基于扩张变异方法的云自适应粒子群算法

Cloud adaptive particle swarm optimizer based on variation of expansion method

提出了一种基于扩张变异方法的云自适应粒子群算法,该算法利用云模型X条件云发生器自适应调整每一个粒子个体惯性权值。采用扩张变异方法进行变异,可避免因多维而多变量引起多因素的干扰,加快搜索速度,其目的进一步改进粒子群算法的性能,为解决高维空间优化问题提供一种有效方法。最后,以高维函数优化为实例,计算机仿真结果表明,给出的算法具有鲁棒性强、收敛速度快、精度高等特点。

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.