Name
Title | ||
|---|---|---|
A cluster-based immune-inspired algorithm using manifold learning for multimodal multi-objective optimization |
Abstract | ||
|---|---|---|
Both problem characteristics in multimodality and multi-objective are involved in multimodal multi-objective optimization problems (MMOPs). How to locate diverse Pareto sets and approximate Pareto front simultaneously is a challenging research topic. To address this issue, a cluster-based immune-inspired algorithm using manifold learning is proposed in this paper for solving MMOPs. First of all, the population is partitioned into multiple subpopulations, and each of them is expected to find equivalent Pareto solutions in different regions. Subsequently, the immune-inspired algorithm with proportional cloning and hypermutation is developed for improving the diversity of the population and obtaining high-quality Pareto solutions in the decision space. Additionally, principal component analysis is adopted to learn the manifold of the Pareto set, further improve the convergence, and enhance interaction among subpopulations. The proposed algorithm is compared with six state-of-the-art algorithms. Experimental results demonstrate that the proposed algorithm is capable of locating equivalent Pareto optimal solutions in the decision space and maintaining the diversity and convergence of solutions in both decision space and objective space, simultaneously. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.ins.2021.09.043 | Information Sciences |
Keywords | DocType | Volume |
Multimodal multi-objective optimization,Immune-inspired algorithm,Manifold learning,Principal component analysis | Journal | 581 |
ISSN | Citations | PageRank |
0020-0255 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiwei Zhang | 1 | 0 | 0.34 |
| Ningjun Zhang | 2 | 0 | 0.34 |
| Weizheng Zhang | 3 | 14 | 2.22 |
| Gary G. Yen | 4 | 1744 | 94.45 |
| Guoqing Li | 5 | 2 | 1.72 |