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A Decomposition-Based Evolutionary Algorithm with Correlative Selection Mechanism for Many-Objective Optimization.

Ruochen Liu1, Ruinan Wang2, Renyu Bian3

  • 1Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, International Center of Intelligent Perception and Computation, Xidian University, Xi'an, 710071, China ruochenliu@xidian.edu.cn.

Evolutionary Computation
|October 13, 2020
PubMed
Summary
This summary is machine-generated.

A new many-objective evolutionary algorithm based on decomposition with correlative selection mechanism (MOEA/D-CSM) maintains population diversity. This approach effectively guides solutions toward the Pareto-optimal front in many-objective optimization problems.

Keywords:
Many-objective optimizationcorrelative selection mechanism.decompositionevolutionary computation

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Evolutionary Computation

Background:

  • Decomposition-based evolutionary algorithms are effective for multiobjective optimization.
  • Applying decomposition to many-objective optimization problems is an emerging research area.
  • Existing methods require careful reference point generation for many objectives.

Purpose of the Study:

  • To propose a novel many-objective evolutionary algorithm based on decomposition with a correlative selection mechanism (MOEA/D-CSM).
  • To enhance diversity maintenance and convergence towards the Pareto-optimal front in many-objective problems.
  • To introduce and utilize the concept of correlation between individuals and reference points.

Main Methods:

  • Developed MOEA/D-CSM, incorporating a correlative selection mechanism.
  • Introduced the concept of 'correlation' between individuals and reference points.
  • Utilized penalty boundary intersection (PBI) for decomposition and employed a selection strategy to remove suboptimal individuals.

Main Results:

  • MOEA/D-CSM demonstrated competitive performance on many-objective test problems (3-15 objectives).
  • The correlative selection mechanism effectively maintained population diversity.
  • The algorithm showed improved convergence towards the Pareto-optimal front compared to state-of-the-art algorithms.

Conclusions:

  • MOEA/D-CSM is a promising approach for solving many-objective optimization problems.
  • The correlative selection mechanism is key to maintaining diversity and improving convergence.
  • The proposed algorithm offers a competitive alternative to existing many-objective evolutionary algorithms.