基于相似度的改进粒子群优化算法

针对粒子群算法易于过早收敛的不足，通过引入粒子间新的相似度的概念来度量粒子群的多样性程度，并用自适应变化阈值手段来控制调整粒子群算法的收敛速度，使其缓缓趋向于全局最优，在粒子群算法迭代过程中以相似度为基础，通过高斯等噪声扰动来重新调整粒子的位置从而避免算法陷入局部最优，从而得到了一种PSO算法的改进算法，实验和性能分析表明，新算法可以有效提高算法的全局搜索能力，并有效回避收敛早熟问题。

Improved particle swarm optimization algorithm based on similarity

The biggest flaw of PSO（Particle Swarm Optimization） is easy to premature convergence, although some improved PSO algorithms can increase the ability of convergence, but they can not fundamentally solve the problem of premature convergence. In this paper, a new concept of similarity between particles is proposed to measure the degree of the diversity of particle swarm, and for the purpose of gaining a gradual progress of global optimal solution, adaptive thresholds are used to control the adjustment of convergence rate of particle swarm algorithm. In each iteration stage, Gaussian noise and other disturbances based on the similarity are also used to readjust the position of the particle in order to avoid the particle plunging local optimum. Experimental results and theoretical discussion show that new algorithm can effectively improve the globally searching ability of PSO, and effectively avoid the premature convergence.