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A Meta-Objective Approach for Many-Objective Evolutionary Optimization.

Dunwei Gong1, Yiping Liu1,2, Gary G Yen3

  • 1School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.

Evolutionary Computation
|November 27, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel meta-objective approach to enhance multi-objective evolutionary algorithms for many-objective optimization problems. The method improves convergence and diversity, boosting algorithm performance in high-dimensional spaces.

Keywords:
Many-objective optimizationconvergencediversity.evolutionary multi-objective optimizationmeta-objective

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Area of Science:

  • Computational Intelligence
  • Optimization Theory
  • Computer Science

Background:

  • Many-objective optimization problems (MaOPs) pose significant challenges for Pareto-based evolutionary algorithms.
  • Maintaining both convergence and diversity in high-dimensional objective spaces is a key difficulty.

Purpose of the Study:

  • To propose a novel meta-objective (MeO) approach to transform MaOPs into more tractable forms.
  • To enhance the performance of Pareto-based algorithms in solving MaOPs.

Main Methods:

  • Developed a meta-objective (MeO) framework that reformulates MaOPs without altering Pareto optimal solutions or the number of objectives.
  • Each meta-objective incorporates components measuring solution convergence and diversity.
  • The MeO approach can be integrated with various multi-objective evolutionary algorithms.

Main Results:

  • The MeO approach effectively separates convergence and diversity evaluation, overcoming limitations of traditional density-based criteria like crowding distance.
  • Penalizing solutions based on convergence performance in the meta-objective space enhances Pareto dominance effectiveness.
  • Comparative studies demonstrate the competitive performance of the MeO approach on MaOPs.

Conclusions:

  • The proposed meta-objective approach offers a significant improvement for solving many-objective optimization problems.
  • It enhances the ability of Pareto-based algorithms to handle high-dimensional objective spaces.
  • The MeO method provides a flexible and effective strategy for tackling complex optimization challenges.