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An Improved A ∗ Algorithm Based on Simulated Annealing and Multidistance Heuristic Function
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OA
AI
DOI：10.1155/int/5979509.png)
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Abstract
En 中文
The traditional A (& lowast;) algorithm has problems such as low search speed and huge expansion nodes, resulting in low algorithm efficiency. This article proposes a circular arc distance calculation method in the heuristic function, which combines the Euclidean distance and the Manhattan distance as radius, uses a deviation distance as the correction, and assignes dynamic weights to the combined distance to make the overall heuristic function cost close to reality. Furthermore, the repulsive potential field function and turning cost are introduced into the heuristic function, to consider the relative position of obstacles while minimizing turns in the path. In order to reduce the comparison of nodes with similar cost values, the bounded suboptimal method is used, and the idea of simulated annealing is introduced to overcome the local optima trapped by node expansion. Simulation experiments show that the average running time of the improved algorithm has decreased by about 70%, the number of extended nodes has decreased by 92%, and the path has also been shortened, proving the effectiveness of the algorithm improvement.
Keywords:
A(& lowast
) algorithm
arc distance
Floyd algorithm
heuristic function
path planning
repulsive potential field
simulated annealing
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