Robot Path Planning by Fusing Particle Swarm Algorithm and Improved Grey Wolf Algorithm

doi:10.16182/j.issn1004731x.joss.22-0407

Abstract

Menglong Cao, Wenbin Zhao, Zhiqiang Chen. Robot Path Planning by Fusing Particle Swarm Algorithm and Improved Grey Wolf Algorithm[J]. Journal of System Simulation, 2023, 35(8): 1768-1775.

Figures/Tables 13

Fig. 1

Fig. 2

Fig. 3

Table 1

Algorithm parameters

算法	参数设置
GWO	种群规模设置为50，最大迭代次数为500， $a$ 从2线性减小到0
HGWO	种群规模设置为50，最大迭代次数为500， $a$ 从2线性减小到0
PSO-GWO	种群规模设置为50，最大迭代次数为500， $k$ =5， $a$ 从2非线性减小到0，粒子群算法参数：学习因子c ₁=15，c ₂=15，惯性权重 $ω$ =9.8
A*算法	启发式函数F=G+H，最大迭代次数为500

Table 1

Fig. 4

Table 2

Table 3

Fig. 5

Table 4

Table 5

Fig. 6

Table 6

Table 7

References 16

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算法	初始路径长度	最优路径长度	最优值迭代次数	转折次数
GWO	1 500	1 048	376	12
HGWO	1 423	948	356	8
A*	–	976	350	13
PSO-GWO	1 412	875	308	5

算法	最优路径长度	最劣路径长度	平均路径长度	平均迭代次数	平均运行时间/s
GWO	1 065	1 125	1 089	362	13.2
HGWO	952	1 032	976	346	11.5
A*	965	1 042	965	345	12.9
PSO-GWO	904	1 105	904	332	10.6

算法	初始路径长度	最优路径长度	最优值迭代次数	转折次数
GWO	–	–	–	–
HGWO	2 200	1 600	448	17
A*	–	1 495	476	20
PSO-GWO	1 400	1 200	435	10

算法	最优路径长度	最劣路径长度	平均路径长度	平均迭代次数	平均运行时间/s
GWO	–	–	–	–	–
HGWO	1 532	1 602	1 596	476	15.3
A*	1 482	1 528	1 425	480	19.2
PSO-GWO	1 135	1 403	1 356	423	14.1

算法	初始路径长度	最终路径长度	最优值迭代次数	转折次数
GWO	1 375	985	464	7
HGWO	1 295	954	374	10
A*	–	978	458	20
PSO-GWO	1 156	932	332	6

Robot Path Planning by Fusing Particle Swarm Algorithm and Improved Grey Wolf Algorithm

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算法	最优路径长度	最劣路径长度	平均路径长度	平均迭代次数	平均运行时间/s
GWO	985	1 298	1 089	468	17.5
HGWO	954	1 135	1 024	432	15.2
A*	978	1 168	1 045	475	18.1
PSO-GWO	895	1 156	923	415	13.1

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