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A hybrid strategy-based GJO algorithm for robot path planning
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AI
DOI：10.1016/j.eswa.2023.121975.png)
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Addressing the challenges of low convergence accuracy , stagnation at local optima in the application of the golden jackal optimizer (GJO) to mobile robot path planning, this paper proposes a hybrid strategy-based golden jackal optimizer (HGJO) algorithm. The improved algorithm employs a pre-decreasing slow nonlinear energy decay strategy to balance the global and local search capabilities. The roulette wheel selection algorithm and Levy flight strategy are introduced into the position update of the GJO algorithm, so the proposed algorithm avoids stagnation at the local optimum. The HGJO algorithm is evaluated against some state-of-the-art optimizers on 23 benchmark functions and the CEC2021 benchmark function. It is also applied to ablation experiments for mobile robot path planning. The experimental results show that the HGJO algorithm improves the average path length in path planning by 0.21%, 82.4% , 7.9% over the original algorithm in three different environments under 30 independent experiments.
Keyword:
Golden jackal optimization
Sand cat swarm optimization
Roulette wheel selection
Levy flight strategy
Path planning
Mobile robot
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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