摘要
针对粒子群优化算法中出现的对大规模问题的搜索失败,分析了粒子群优化算法的收敛性,指出了粒子速度与搜索失败的关系,提出了一种根据速度信息自适应调整参数的粒子群优化算法.在满足收敛性的条件下,该算法能使粒子根据理想速度自适应调整参数进行搜索.实验结果表明,该算法能解决基本粒子群算法在求解高维、多峰等复杂非线性优化问题时出现的易陷入局部最优和不收敛等搜索失败问题.
According to the search failure for large-scale problem via the particle swarm optimization algorithm, the convergence of particle swarm optimization algorithm is analyzed and the relationship between the particle velocity and the search failure is pointed out. Then, an adaptive parameter-adjusting particle swarm optimization algorithm according to the velocity information is put forward. Under the convergent conditions, the proposed algorithm can perform the search by adaptively adjusting the parameters according to the ideal velocity. Experimental results indicate that the proposed algorithm avoids the local optimization and divergence commonly occurred in the conventional particle swarm optimization algorithm in multi-dimension and multi-peak conditions.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2008年第9期6-10,共5页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金重大项目(10472034
10590351)
关键词
粒子群优化算法
自适应性
平均速度
particle swarm optimization algorithm
adaptability
average velocity
