一种动态分群带熵权的粒子群优化方法

为提高粒子群优化的求解性能，提出了一种动态分群带熵权的粒子群优化求解方法．该方法采用k的均值聚类获得子群总数，在子群粗搜索过程中充分利用其他粒子的熵信息，采用子群及其他子群搜索的最优解信息构建熵权以调整惯性权重，利用自身群粒子经过m次迭代时的优化信息构建熵权以调整本群的全局最优值．在子群精搜索过程中，利用各子群获得的最优解信息作为新群的初始设置，利用其他粒子的迭代信息构建熵权来调整全局最优值．采用传统的粒子群优化算法、其他文献中的方法以及新提出的方法分别对4个经典的测试函数进行对比实验，从获得解的最优值、平均值、标准差以及平均迭代数作对比，从而验证了该方法具有求解精度高以及优化求解迭代次数少等优点．

Particle swarm optimization method based on dynamic sub-swarms with entropy weight

To improve the performance of particle swarm optimization, a particle swarm optimization method based on dynamic sub-swarms with entropy weight is proposed. The method of k-means is applied to obtain the number of subgroups, and during the course searching, to utilize the entropy information about other particles, optimal solution information from the subgroup searching process and those from other subgroups are used to form the entropy weight so as to adjust the inertia weight, and the entropy weight is formed by optimization information of m times iterations to adjust the global optimization solution of the particle swarm. During the fine searching, optimization information obtained from each particle swarm is used as the initial setting of the new swarm, and the iteration information about other particles is used to from the entropy weight to adjust the global optimization solution. Some traditional methods and the proposed method in this paper are compared with four classical test functions, and the results show that the method proposed in the paper has advantages of high precision and fewer iterations.