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April 1, 2026Open Access

Multi-objective Particle Swarm Optimization considering the diversity of the inferior solutions

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Authors

哲北哲士 北山雅荒雅生 荒川光山光悦 山崎

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Overview

Multi-objective optimization using Particle Swarm Optimization improves solution diversity, indicating enhanced effectiveness of the approach.

Key Points

  • The research aims to enhance solution diversity in multi-objective optimization using Particle Swarm Optimization (PSO).
  • Developed a method for selecting g-best particles from non-inferior solutions.
  • Defined g-best without adding new parameters using relative distance in objective space.
  • Selected g-best among inferior solutions using absolute distance in objective space.
  • Provided geometric interpretation of particle movement.
  • Validated the approach with numerical examples.
  • Achieved increased diversity among pareto optimal solutions.
  • Demonstrated effectiveness through typical numerical examples.
  • Showed that the proposed method improves the search process in multi-objective problems.

Cite This Study

北山 et al. (2008) studied this question.

synapsesocial.com/papers/69cd7ae65652765b073a87c8https://doi.org/10.24517/00007841
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