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A Many-Objective Optimization Algorithm Based on Weight Vector Adjustment.

Yanjiao Wang1, Xiaonan Sun1

  • 1School of Electrical Engineering, Northeast Electric Power University, Jilin 132000, Jilin, China.

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|November 15, 2018
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Summary
This summary is machine-generated.

This study introduces NSGA-III-WA, an enhanced many-objective evolutionary algorithm. It improves convergence and distribution on the Pareto front using adaptive weight vector adjustment and a hybrid evolutionary model.

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Area of Science:

  • Multi-objective optimization
  • Evolutionary computation

Background:

  • Many-objective evolutionary algorithms (MOEAs) face challenges in convergence and solution distribution.
  • Existing algorithms like NSGA-III require improvements for complex objective spaces.

Purpose of the Study:

  • To enhance the performance of the NSGA-III algorithm for many-objective optimization problems.
  • To improve both convergence speed and the uniform distribution of solutions on the Pareto front.

Main Methods:

  • Proposed NSGA-III-WA algorithm featuring adaptive weight vector adjustment.
  • Decomposition of objective space into subspaces with density-based weight vector allocation.
  • Hybrid evolutionary model combining differential and genetic evolution strategies.

Main Results:

  • NSGA-III-WA demonstrated superior convergence and distribution compared to five other algorithms.
  • Effective performance on DTLZ and WFG test instances with 3-15 objectives.
  • Whitney-Wilcoxon rank-sum test confirmed the significance of the improvements.

Conclusions:

  • The adaptive weight vector adjustment and hybrid evolutionary model effectively enhance MOEA performance.
  • NSGA-III-WA offers a robust solution for achieving better convergence and distribution in many-objective optimization.