Knowledge Transfer for Dynamic Multi-Objective Optimization With a Changing Number of Objectives

Gan RUAN, Leandro L. MINKU*, Stefan MENZEL, Bernhard SENDHOFF, Xin YAO*

*Corresponding author for this work

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

Abstract

Different from most other dynamic multi-objective optimization problems (DMOPs), DMOPs with a changing number of objectives usually result in expansion or contraction of the Pareto front or Pareto set manifold. Knowledge transfer has been used for solving DMOPs, since it can transfer useful information from solving one problem instance to solve another related problem instance. However, we show that the state-of-the-art transfer algorithm for DMOPs with a changing number of objectives lacks sufficient diversity when the fitness landscape and Pareto front shape present nonseparability, deceptiveness or other challenging features. Therefore, we propose a knowledge transfer dynamic multi-objective evolutionary algorithm (KTDMOEA) to enhance population diversity after changes by expanding/contracting the Pareto set in response to an increase/decrease in the number of objectives. This enables a solution set with good convergence and diversity to be obtained after optimization. Comprehensive studies using 13 DMOP benchmarks with a changing number of objectives demonstrate that our proposed KTDMOEA is successful in enhancing population diversity compared to state-of-the-art algorithms, improving optimization especially in fast changing environments.
Original languageEnglish
Number of pages15
JournalIEEE Transactions on Emerging Topics in Computational Intelligence
Early online date2 May 2024
DOIs
Publication statusE-pub ahead of print - 2 May 2024

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 62250710682, in part by Guangdong Provincial Key Laboratory under Grant 2020B121201001, in part by the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under Grant 2017ZT07X386, and in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 766186.

© 2024 The Authors.

Keywords

  • Knowledge transfer
  • Changing objectives
  • Dynamic optimization
  • Evolutionary algorithms
  • Multi-objective optimization

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