摘要
针对粒子群算法局部搜索能力差,后期收敛速度慢等缺点,提出了一种改进的粒子群算法,该算法是在粒子群算法后期加入拟牛顿方法,充分发挥了粒子群算法的全局搜索性和拟牛顿法的局部精细搜索性,从而克服了粒子群算法的不足,把超越方程转化为函数优化的问题,利用该算法求解,数值实验结果表明,算法有较高的收敛速度和求解精度。
An improved particle swarm optimization algorithm is proposed for particle swarm optimization algorithm shortcomings which are poor local searching ability, slow con- vergence speed in the latter, and so on. This algorithm is added quasi-Newton method in the late of particle swarm optimization algorithm. It has displayed sufficiently the characteristics of particle swarm optimization algorithm＇ s group search and quasi-Newton method＇ s local strong search. At the same time, it overcomes the disadvantages of particle swarm optimiza- tion algorithm. Transcendental equations axe converted to function optimization problems which are solved using this algorithm. Numerical results show that the algorithm has a high convergence speed and solution precision.
出处
《数学的实践与认识》
CSCD
北大核心
2014年第22期172-176,共5页
Mathematics in Practice and Theory
基金
山西省高等学校科技创新项目(2013158)
关键词
粒子群算法
拟牛顿法
超越方程
优化
particle swarm
optimization algorithm
Quasi-Newton method
transcendental equation
optimizatio
