Dynamic multi-objective evolutionary algorithms for single-objective optimization
•Convert a single-objective optimization problem with many local optima into an equivalent dynamic multi-objective optimization problem.•The converted two objectives include the original objective and a niche-count objective, niche-count aims to maintain the population diversity.•The niche radius pr...
Uložené v:
| Vydané v: | Applied soft computing Ročník 61; s. 793 - 805 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
01.12.2017
|
| Predmet: | |
| ISSN: | 1568-4946, 1872-9681 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Shrnutí: | •Convert a single-objective optimization problem with many local optima into an equivalent dynamic multi-objective optimization problem.•The converted two objectives include the original objective and a niche-count objective, niche-count aims to maintain the population diversity.•The niche radius provides a proper balance between the exploitation and the exploration by gradually pushing the niche radius to zero.
This paper proposes a new method for handling the difficulty of multi-modality for the single-objective optimization problem (SOP). The method converts a SOP to an equivalent dynamic multi-objective optimization problem (DMOP). A new dynamic multi-objective evolutionary algorithm (DMOEA) is implemented to solve the DMOP. The DMOP has two objectives: the original objective and a niche-count objective. The second objective aims to maintain the population diversity for handling the multi-modality difficulty during the search process. Experimental results show that the performance of the proposed algorithm is significantly better than the state-of-the-art competitors on a set of benchmark problems and real world antenna array problems. |
|---|---|
| ISSN: | 1568-4946 1872-9681 |
| DOI: | 10.1016/j.asoc.2017.08.030 |