Name
Title | ||
|---|---|---|
Multi-leader PSO (MLPSO): A new PSO variant for solving global optimization problems. |
Abstract | ||
|---|---|---|
•Weak exploration ability and premature convergence restrict performance of PSO.•Particles consult more valuable information to adjust its search pattern.•Leaders enhance diversity of particles' search pattern.•Particles dynamically select their leaders based on the game theory.•The best leader of generations updates itself through a self-learning process. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.asoc.2017.08.022 | Applied Soft Computing |
Keywords | Field | DocType |
Particle swarm optimization,Modified memory structure,Multi-leader mechanism,Game theory,CEC 2013 | Particle swarm optimization,Mathematical optimization,Premature convergence,Local optimum,Fuzzy cognitive map,Game theory,Artificial intelligence,Optimization problem,Mathematics,Machine learning,Global optimization problem | Journal |
Volume | ISSN | Citations |
61 | 1568-4946 | 1 |
PageRank | References | Authors |
0.35 | 31 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Penghui Liu | 1 | 9 | 1.83 |
| Jing Liu | 2 | 1043 | 115.54 |