AREA: An adaptive reference-set based evolutionary algorithm for multiobjective optimisation
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.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Jiang, S., Li, H.,Guo, J., Zhong, M.,Yang, S., Kaiser, M. and Krasnogor, N. (2020) AREA: An adaptive reference-set based evolutionary algorithm for multiobjective optimisation. Information Sciences,515, pp. 365-387.
ISSN : 0020-0255
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes