摘要
针对现有算法中初始种群随机性强、局部搜索能力差、移动公式效率低等问题,提出了一种改进的类电磁机制算法.结合反向学习理论,引入带扰动因子的反向学习机制构造初始种群;提出了一种双混沌优化机制用于局部搜索;运用改进后的公式计算粒子之间的合力;设计了一种自适应移动算子来更新粒子.实验结果表明,改进后的算法具有更好的收敛效果和更高的求解精度.
An improved Electromagnetism-like mechanism algorithm is proposed to overcome the drawbacks of the original EM algorithm,such as strong randomness of the initial population,low ability for local search and low efficiency in the movement according to the total force.The new algorithm generates the initial population with the disturbance factor and opposite learning mechanism,improves the local search algorithm with the double chaotic search method,and calculates the total force between particles with the modified equation.Besides,the algorithm is used to design an adaptive move operator for updating the locations of those particles. Experimental results indicate that the proposed algorithm has a better convergence effect and a higher solution accuracy.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2014年第5期79-83,147,共6页
Journal of Xidian University
基金
国家部委基础科研计划资助项目(A1120132007)
关键词
类电磁机制算法
反向学习机制
扰动
双混沌
全局优化
electromagnetism-like mechanism algorithm
opposite learning mechanism
disturbance
dual chaotic search
global optimization
