A Two-Population Algorithm for Large-Scale Multi-objective Optimization Based on Fitness-Aware Operator and Adaptive Environmental Selection

Bingdong LI, Yan ZHANG, Peng YANG, Xin YAO, Aimin ZHOU

Research output: Journal PublicationsJournal Article (refereed)peer-review

3 Citations (Scopus)

Abstract

Multi-objective optimization problems (MOPs) containing a large number of decision variables, which are also known as large-scale multi-objective optimization problems (LSMOPs), pose great challenges to most existing evolutionary algorithms. This is mainly because that a high dimensional decision space degrades the effectiveness of search operators notably, and balancing convergence and diversity becomes a challenging task. In this paper, we propose a two-population based algorithm for large-scale multi-objective optimization named LSTPA. In the proposed algorithm, solutions are classified in to two subpopulations: a Convergence subPopulation (CP) and a Diversity subPopulation (DP), aiming at convergence and diversity respectively. In order to improve convergence speed, a fitness-aware variation operator (FAVO) is applied to drive DP solutions towards CP. Besides, an adaptive penalty based boundary intersection (APBI) strategy is adopted for environmental selection in order to balance convergence and diversity temporally during different stages of evolution process. Experimental results on benchmark test problems with 100-2000 decision variables demonstrate that the proposed algorithm can achieve the best overall performance compared with several state-of-the-art large-scale multi-objective evolutionary algorithms. IEEE
Original languageEnglish
Pages (from-to)1-1
Number of pages1
JournalIEEE Transactions on Evolutionary Computation
DOIs
Publication statusPublished - 2023
Externally publishedYes

Keywords

  • Classification algorithms
  • Convergence
  • Evolutionary Algorithm
  • Evolutionary computation
  • Evolutionary Multi-objective Optimization
  • Fitness-Aware Operator
  • Large-Scale Multi-objective Optimization
  • Optimization
  • Search problems
  • Sociology
  • Statistics
  • Two-Archive Algorithm

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