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.
Bibliographical noteSJ, MK, and NK acknowledge the Engineering and Physical Sciences Research Council (EPSRC) for funding project “Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies (EP/N031962/1)”.
CRediT authorship contribution statement
Shouyong Jiang: Conceptualization, Methodology, Software, Writing - original draft. Hongru Li: Methodology. Jinglei Guo: Conceptualization, Writing - review & editing. Mingjun Zhong: Writing - review & editing. Shengxiang Yang: Conceptualization, Writing - review & editing. Marcus Kaiser: Supervision, Writing - review & editing. Natalio Krasnogor: Supervision, Writing - review & editing.
- Local mating
- Multiobjective optimisation
- Pareto front
- Reference set
- Search target
- NONDOMINATED SORTING APPROACH
- SCALARIZING FUNCTIONS
- PREFERENCE ARTICULATION