Journal of System Simulation
Abstract
Abstract: Aiming at the long paths and slow convergence speed of GWO algorithm in robot path planning, a hybrid PSO-GWO algorithm based on PSO algorithm and the improved GWO algorithm is proposed. By running PSO algorithm for many times, the initial wolf group size and initial fitness value are determined. A nonlinear convergence factor is introduced to balance the exploration and development capabilities of GWO algorithm, and a dynamic inertia weight factor is proposed to ensure the leadership system of alpha wolf and to promote the population communication. Levy flight and greedy strategy are used to effectively avoid the local optima and obtain the optimal solution. Simulation experiments show that the average path length of the proposed algorithm is reduced by 17%, 16% and 16.2% compared with the GWO algorithm under three maps. The average running time is reduced by 13%, 8%, 16%.
Recommended Citation
Cao, Menglong; Zhao, Wenbin; and Chen, Zhiqiang
(2023)
"Robot Path Planning by Fusing Particle Swarm Algorithm and Improved Grey Wolf Algorithm,"
Journal of System Simulation: Vol. 35:
Iss.
8, Article 13.
DOI: 10.16182/j.issn1004731x.joss.22-0407
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss8/13
First Page
1768
Last Page
1775
CLC
TP391.9
Recommended Citation
Cao Menglong, Zhao Wenbin, Chen Zhiqiang. Robot Path Planning by Fusing Particle Swarm Algorithm and Improved Grey Wolf Algorithm[J]. Journal of System Simulation, 2023, 35(8): 1768-1775.
DOI
10.16182/j.issn1004731x.joss.22-0407
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