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A dual-population Constrained Many-Objective Evolutionary Algorithm based on reference point and angle easing

Chen Ji1, Linjie Wu1, Tianhao Zhao1

  • 1Shanxi Key Laboratory of Big Data Analysis and Parallel Computing, Taiyuan University of Science and Technology, Taiyuan, ShanXi, China.

Peerj. Computer Science
|August 15, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new dual-population evolutionary algorithm to solve complex constrained many-objective optimization problems (CMaOPs). The novel approach effectively identifies challenging constrained Pareto frontiers (CPFs), improving optimization performance.

Keywords:
Constraint handlingDual-populationEasing strategyEvolutionary algorithmMany-objective optimization

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Area of Science:

  • Optimization Algorithms
  • Computational Intelligence
  • Mathematical Modeling

Background:

  • Constrained many-objective optimization problems (CMaOPs) present challenges due to intricate and uneven Pareto frontiers.
  • Traditional algorithms often suffer from premature convergence, hindering the discovery of optimal feasible solutions.
  • Existing methods struggle to effectively navigate and identify complex constrained Pareto frontiers (CPFs).

Purpose of the Study:

  • To develop a novel algorithm for effectively solving CMaOPs.
  • To address the limitations of traditional algorithms in converging to refined and uneven PFs.
  • To improve the discovery of superior solutions in complex optimization landscapes.

Main Methods:

  • A dual-population constrained many-objective evolutionary algorithm (dCMaOEA-RAE) was proposed.
  • A relaxed selection strategy using reference points and angle easing was employed.
  • Cooperation between dual populations was facilitated to retain potentially valuable solutions.

Main Results:

  • The dCMaOEA-RAE algorithm demonstrated competitiveness across three evaluation indicators.
  • Experimental results on 77 test problems validated the algorithm's efficacy.
  • Comparisons with ten state-of-the-art algorithms confirmed its superior performance.

Conclusions:

  • The proposed dCMaOEA-RAE effectively guides populations towards optimal feasible regions.
  • The algorithm successfully obtains superior solutions for CMaOPs.
  • This novel approach offers a significant advancement in tackling challenging optimization problems.