加速收敛的粒子群优化算法

Accelerate convergence particle swarm optimization algorithm

在基本粒子群优化算法的理论分析的基础上,提出一种加速收敛的粒子群优化算法,并从理论上证明了该算法的快速收敛性,同时对该算法中的参数进行了优化.为了防止其在快速收敛的同时陷入局部最优,采用依赖部分最差粒子信息的变异操作.最后通过与其他几种经典粒子群优化算法的性能比较,表明了该算法的高效和稳健,且明显优于现有的几种经典的粒子群算法.

An accelerate convergence particle swarm optimization（ACPSO） algorithm is proposed based on analyzing the convergence of basal particle swarm optimization（BPSO） algorithm.The convergence speed of ACPSO algorithm is very quickly through theoretical analysis.Then the parameters in this algorithm are optimized.The mutation operator of depending on segmental worst particles’ information is shown to escape the local optimal.The performance of ACPSO algorithm with the optimal parameters is tested on several classical functions by comparing with four classical PSO algorithms.The experimental results show that the ACPSO algorithm is efficient and robust.Especially,the convergence speed of ACPSO is superior to several classical PSO algorithms obviously.