| Autor: |
Liu, Yanmin, Luo, Dongshen, Zhao, Qingzhen, Sui, Changling |
| Zdroj: |
Advanced Intelligent Computing Theories & Applications; 2010, p595-601, 7p |
| Abstrakt: |
PSO may easily get trapped in a local optimum, when solving complex multimodal problems. In this paper, an improved PSO based on small world network and comprehensive is proposed. The learning exemplar of each particle includes three parts: the global best particle, its own best particle (pbest) and the pbest of its neighborhood. And a random position around itself is needed to increase a probability to jump to that promising region. These strategies enable the diversity of the swarm to be preserved to discourage premature convergence. In benchmark function test, the SCPSO algorithm achieves better solutions than other PSOs. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
