摘要
针对粒子群优化算法求解精度低、局部搜索能力差、进化后期收敛速度慢等问题,本文提出一种改进粒子速度和位置更新公式的粒子群优化算法(particle swarm optimization algorithm with improved particle velocity and position update formula,IPSO-VP).IPSO-VP算法提出一种自适应粒子速度和位置更新策略,采用基于Logistic混沌呈非线性变化的惯性权重,以此来加快算法的收敛速度、平衡算法的全局和局部搜索能力、提高收敛精度.最后将本文所提算法与6个改进粒子群算法在12个测试函数上进行寻优比较,结果表明,本文所提算法在收敛速度和寻优精度方面均优于其他6种改进算法.
Particle swarm optimization algorithm with improved particle velocity and position update formula(IPSO-VP)is proposed to solve the problems of low solution precision,poor local search ability and slow convergence rate in the later stage of evolution.IPSO-VP algorithm proposes an adaptive particle velocity and position update strategy,which adopts the inertia weight based on Logistic chaos,which is nonlinear,to accelerate the convergence rate,balance the global and local search ability of the algorithm,and improve the convergence precision.Finally,the proposed algorithm is compared with six improved particle swarm optimization algorithms on twelve test functions.Simulation results show that the proposed algorithm is superior to the other six improved particle swarm optimization algorithms in terms of convergence rate and optimization precision.
作者
李二超
高振磊
Li Erchao;Gao Zhenlei(College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China)
出处
《南京师大学报（自然科学版）》
CAS
CSCD
北大核心
2022年第1期118-126,共9页
Journal of Nanjing Normal University(Natural Science Edition)
基金
国家自然科学基金项目(61763026).
