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Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems.

Xiang Yu1, Xueqing Zhang2

  • 1Provincial Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang, Jiangxi, China.

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|February 14, 2017
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Summary
This summary is machine-generated.

This study introduces multiswarm comprehensive learning particle swarm optimization (MSCLPSO) for multiobjective optimization problems. MSCLPSO effectively identifies a diverse set of nondominated solutions on the true Pareto front in a single run.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • Comprehensive Learning Particle Swarm Optimization (CLPSO) is a robust single-objective metaheuristic.
  • Existing multiobjective particle swarm optimizers have limitations in handling complex objective spaces.

Purpose of the Study:

  • To extend CLPSO for multiobjective optimization by proposing the Multiswarm CLPSO (MSCLPSO) algorithm.
  • To enhance the discovery of true Pareto fronts for complex optimization problems.

Main Methods:

  • MSCLPSO utilizes multiple swarms, each optimizing a single objective independently.
  • Personal best positions are objective-specific, with external elitist storage.
  • Mutation and a modified Differential Evolution (DE) strategy are applied to elitists for improved exploration.

Main Results:

  • MSCLPSO demonstrated the ability to find nondominated solutions distributed effectively across the Pareto front.
  • Experimental results on benchmark problems validate the algorithm's performance in a single run.

Conclusions:

  • MSCLPSO offers a novel approach to multiobjective optimization by leveraging CLPSO's strengths.
  • The proposed mutation and DE strategies contribute to discovering a more accurate Pareto front.