Name
Abstract | ||
|---|---|---|
A heuristic version of the particle swarm optimization (PSO) is introduced in this paper. In this new method called "The heuristic particle swarm optimization(HPSO)", we use heuristics to choose the next particle to update its velocity and position. By using heuristics , the convergence rate to local minimum is faster. To avoid premature convergence of the swarm, the particles are re-initialized with random velocity when moving too close to the global best position. The combination of heuristics and re-initialization mechanism make HPSO outperform the basic PSO and recent versions of PSO. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1276958.1276988 | GECCO |
Keywords | Field | DocType |
random velocity,particle swarm optimization,next particle,global best position,premature convergence,basic pso,heuristic particle swarm optimization,heuristic version,convergence rate,local minimum,heuristic | Particle swarm optimization,Heuristic,Mathematical optimization,Premature convergence,Swarm behaviour,Computer science,Multi-swarm optimization,Heuristics,Artificial intelligence,Rate of convergence,Machine learning,Metaheuristic | Conference |
Citations | PageRank | References |
3 | 0.43 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hoang Thanh Lam | 1 | 108 | 8.49 |
| Popova Nina Nicolaevna | 2 | 3 | 0.43 |
| Nguyen Thoi Minh Quan | 3 | 3 | 0.43 |