摘要
标准的粒子群算法引入惯性权重w,成为一种有效寻找函数极值的计算方法,且简单易行收敛速度快。目前普遍采用的是线性递减动态惯性权重策略,但其存在着复杂的非线性搜索过程。在线性递减策略的基础上,提出了非线性递减动态惯性权重策略,采用Griewank、Rastrigrin、Sphere、J.D.Schaffer 4个标准测试函数进行了仿真实验,与基本粒子群算法中惯性权重取固定值、线性递减LDIW和指数曲线非线性递减进行了比较。实验结果表明改进的非线性权值递减策略无论从收敛速度、收敛精度还是迭代次数都明显优于其他算法。
The standard particle group algorithm introduces inertial weight W,which has become an effective calculation method for finding the maximum value of functions,and it is simple and easy to achieve rapid convergence.Now it is generally used as a linear decreasing dynamic inertial weight strategy.Although this improvement is very successful,however,the search process is a nonlinear complex process.A nonlinear decreasing dynamic inertial weight strategy based on the linear diminishing strategy was proposed.The simulation experiments were carried out using the four standard test functions of Griewank,Rastrigrin,Sphere,and J.D.Schaffer,and the fixed weight,linear decrease LDIW and exponential cure nonlinear decline were taken with the inertial weight in the elementary particle group algorithm.The experimental results show that the improved nonlinear weight reduction strategy is superior to other algorithms in terms of convergence speed,convergence precision and iteration times.
作者
郭丽丽
刘勇
王卫西
GUO Li-Li;LIU Yong;WANG Wei-Xi(School of Electronic Engineering,Heilongjiang University,Harbin 150080,China)
出处
《黑龙江大学工程学报》
2019年第1期67-71,共5页
Journal of Engineering of Heilongjiang University
基金
国家自然科学基金资助项目(61501176)
黑龙江省自然科学基金资助项目(F2018025)
关键词
粒子群算法
惯性权重
线性递减
非线性递减
particle swarm algorithm
inertial weight
linear decrease
nonlinear decrease
