Journal of System Simulation
Abstract
Abstract: To address the issues of susceptibility to local optima and slow convergence in mobile robot path planning within complex terrain scenarios, an enhanced artificial gorilla troops optimizer with integration of quadratic interpolation and elite individual genetic strategies (QGGTO) is proposed. The algorithm integrates quadratic interpolation and elite individual genetic strategies to promote information exchange among candidate solutions, thereby accelerating convergence, while maintaining population diversity to avoid local optima. For complex terrains containing both regular and irregular obstacles, a cost function that comprehensively considers walking distance, safety, and turning angles is constructed to uniformly evaluate the path planning performance of the algorithm. Simulation experiments demonstrate that QGGTO overall optimization performance surpassing GTO and six other competitive algorithms. QGGTO can assist robots in planning paths closest to the global optimum in four complex obstacle environments, validating its effectiveness in practical applications.
Recommended Citation
Ye, Chen; Shao, Peng; Zhang, Shaoping; Li, Wenting; and Zhou, Tengming
(2025)
"Enhanced Artificial Gorilla Algorithm for Mobile Robot Path Planning,"
Journal of System Simulation: Vol. 37:
Iss.
6, Article 12.
DOI: 10.16182/j.issn1004731x.joss.24-0163
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss6/12
First Page
1474
Last Page
1485
CLC
TP391.9
Recommended Citation
Ye Chen, Shao Peng, Zhang Shaoping, et al. Enhanced Artificial Gorilla Algorithm for Mobile Robot Path Planning[J]. Journal of System Simulation, 2025, 37(6): 1474-1485.
DOI
10.16182/j.issn1004731x.joss.24-0163
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