动态学习混沌映射的粒子群算法

传统粒子群优化算法（PSO）对社会认知部分与自我认知部分都采用恒定学习常数，一定程度上限制种群全局协调能力。在算法收敛后期种群多样性丧失而导致全部个体收敛于搜索空间中的某一点，这易诱发早熟现象。针对这种缺陷提出一种动态学习混沌映射的粒子群优化算法（VLCMPSO）。在算法初期迭代中应多考虑自身记录的最佳点，在算法后期应快速向种群最佳点收敛，因而设计一种进行协调的动态学习因子。为克服早熟现象，判断种群多样性方差低于设定阈值时，以混沌映射的方式将该代最优个体位置更新且以新的方式进行优化操作。经实验证明新算法在收敛速度与精度上都具有更好的性能。

Chaotic mapping particle swarm optimization algorithm based on variable learning factors

The traditional particle swarm optimization algorithm uses constant learning constants for social and self -cognition to limit the population ''s global coordination ability. In the late convergence of the algorithm, the diversity of the population is lost and all the individuals converge to one point in search space, which can trigger the precocious convergence. In view of this defect, this paper proposed a chaotic map particle swarm optimization algorithm based on variable learning factor. In the early stage of the algorithm, the emphases should focus on the best location of self-recording. At the later period of the algorithm, it should design a coordinated dynamic learning factor to converge on the best position of population. In order to overcome the premature phenomenon and determine the variance of population diversity below the set value, using chaotic mapping updated the optimal individual location of the generation and utilizing a new way to optimized. The experimental show the new algorithm has better performance in convergence speed and precision.