Ra-dominance: A new dominance relationship for preference-based evolutionary multiobjective optimization
| dc.cclicence | N/A | en |
| dc.contributor.author | Zou, Juan | |
| dc.contributor.author | Yang, Qite | |
| dc.contributor.author | Yang, Shengxiang | |
| dc.contributor.author | Zheng, Jinhua | |
| dc.date.acceptance | 2020-02-18 | |
| dc.date.accessioned | 2020-03-03T09:48:04Z | |
| dc.date.available | 2020-03-03T09:48:04Z | |
| dc.date.issued | 2020-02-25 | |
| dc.description | 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. | en |
| dc.description.abstract | While traditional Pareto-based evolutionary multi-objective optimization (EMO) algorithms have shown an excellent balance between convergence and diversity on a wide range of practical problems with two or three objectives in real applications, the decision maker (DM) is interested in a unique set of solutions rather than the whole population on Pareto optimal front (POF). In addition, Pareto-based EMO algorithms have some shortcomings in dealing with many-objective problems because of insufficient selection pressure toward trade-off solutions. Due to the above, it is crucial to incorporate DM preference information into EMO and seek a representative subset of Pareto optimal solutions with an increase in the number of objectives. This paper proposes a new dominance relationship, called Ra-dominance, which can improve diversity among the Pareto-equivalent solutions increase the selection pressure in evolutionary process. It has the ability to guide the population toward areas more responsive to the needs of the DM according to a reference point and preference angle. We use the new dominance relationship in the NSGA-II algorithm, and the efficacy and usefulness of the modified procedure are assessed through two- to ten-objective problems. Experimental results show that the algorithm applying this new dominance relationship is highly competitive when compared with four state-of-the-art preference-based EMO methods. | en |
| dc.funder | No external funder | en |
| dc.funder.other | National Natural Science Foundation of China | en |
| dc.identifier.citation | Zou, J., Yang, Q., Yang, S., and Zheng, J. (2020) Ra-dominance: A new dominance relationship for preference-based evolutionary multiobjective optimization. Applied Soft Computing, 90, 106192. | en |
| dc.identifier.doi | https://doi.org/10.1016/j.asoc.2020.106192 | |
| dc.identifier.issn | 1568-4946 | |
| dc.identifier.uri | https://dora.dmu.ac.uk/handle/2086/19276 | |
| dc.language.iso | en_US | en |
| dc.peerreviewed | Yes | en |
| dc.projectid | 61876164, 61673331, 61379062 and 61772178 | en |
| dc.publisher | Elsevier | en |
| dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
| dc.subject | Evolutionary algorithm | en |
| dc.subject | Reference point | en |
| dc.subject | Decision maker | en |
| dc.subject | Multiobjective optimization | en |
| dc.subject | Dominance relationship | en |
| dc.title | Ra-dominance: A new dominance relationship for preference-based evolutionary multiobjective optimization | en |
| dc.type | Article | en |