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Ding, R, Dong, H, He, J 
ORCID: https://orcid.org/0000-0002-5616-4691 and Li, T,
2019.
A novel two-archive strategy for evolutionary many-objective optimization algorithm based on reference points.
Applied Soft Computing, 78, pp. 447-464.
ISSN 1568-4946
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Abstract
Current evolutionary many-objective optimization algorithms face two challenges: one is to ensure population diversity for searching the entire solution space. The other is to ensure quick convergence to the optimal solution set. In this paper, we propose a novel two-archive strategy for evolutionary many-objective optimization algorithm. The uniform archive strategy, based on reference points, is used to keep population diversity in the evolutionary process, and to ensure that an evolutionary algorithm is able to search the entire solution space. The single elite archive strategy is used to ensure that individuals with the best single objective value are able to evolve into the next generation and have more opportunities to generate offspring. This strategy aims to improve the convergence rate. Then this novel two-archive strategy is applied to improving the Non-dominated Sorting Genetic Algorithm (NSGA-III). Simulation experiments are conducted on benchmark test sets and experimental results show that our proposed algorithm with the two-archive strategy has a better performance than other state-of-art algorithms.
| Item Type: | Journal article |
|---|---|
| Publication Title: | Applied Soft Computing |
| Creators: | Ding, R., Dong, H., He, J. and Li, T. |
| Publisher: | Elsevier |
| Date: | 2019 |
| Volume: | 78 |
| ISSN: | 1568-4946 |
| Identifiers: | Number Type 10.1016/j.asoc.2019.02.040 DOI S1568494619301048 Publisher Item Identifier |
| Divisions: | Schools > School of Science and Technology |
| Record created by: | Jonathan Gallacher |
| Date Added: | 18 Mar 2019 11:48 |
| Last Modified: | 01 Mar 2020 03:00 |
| URI: | https://irep.ntu.ac.uk/id/eprint/36061 |
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