Name
Abstract | ||
|---|---|---|
One of the biggest drawbacks of the original Particle Swarm Optimization is the premature convergence and fast loss of diversity in the population. In this paper, we propose and discuss a simple yet effective modification to help the PSO maintain diversity and avoid premature convergence. The particles are randomly attracted towards the border points of the search space. We use the CEC13 Benchmark function set to test the performance of proposed method and compare it to original PSO. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-59060-8_60 | ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2017, PT II |
Keywords | Field | DocType |
Particle swarm optimization, PSO, Diversity | Particle swarm optimization,Population,Mathematical optimization,Premature convergence,Computer science | Conference |
Volume | ISSN | Citations |
10246 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michal Pluhacek | 1 | 217 | 47.34 |
| Roman Senkerik | 2 | 375 | 74.92 |
| Adam Viktorin | 3 | 29 | 16.76 |
| Tomas Kadavy | 4 | 20 | 20.97 |