Name
Abstract | ||
|---|---|---|
As a new and promising swarm intelligence algorithm, brain storm optimization (BSO) has drawn more attention of researches and has been successfully applied to solve the real-world optimization problems. However, too many parameters make the algorithm more complex and greatly limit the convergence performance. Thus, this paper proposed a novel BSO variant, named self-adaptive BSO with pbest guided step-size (SPBSO), in which a simple self-adaptive strategy is employed to choose a creating strategy in a random manner rather than depending on several adjustable parameters. In addition, the pbest guided step-size and dynamic clustering number are used to accelerate the convergence speed. The experimental studies have been tested on a set of widely used benchmark functions (including the CEC 2014 problems). Experimental results and comparison with the state-of-the-art BSO variants and some recently proposed PSO and DE algorithms, have proved that the proposed algorithm is competitive. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3233/JIFS-181310 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | Field | DocType |
Brain storm optimization,global optimization,self-adaptive strategy,pbest guided step-size | Storm,Self adaptive,Artificial intelligence,Optimization algorithm,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
36 | 6 | 1064-1246 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hu Peng | 1 | 46 | 13.63 |
| Changshou Deng | 2 | 1 | 1.02 |
| Zhijian Wu | 3 | 247 | 18.55 |