Name
Abstract | ||
|---|---|---|
This paper presents a two-stage approach, named Cooperatively Coevolving Competition Swarm Optimiser ((CSO)-S-3), for large-scale optimisation. (CSO)-S-3 first detects interactions among the original variables by a differential grouping algorithm, therein decomposing a large-scale problem into several sub-components. In the next stage, a new approach is developed using the competition mechanism to independently optimise each sub-component obtained in the first stage. Hence, (CSO)-S-3 takes advantages of both divide-and-conquer and competition strategies, achieving the ability to address large-scale and complex optimisation problems. The proposed method is evaluated by comparing with several state-of-the-art algorithms on different benchmark functions, and the comparative results demonstrated its effectiveness. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1080/17517575.2019.1681518 | ENTERPRISE INFORMATION SYSTEMS |
Keywords | DocType | Volume |
Large-scale optimisation optimiser, cooperative co-evolution, competition, swarm intelligence | Journal | 14 |
Issue | ISSN | Citations |
9-10 | 1751-7575 | 1 |
PageRank | References | Authors |
0.35 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rushi Lan | 1 | 100 | 15.72 |
| Yu Zhu | 2 | 65 | 12.88 |
| Huimin Lu | 3 | 780 | 73.60 |
| Zhiling Tang | 4 | 4 | 2.42 |
| Zhenbing Liu | 5 | 33 | 11.75 |
| Xiaonan Luo | 6 | 697 | 92.76 |