A self-exploratory competitive swarm optimization algorithm for large-scale multiobjective optimization
| dc.cclicence | CC-BY-NC-ND | en |
| dc.contributor.author | Qi, Sheng | |
| dc.contributor.author | Zou, Juan | |
| dc.contributor.author | Yang, Shengxiang | |
| dc.contributor.author | Jin, Yaochu | |
| dc.contributor.author | Zheng, Jinhua | |
| dc.contributor.author | Yang, Xu | |
| dc.date.acceptance | 2022-07-18 | |
| dc.date.accessioned | 2022-08-09T15:47:59Z | |
| dc.date.available | 2022-08-09T15:47:59Z | |
| dc.date.issued | 2022-09-22 | |
| 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 | With the popularity of “flipped classrooms,” teachers pay more attention to cultivating students’ autonomous learning ability while imparting knowledge. Inspired by this, this paper proposes a Self-exploratory Competitive Swarm Optimization algorithm for Large-scale Multiobjective Optimization (SECSO). Its idea is very simple and there are no parameters that need to be adjusted. Particles evolve by exploring their neighboring space and learning from other particles in the swarm, thereby simultaneously enhancing the diversity and convergence performance of the algorithm. Compared with eight state-of-the-art large-scale multiobjective evolutionary algorithms, the proposed method exhibited outstanding performance on LSMOP problems with up to 10,000 decision variables. Unlike most existing large-scale evolutionary algorithms that usually require a large number of objective evaluations, SECSO shows the ability to find a set of well converged and diverse non-dominated solutions. | en |
| dc.funder | Other external funder (please detail below) | en |
| dc.funder.other | National Natural Science Foundation of China | en |
| dc.funder.other | Natural Science Foundation of Hunan Province, China | en |
| dc.identifier.citation | Qi, S., Zou, J., Yang, S., Jin, Y., Zheng, J. and Yang. X. (2022) A self-exploratory competitive swarm optimization algorithm for large-scale multiobjective optimization. Information Sciences, 609, pp. 1601-1620 | en |
| dc.identifier.doi | https://doi.org/10.1016/j.ins.2022.07.110 | |
| dc.identifier.issn | 0020-0255 | |
| dc.identifier.uri | https://hdl.handle.net/2086/22094 | |
| dc.language.iso | en_US | en |
| dc.peerreviewed | Yes | en |
| dc.projectid | 62176228 and 61876164 | en |
| dc.projectid | 2020JJ4590 | en |
| dc.publisher | Elsevier | en |
| dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
| dc.subject | Evolutionary algorithms | en |
| dc.subject | Large-scale optimization | en |
| dc.subject | Multiobjective optimization | en |
| dc.subject | Self-exploratory | en |
| dc.title | A self-exploratory competitive swarm optimization algorithm for large-scale multiobjective optimization | en |
| dc.type | Article | en |