PSO算法的稳定性分析及算法改进

种群多样性的缺失是导致PSO算法易陷入早熟早收敛的重要原因，因此对基于线性定常离散系统的PSO算法的稳定性作了理论分析，并分析了种群多样性缺失的原因，根据此特性提出了一种·贯r}权重因子在一定范围内随机取值且学习因子取恒定常数的改进Pso算法，该算法可以使粒子速度具有一定的概率发散，以保持种群的多样性。通过对3个约束优化问题的仿真实验表明，该算法跳出局部极值的概率很大，可有效地避免早熟早收敛。

Stability Analysis of Particle Swarm Optimization Algorithm and its Improved Algorithm

The loss of population diversity is an important reason which leads to the premature convergence of the PSO algorithm Therefore, the stability of the PSO algorithm based on the linear time-invariant discrete system was analyzed theoretically and the possible reasons of the lack of the population diversity were discussed in this paper. Based on the stability of the algorithm, an improved PSO algorithm was presented in which the inertia weight factor value is got randourly within a certain range and the leaning factor value is a constant. In the algorithm the population diversity can be maintained by the character that the particle speed has certain probability. The simulation experiments of three constraint optimization problems show that the algorithm has great probability to jump out of local extremum,and avoids the precocious premature convergence effectively.