基于遗传因子优化粒子群算法

刘欢　郝俊　李文娜　陈雅梦

摘 要：針对粒子群在搜索最优值时间过长、易陷入局部最优、无法挑选出最优值的问题，本文在粒子群算法的基础上，结合遗传算法中的变异因子，提出一种基于遗传优化粒子群的算法。首先，该算法采用对数函数递减惯性策略加速粒子跳出局部最优，其次，遗传变异因子增加个体极值的多样性来寻出最佳值;最后，基于一定的迭代次数，根据标准函数Rastrigin进行寻优效果测试验证，仿真结果表明，改进后的算法能够避免进入局部最优情况，并且在最佳适应度、标准差和寻优时长等性能指标优越于其他算法。

关键词：粒子群算法 遗传因子 适应度函数

Optimizing Particle Swarm Optimization Algorithm based on Genetic Factors

Liu Huan，Sean Sean，Li Wenna，Chen Yameng

Abstract：Aiming at the problem that the particle swarm is too long to search for the optimal value， it is easy to fall into the local optimal， and the optimal value cannot be selected. Based on the particle swarm algorithm and the mutation factor in the genetic algorithm， this paper proposes a method based on genetic optimization of particle swarm algorithm. First， the algorithm uses the logarithmic function decreasing inertia strategy to accelerate the particles out of the local optimum. Secondly， the genetic variation factor increases the diversity of individual extreme values to find the best value; finally， based on a certain number of iterations， it is performed according to the standard function Rastrigin. The optimization results are tested and verified， and the simulation results show that the improved algorithm can avoid entering the local optimal situation， and is superior to other algorithms in performance indicators such as the best fitness， standard deviation， and optimization time.

Key words：particle swarm optimization， genetic factor， fitness function