Research on path planning based on improved double layer (ordinary and elite) ant colony algorithm

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Algorithm optimization improves path quality and smoothness in complex environments, indicating its effectiveness.

The TLA-ACO algorithm reduces path length by 7.24% in a 30 × 30 grid compared to traditional methods.
Key optimizations were made using pheromone volatility and a novel hybrid search strategy.
The proposed method includes a dynamic pheromone update model for better global search capabilities.
Post-processing combined with Bézier curve fitting enhances geometric rationality and path smoothness.

Yang et al. (2025) studied this question.

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View Full Paper

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Authors

Algorithm optimization improves path quality and smoothness in complex environments, indicating its effectiveness.

The TLA-ACO algorithm reduces path length by 7.24% in a 30 × 30 grid compared to traditional methods.
Key optimizations were made using pheromone volatility and a novel hybrid search strategy.
The proposed method includes a dynamic pheromone update model for better global search capabilities.
Post-processing combined with Bézier curve fitting enhances geometric rationality and path smoothness.

Yang et al. (2025) studied this question.

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Bookmark