一种带停滞信息的自适应粒子群优化方法

Adaptive particle swarm optimization method with stagnancy information

为了提高粒子群优化算法的性能,设计了优化粒子带停滞信息的年龄结构网,并利用这种年龄结构网信息自适应地更改粒子群优化算法的3个关键参数.构建了一种带停滞信息的自适应粒子群优化方法,给出了该方法的具体优化步骤.采用4个经典的低维及高维Benchmark测试函数验证该优化方法的求解性能,并同引力搜索算法以及传统的不带停滞信息的粒子群优化算法进行求解对比.通过对比可知,该方法在低维多峰函数优化时,其搜索效率均2倍于其他文献中的方法,对于维数高于2维的高维函数,该方法的优化效率同其他文献中的方法基本相同,但在获得全局解及局部解的能力以及所求解的精度方面均远高于其他文献中的方法.

To improve the performance of the particle swarm optimization algorithm, the optimal network of the particle age structure with stagnation information is designed, and the information about this network is used to adaptively change the three key parameters of the particle swarm optimization algorithm. At the same time, an adaptive particle swarm optimization method with stagnancy information is proposed and specific optimization steps of this method are given. Four classical low and high dimension benchmark test functions are used to validate the performance of the optimization method, and a comparison study is made with gravitational search algorithm and the traditional particle swarm optimization algorithm without stagnancy information. The comparison study shows that the search efficiency of the proposed method is 2 times higher than that of other methods in the literature in the case of low dimensional multimodal functions. When the dimension of functions is higher than 2, the search efficiency of the proposed method is almost the same as that of other methods, but with the better ability to achieve global solution and local solutions, and the higher solving precision.