带反向预测及斥力因子的改进粒子群优化算法

Improved particle swarm optimization algorithm with reverse forecast and repulsion

针对传统粒子群算法在求解高维空间中复杂多峰函数时容易陷入局部最优的问题,提出带反向预测和斥力因子的改进粒子群优化算法.算法通过引入反向预测因子改进速度更新方式,以降低粒子在运动过程中产生惰性而出现早熟收敛的概率,并给出带斥力因子的位置修正策略,使粒子均匀分散于搜索空间,从而避免陷入局部最优.实验分析表明,在对高维空间中复杂多峰函数进行优化求解时,改进的粒子群优化算法较传统粒子群算法更加优越.

For the complex multi-peaks function with high dimension, the improved particle swarm optimization algorithm with reverse forecast and repulsion（RFRPSO） is proposed on the basis of analyzing the problem of premature. Firstly, this method improves the speed renewal way by introducing the reverse forecast factor in order to decreas the probability of premature convergence. Furthermore, the repulsion factor is introduced to make the swarm even distribution in search space, which can avoid local optimum. Finally, experimental results show that the performance of RFRPSO algorithm is superior to the traditional PSO algorithm.