Name
Abstract | ||
|---|---|---|
The search direction and the search step size are two important factors which affect the performance of algorithms. In this paper, we combine Particle Swarm Optimization (PSO) with EP to form two new algorithms namely PSOEP and SAVPSO. The basic idea is to introduce the search direction to the mutation operator of EP and use lognormal self-adaptive strategy to control the velocity of PSO to guide the individual at a faster convergence rate. All of these algorithms are compared to each other with respect to the similarities and differences of their basic components, as well as their performances on seven benchmark problems. Our experimental results show that PSOEP performs much better than all other version of EPs, and SAVPSO performs much better than PSO for the benchmark functions. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-16493-4_21 | ISICA (1) |
Keywords | Field | DocType |
particle swarm optimization,important factor,basic idea,search direction,hybrid evolutionary algorithms design,search step size,benchmark function,basic component,convergence rate,benchmark problem | Particle swarm optimization,Incremental heuristic search,Mathematical optimization,Evolutionary algorithm,Computer science,Rate of convergence,Mutation operator | Conference |
Volume | ISSN | ISBN |
6382 | 0302-9743 | 3-642-16492-7 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lin GM | 1 | 1048 | 90.67 |
| Sundong Liu | 2 | 1 | 0.69 |
| Fei Tang | 3 | 0 | 0.34 |
| Huijie Wang | 4 | 0 | 0.34 |