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This study introduces HCRMOPSO, a novel multi-objective particle swarm optimization algorithm. It enhances diversity and adaptability, outperforming existing methods on benchmark problems.

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Swarm intelligence

Background:

  • Traditional multi-objective particle swarm optimization (MOPSO) faces challenges like low archive diversity and parameter sensitivity.
  • Balancing global exploration and local exploitation remains difficult in existing MOPSO methods.

Purpose of the Study:

  • To introduce HCRMOPSO, a novel algorithm addressing MOPSO's limitations.
  • To improve diversity maintenance, parameter adaptability, and overall performance in multi-objective optimization.

Main Methods:

  • Leverages hierarchical clustering (Ward's linkage) for reference points, combined with ideal point and crowding distance.
  • Implements particle fusion for updating personal best positions and adaptive parameter tuning based on neighborhood diversity.
  • Introduces a new strategy for specific particle types to optimize the search process.

Main Results:

  • HCRMOPSO effectively maintains external archive diversity, mitigating deficiencies in traditional MOPSO.
  • Adaptive parameter tuning enhances the algorithm's overall adaptability and search efficiency.
  • Demonstrates superior performance compared to ten existing algorithms across 22 standard test problems.

Conclusions:

  • HCRMOPSO offers a significant advancement in multi-objective particle swarm optimization.
  • The proposed methods effectively address diversity issues and improve optimization capabilities.
  • HCRMOPSO shows superior effectiveness for complex multi-objective optimization tasks.