[1]李二超,高振磊.改进粒子速度和位置更新公式的粒子群优化算法[J].南京师大学报(自然科学版),2022,45(01):118-126.[doi:10.3969/j.issn.1001-4616.2022.01.017]
　Li Erchao,Gao Zhenlei.Particle Swarm Optimization Algorithm With Improved Particle Velocity and Position Update Formula[J].Journal of Nanjing Normal University(Natural Science Edition),2022,45(01):118-126.[doi:10.3969/j.issn.1001-4616.2022.01.017]
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改进粒子速度和位置更新公式的粒子群优化算法()
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《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
第45卷
期数:
2022年01期
页码:
118-126
栏目:
·计算机科学与技术·
出版日期:
2022-03-15

文章信息/Info

Title:
Particle Swarm Optimization Algorithm With Improved Particle Velocity and Position Update Formula
文章编号:
1001-4616(2022)01-0118-09
作者:
李二超; 高振磊
(兰州理工大学电气工程与信息工程学院,甘肃 兰州 730050)
Author(s):
Li Erchao; Gao Zhenlei
(College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China)
关键词:
粒子群优化; 自适应; Logistic混沌; 收敛速度; 寻优精度
Keywords:
particle swarm optimization; adaptive; Logistic chaos; convergence rate; optimization precision
分类号:
TP273
DOI:
10.3969/j.issn.1001-4616.2022.01.017
文献标志码:
A
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.
Abstract:
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.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2021-08-31.
基金项目:国家自然科学基金项目(61763026).
通讯作者:李二超,博士,教授,博士生导师,研究方向:多目标优化、人工智能、进化计算. E-mail:lecstarr@163.com
更新日期/Last Update: 1900-01-01