Name
Abstract | ||
|---|---|---|
Particle Swarm Optimization (PSO) shares many similarities with evolutionary algorithms (EAs), while the standard PSO does not use any evolution operators such as crossover and mutation. This paper presents a hybrid PSO algorithm to inherit some excellent characteristics of advanced evolutionary computation techniques. The proposed method employs a novel multi-parent crossover operator and a self-adaptive Cauchy mutation operator to help escape from local optima. Experimental results on a suit of well-known benchmark functions with many local minima have shown that the proposed method could successfully deal with those difficult multimodal optimization problems. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICNC.2008.643 | ICNC |
Keywords | Field | DocType |
particle swarm optimization,self-adaptive cauchy mutation operator,evolution operator,local optimum,advanced evolutionary computation technique,evolutionary algorithm,hybrid pso algorithm,novel multi-parent crossover operator,standard pso,local minimum,evolutionary computing,optimization,local minima,optimization problem,evolutionary computation,benchmark testing,convergence,evolutionary algorithms,mathematical model | Particle swarm optimization,Mathematical optimization,Crossover,Evolutionary algorithm,Local optimum,Computer science,Evolutionary computation,Multi-swarm optimization,Artificial intelligence,Optimization problem,Genetic algorithm,Machine learning | Conference |
Citations | PageRank | References |
7 | 0.48 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Wang | 1 | 386 | 27.33 |
| Zhijian Wu | 2 | 313 | 21.20 |
| Yong Liu | 3 | 2526 | 265.08 |
| Sanyou Zeng | 4 | 394 | 42.60 |