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Handling multi-objective optimization problems with a comprehensive indicator and layered particle swarm optimizer.

Xianzi Zhang1, Yanmin Liu2, Jie Yang2

  • 1School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang 550025, China.

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|September 7, 2023
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Summary
This summary is machine-generated.

A new algorithm, IMOPSOCE, enhances multi-objective particle swarm optimization by improving convergence and diversity. It addresses common issues like premature convergence in complex problems.

Keywords:
distance of inflection pointdistribution coefficientmulti-objective optimizationmulti-objective particle swarm optimization

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • Multi-objective particle swarm optimization (MOPSO) faces challenges like premature convergence and inadequate diversity, especially in high-dimensional problems.
  • These limitations can lead to algorithms getting trapped in local optima, hindering effective solution finding.

Purpose of the Study:

  • To introduce a novel algorithm, IMOPSOCE, designed to overcome the inherent drawbacks of traditional MOPSO.
  • To enhance both the convergence speed and the diversity of solutions in complex optimization scenarios.

Main Methods:

  • Developed an external archive maintenance strategy incorporating inflection point distance and distribution coefficient.
  • Utilized a comprehensive indicator (CM) to refine non-dominated solutions, improving swarm convergence and diversity.
  • Implemented a random inertia weight strategy to balance exploration and exploitation.
  • Introduced adaptive flight modes for particles to boost overall optimization capacity.

Main Results:

  • IMOPSOCE demonstrated improved convergence and diversity compared to existing MOPSO algorithms.
  • The algorithm effectively avoided local optima in complex, high-dimensional test functions.
  • Comparative analysis on 22 test functions showed IMOPSOCE's superior performance against 10 other algorithms.

Conclusions:

  • The proposed IMOPSOCE algorithm offers a significant advancement in addressing MOPSO limitations.
  • Its innovative strategies provide enhanced performance, particularly for complex and high-dimensional optimization tasks.
  • IMOPSOCE shows strong competitiveness and outperformance across a majority of tested functions.