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

Lookup NU author(s): Professor Marcus Kaiser, Professor Natalio KrasnogorORCiD


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© 2019. 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.

Publication metadata

Author(s): Jiang S, Li H, Guo J, Zhong M, Yang S, Kaiser M, Krasnogor N

Publication type: Article

Publication status: Published

Journal: Information Sciences

Year: 2020

Volume: 515

Pages: 365-387

Print publication date: 01/04/2020

Online publication date: 05/12/2019

Acceptance date: 04/12/2019

ISSN (print): 0020-0255

ISSN (electronic): 1872-6291

Publisher: Elsevier Inc.


DOI: 10.1016/j.ins.2019.12.011


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