Name
Abstract | ||
|---|---|---|
Particle Swarm Optimization (PSO) has shown its fast search speed in many complicated optimization and search problems. However, PSO could often easily fall into local optima because the particles could quickly get closer to the best particle. At such situations, the best particle could hardly be improved. This paper proposes a new hybrid PSO (HPSO) to solve this problem by adding a Cauchy mutation on the best particle so that the mutated best particle could lead all the rest of particles to the better positions. Experimental results on many well-known benchmark optimization problems have shown that HPSO could successfully deal with those difficult multimodal functions while maintaining fast search speed on those simple unimodal functions in the function optimization. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/SIS.2007.367959 | SIS |
Keywords | Field | DocType |
search problem,genetic programming,computer science,particle swarm,testing,random number generation,geology,optimization problem,evolutionary computation,particle swarm optimization,genetic algorithms | Particle swarm optimization,Mathematical optimization,Local optimum,Evolutionary computation,Multi-swarm optimization,Genetic programming,Search problem,Optimization problem,Mathematics,Genetic algorithm | Conference |
Volume | Issue | ISSN |
null | null | null |
Citations | PageRank | References |
41 | 2.16 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Wang | 1 | 386 | 27.33 |
| Yong Liu | 2 | 2526 | 265.08 |
| Changhe Li | 3 | 1044 | 43.37 |
| Sanyou Zeng | 4 | 394 | 42.60 |