Cloud adaptive particle swarm optimizer based on variation of expansion method
Wei Xing-qiong · Jisuanji gongcheng yu sheji · 2009
A cloud adaptive particle swarm optimizer algorithm based on the expansion of variability is put forward. The algorithm using cloud model X-generator adaptive adjustment of inertia value of every particles. Variation expansion methods used to avoid multi-dimensional and multi-variable interference which cause by many factors, its purpose is to further improve the performance of PSO, and it provides an effective optimization method to high-dimensional space. Finally, take the optimizer high-dimensional functions as an example, the computer simulation results show that the algorithm has the characteristics of high robust, fast convergence and high accuracy.