of-the-art algorithms on 21 test problems.
  • The algorithm effectively handled multi-objective optimization problems with varying Pareto front shapes.
  • Validation of enhanced convergence and diversity in the obtained solution sets.

Conclusions:

  • EGC-CMOPSO offers a robust and effective approach for solving multi-objective optimization problems.
  • The enhanced grid and clustering strategies significantly improve solution quality and algorithm efficiency.
  • The proposed method shows strong potential for practical applications requiring fast and accurate multi-objective optimization.