Logistic动态粒子群优化算法的改进及分析

A Logistic Dynamic Particle Swarm Optimization Algorithm Based on Mutation and Hybrid Optimization

分析了Kennedy最新提出的高斯动态粒子群优化算法(GDPSO)的寻优模式,针对GDPSO的特点,结合粒子群优化算法的新寻优模式,提出了Logistic动态粒子群优化算法(LDPSO);并基于LDPSO和GDPSO的特性,设计了LDPSO算法的两种改进策略——混合优化策略和最优粒子变异策略,混合优化策略用以提高收敛速度,最优粒子变异策略用以保持群体多样性,避免算法陷入局部最优。实验结果显示了LDPSO及其改进算法的有效性。

The optimization strategy of Gaussian dynamic particle swarm optimization （GDPSO） is analyzed in detail. Considering the characteristic of GDPSO,Logistic dynamic particle swarm optimization （LDPSO） is developed based on the new optimization strategy. Two improved strategies are designed, hybrid optimization Strategy and mutation strategy of best particle. Hybrid optimization strategy is adopted to improve the convergence speed ,and mutation strategy of best particle is employed to maintain the diversity of population. Experimental simulation results demonstrate that LDPSO and its improved version with two improved strategies can solve complex optimization problems efficiently.