Name
Abstract | ||
|---|---|---|
Multimodal optimization is still one of the most challenging tasks for evolutionary computation. In recent years, many evolutionary multi-modal optimization algorithms have been developed. All these algorithms must tackle two issues in order to successfully solve a multi-modal problem: how to identify multiple global/local optima and how to maintain the identified optima till the end of the search. For most of the multi-modal optimization algorithms, the fine-local search capabilities are not effective. If the required accuracy is high, these algorithms fail to find the desired optima even after converging near them. To overcome this problem, this paper integrates a novel local search technique with some existing PSO based multimodal optimization algorithms to enhance their local search ability. The algorithms are tested on 14 commonly used multi-modal optimization problems and the experimental results suggest that the proposed technique not only increases the probability of finding both global and local optima but also reduces the average number of function evaluations. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.ins.2012.02.011 | Inf. Sci. |
Keywords | DocType | Volume |
novel local search technique,local optimum,niching particle swarm optimization,multi-modal problem,multi-modal optimization problem,evolutionary multi-modal optimization algorithm,multi-modal optimization algorithm,fine-local search capability,multimodal optimization algorithm,Multimodal optimization,local search ability | Journal | 197, |
ISSN | Citations | PageRank |
0020-0255 | 52 | 1.56 |
References | Authors | |
20 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| B. Y. Qu | 1 | 203 | 11.67 |
| Jing J. Liang | 2 | 2073 | 107.92 |
| P. N. Suganthan | 3 | 10876 | 412.72 |