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An adaptive decomposition evolutionary algorithm based on environmental information for many-objective optimization.

Zhihui Wei1, Jingming Yang1, Ziyu Hu1

  • 1Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, China.

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|November 9, 2020
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Summary
This summary is machine-generated.

A new adaptive decomposition evolutionary algorithm (MaOEA/ADEI) balances convergence and diversity in many-objective problems by dynamically adjusting the penalty factor using environmental information. This approach outperforms traditional methods on benchmark tests.

Keywords:
Adaptive decompositionEvolutionary algorithmMany-objective optimizationWeight vectors adaption

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Area of Science:

  • Optimization algorithms
  • Evolutionary computation
  • Multi-objective optimization

Background:

  • Traditional penalty boundary intersection (PBI) decomposition methods struggle with many-objective problems due to fixed penalty factors.
  • This imbalance between convergence and diversity hinders performance in complex optimization tasks.

Purpose of the Study:

  • To propose an adaptive decomposition evolutionary algorithm based on environmental information (MaOEA/ADEI).
  • To address the convergence-diversity imbalance in many-objective optimization problems.

Main Methods:

  • Developed MaOEA/ADEI with an adaptive penalty factor determined by environmental information (weight vector distribution and population status).
  • Incorporated a parent individual selection strategy for enhanced variation.
  • Implemented a weight vector adaptation strategy to manage scaled objectives.

Main Results:

  • MaOEA/ADEI demonstrated superior performance compared to four other algorithms across 24 benchmark instances.
  • The proposed algorithm achieved the best results on 14 of the tested instances.

Conclusions:

  • The adaptive penalty factor mechanism effectively balances convergence and diversity.
  • MaOEA/ADEI offers a robust solution for many-objective optimization problems, outperforming existing methods.