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R2-Based Multi/Many-Objective Particle Swarm Optimization.

Alan Díaz-Manríquez1, Gregorio Toscano2, Jose Hugo Barron-Zambrano1

  • 1Facultad de Ingeniería y Ciencias, Universidad Autónoma de Tamaulipas, 87000 Victoria, TAMPS, Mexico.

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This summary is machine-generated.

This study introduces a novel approach combining the R2 performance measure with Particle Swarm Optimization for multi/many-objective optimization. The method effectively guides search without Pareto dominance, yielding competitive results against established algorithms.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Multi-objective Optimization

Background:

  • Multi-objective optimization problems (MOPs) present challenges in finding optimal solutions.
  • Existing methods often rely on Pareto dominance or external archives, which can be complex.
  • Handling many-objective optimization problems (MaOPs) requires efficient search guidance strategies.

Purpose of the Study:

  • To propose a novel approach for multi/many-objective optimization by coupling the R2 performance measure with Particle Swarm Optimization (PSO).
  • To demonstrate that this coupling can guide the search process effectively without using Pareto dominance or external archives.
  • To validate the efficacy of the proposed method on various test problems and compare it with existing Multi-Objective Evolutionary Algorithms (MOEAs).

Main Methods:

  • Integration of the R2 performance measure with Particle Swarm Optimization (PSO).
  • Development of a well-designed interaction process to maintain the core metaheuristic.
  • Validation using standard test problems and performance metrics from the literature.
  • Comparative analysis against four well-known MOEAs and an indicator-based MOEA for many-objective problems.

Main Results:

  • The proposed R2-PSO approach yields competitive results compared to four established MOEAs on multi-objective problems.
  • The method demonstrates significant strength in many-objective optimization scenarios.
  • It outperforms a well-known indicator-based MOEA specifically in many-objective problem settings.

Conclusions:

  • The R2 performance measure, when coupled with PSO, offers an effective alternative for multi/many-objective optimization.
  • This approach simplifies the search process by avoiding Pareto dominance and external archives.
  • The method shows particular promise and superior performance in the challenging domain of many-objective optimization.