南京理工大学学报（自然科学版）2012，Vol.36Issue(3)：402-407,6.
基于Boltzmann学习策略的粒子群算法
Particle Swarm Optimization Based On Boltzmann Learning Strategy
艾解清 1高济2
作者信息
- 1. 广东电网公司信息中心广东广州510000
- 2. 广东电网公司信息化评测实验室,广东广州510000
- 折叠
摘要
Abstract
An improved particle swarm optimization ( PSO) based on Boltzmann learning strategy (BLSPSO)is proposed to overcome the problem of premature convergence and easily getting into local extremum of the the standard PSO. Using the idea of the simulated annealing algorithm for reference, the Boltzmann learning strategy is introduced into the standard PSO. In the prophase of BLSPSO, the particles can study different extreme points. The diversities of the particles are preserved to improve the BLSPSO's global optimization ability. In the anaphase of BLSPSO, the particle tends to study the global best information. The convergence velocity is improved, and the stability of the algorithm is ensured. The simulation results show that the BLSPSO has powerful optimizing ability, higher search veracity. It can avoid premature convergence effectively and have good performance in solving multimodal problems compared with other PSO algorithms.关键词
粒子群算法/Boltzmann学习策略/模拟退火/全局寻优/多极值问题Key words
partial swarm optimization/ Boltzmann learning strategy/ simulated annealing/ global optimization/ multimodal problems分类
信息技术与安全科学引用本文复制引用
艾解清,高济..基于Boltzmann学习策略的粒子群算法[J].南京理工大学学报（自然科学版）,2012,36(3):402-407,6.
