Name
Abstract | ||
|---|---|---|
High-Dimensional Dynamic Optimization Problems (HDDOPs) commonly exist in real-world applications. In evolutionary computation field, most of existing benchmark problems, which could simulate HDDOPs, are non-separable. Thus, we give a novel benchmark problem, called high-dimensional moving peaks benchmark to simulate separable, partially separable, and non-separable problems. Moreover, a hybrid Particle Swarm Optimization algorithm based on Grouping, Clustering and Memory strategies, i.e. GCM-PSO, is proposed to solve HDDOPs. In GCM-PSO, a differential grouping method is used to decompose a HDDOP into a number of sub-problems based on variable interactions firstly. Then each sub-problem is solved by a species-based particle swarm optimization, where the nearest better clustering is adopted as the clustering method. In addition, a memory strategy is also adopted in GCM-PSO. Experimental results show that GCM-PSO performs better than the compared algorithms in most cases. |
Year | Venue | Field |
|---|---|---|
2017 | SEAL | Particle swarm optimization,Mathematical optimization,Computer science,Separable space,Evolutionary computation,Multi-swarm optimization,Cluster analysis,Optimization problem,Metaheuristic |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
14 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenjian Luo | 1 | 356 | 40.95 |
| Bin Yang | 2 | 82 | 33.30 |
| Chenyang Bu | 3 | 47 | 9.18 |
| Xin Lin | 4 | 66 | 18.39 |