AREA: An adaptive reference-set based evolutionary algorithm for multiobjective optimisation

Shouyong Jiang*, Hongru Li, Jinglei Guo, Mingjun Zhong, Shengxiang Yang, Marcus Kaiser, Natalio Krasnogor

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Population-based evolutionary algorithms have great potential to handle multiobjective optimisation problems. However, the performance of these algorithms depends largely on problem characteristics. There is a need to improve these algorithms for wide applicability. References, often specified by the decision maker's preference in different forms, are very effective to boost the performance of algorithms. This paper proposes a novel framework for effective use of references to strengthen algorithms. This framework considers references as search targets which can be adjusted based on the information collected during the search. The proposed framework is combined with new strategies, such as reference adaptation and adaptive local mating, to solve different types of problems. The proposed algorithm is compared with state-of-the-arts on a wide range of problems with diverse characteristics. The comparison and extensive sensitivity analysis demonstrate that the proposed algorithm is competitive and robust across different types of problems studied in this paper.

Original languageEnglish
Pages (from-to)365-387
Number of pages23
JournalInformation Sciences
Volume515
Early online date5 Dec 2019
DOIs
Publication statusPublished - Apr 2020

Keywords

  • Local mating
  • Multiobjective optimisation
  • Pareto front
  • Reference set
  • Search target
  • NONDOMINATED SORTING APPROACH
  • DOMINANCE
  • PERFORMANCE
  • DECOMPOSITION
  • SCALARIZING FUNCTIONS
  • PREFERENCE ARTICULATION
  • MOEA/D

ASJC Scopus subject areas

  • Software
  • Information Systems and Management
  • Artificial Intelligence
  • Theoretical Computer Science
  • Control and Systems Engineering
  • Computer Science Applications

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