一种融合优化选择策略的差分粒子群算法

doi:10.19665/j.issn1001-2400.2022.02.025

西安电子科技大学学报 ›› 2022, Vol. 49 ›› Issue (2): 218-227.doi: 10.19665/j.issn1001-2400.2022.02.025

• 计算机科学与技术 & 网络空间安全 • 上一篇下一篇

一种融合优化选择策略的差分粒子群算法

张德华¹(),郝昕源¹(),张妮娜²(),魏倩¹(),刘英¹()

1.河南大学计算机与信息工程学院,河南开封 475004
2.武警综保中心运维室,陕西西安 711700

收稿日期:2020-08-31 出版日期:2022-04-20 发布日期:2022-05-31
通讯作者: 魏倩
作者简介:张德华(1984—),男,副教授,E-mail: dhuazhang@vip.henu.edu.cn;|郝昕源(1998—),男,河南大学硕士研究生,E-mail: 879506070@qq.com;|张妮娜(1982—),女,工程师,硕士,E-mail: 109776574@qq.com;|刘英(1994—),女,河南大学硕士研究生,E-mail: 271212507@qq.com
基金资助:
国家自然科学基金(61771006);国家自然科学基金(62001359);河南省科技厅科技攻关项目(19210221025);河南省高等学校重点研究计划(20A120005);河南大学一流学科培育项目(2018YLTD04)

PSO-DE algorithm based on the optimal selection strategy

ZHANG Dehua¹(),HAO Xinyuan¹(),ZHANG Nina²(),WEI Qian¹(),LIU Ying¹()

1. School of Computer and Information Engineering,Henan University,Kaifeng 475004,China
2. Operation and Maintenance Room of a Comprehensive Security Center of Certain Armed Police Unit,Xi’an 711700,China

Received:2020-08-31 Online:2022-04-20 Published:2022-05-31
Contact: Qian WEI

摘要/Abstract

摘要：

针对粒子群算法进化后期种群多样性减少以及差分粒子群算法出现信息交流误差的问题,提出了一种融合优化选择策略的差分粒子群算法。首先构建了一个用于计算系统偏差的权值网络;其次,引入优化选择策略机制,使用适应度函数作为评价标准;最后,利用系统偏差估计值配准目标传感器量测。在种群多样性和适应度测试中,这种算法种群多样性更加丰富,最优个体适应度值为2.019 4×10^-5;在非机动目标与机动目标测试中,这种算法在大约2 s之后就迅速收敛于真实偏差值附近,收敛时长最短为201.8 s,均方根误差值降低了10%以上。实验结果表明,该算法不仅有效地提高了种群的多样性,而且收敛速度和精度也有了较大的提升。

关键词: 系统偏差配准, 权值网络, 优化选择策略, 差分粒子群算法

Abstract:

Aiming at the problems of the population diversity reduction in the late evolution of particle swarm optimization (PSO),and the information exchange error of PSO-DE,this paper presents a PSO-DE algorithm based on the optimal selection strategy.First,a weighted network (WN) is constructed to calculate the systematic biases.Then the optimal selection strategy mechanism is introduced,and the fitness function is constructed as an evaluation criterion.Finally,the systematic deviation estimate is used to register the target sensor measurement.In the test of population diversity and fitness,the algorithm proposed in the paper has a richer population diversity,and the optimal fitness value of the individual is 2.0194×10^-5.In the experiments on non-maneuvering and maneuvering targets,the deviation value rapidly converges to the true deviation value after about 2s,with the shortest convergence time being 201.8s,and the RMS error value is reduced by more than 10 times.Simulation results show that the algorithm not only increases the population diversity,but also improves the convergence speed and the accuracy.

Key words: systematic biases registration, weight networks, optimal selection strategy, differential evolution particle swarm optimization algorithm

中图分类号:

TP212

张德华,郝昕源,张妮娜,魏倩,刘英. 一种融合优化选择策略的差分粒子群算法[J]. 西安电子科技大学学报, 2022, 49(2): 218-227.

ZHANG Dehua,HAO Xinyuan,ZHANG Nina,WEI Qian,LIU Ying. PSO-DE algorithm based on the optimal selection strategy[J]. Journal of Xidian University, 2022, 49(2): 218-227.

图/表 18

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表1

仿真参数设置"

名称	符号	数值
速度边界	v_max/v_min	0.8/-0.8
位置边界	w_max/w_min	4/-4
PSO、DE种群规模大小	N	20
各层端点个数		2、8、2
网络层数		3
第一层转移函数	$T f 1$	Tanh(·)
结束层转移函数	$T f 0$	Purelin(·)
最大进化代数	G_max	50
最大仿真步长	K	100
目标误差值	y_z	10^-6

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参考文献 14

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一种融合优化选择策略的差分粒子群算法

PSO-DE algorithm based on the optimal selection strategy

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