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Particle Swarm Optimization Based on Boltzmann Learning Strategy


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Particle Swarm Optimization Based on Boltzmann Learning Strategy
AI Jie-qing12GAO Ji3
1.Information Center;2.Software Testing Lab,Guangdong Power Grid Corporation,Guangzhou 510000,China; 3.Institute of Artificial Intelligence,Zhejiang University,Hangzhou 310000,China
partial swarm optimization Boltzmann learning strategy simulated annealing global optimization multimodal problems
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.


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Last Update: 2012-10-12