一种克服局部最优的收缩因子PSO算法

收缩因子粒子群优化算法容易陷入局部最优并出现早熟收敛的现象。为此，提出一种改进的收缩因子粒子群优化算法。该算法引入速度因子和位置因子参数，若粒子向全局最优接近且速度小于设定的速度因子，则认为该粒子可能出现停滞，从而对该粒子进行初始化，以增强粒子活力。在算法陷入局部最优时，通过该方法驱散粒子以提高种群多样性，避免产生早熟收敛现象。对多峰标准测试函数进行仿真实验，结果表明，该算法能提高收敛精度，有效避免算法陷入局部最优。

Constriction Factor Particle Swarm Optimization Algorithm with Overcoming Local Optimum

The constriction factor Particle Swarm Optimization（PSO） algorithm is easily trapped in the local optimum and appeared premature convergence.An improved constriction factor PSO algorithm is proposed to overcome the local optimum.The improved algorithm introduces a position factor and speed factor parameter,if all particles are close to the global optimum and its velocity is less than the speed factor that it is set in advance,these particles are likely to be stagnant,a re-initialization is done to enhance these particles＇ energy.When the algorithm is in local optimum,this method can disperse the particles and improve the diversity of the population and avoid premature convergence.Comparing the three multimodal functions＇ simulation,the data can verify the performance of the improved algorithm,simulation result shows that the improved algorithm improves its convergence accuracy,and effectively avoids falling into local optimum.