Research on navigation of bidirectional A* algorithm based on ant colony algorithm


To overcome the lengthy search time, massive space occupation, and overlong planned path of the traditional A* algorithm, this paper integrates the bidirectional search with the intelligent ant colony algorithm to obtain the heuristic function selection factor, and uses the factor to improve the evaluation function of the algorithm. The simulation results show that the improved algorithm achieved better dynamic navigation than the traditional A* algorithm both in search time and distance, featuring shorter path searching time and the algorithm running time. Therefore, the result of this research has effectively reduced the search time and enhanced the dynamic search.

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The work described in this paper was supported by Special Fund for Science and Technology Innovation Cultivation for College Students in Guangdong Province. The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through Research Group No. RG- 1441-331.

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Correspondence to Yu-qiang Chen.

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Chen, Y., Guo, J., Yang, H. et al. Research on navigation of bidirectional A* algorithm based on ant colony algorithm. J Supercomput (2020).

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  • Heuristic function
  • A* algorithm
  • Path planning
  • Bidirectional search
  • Ant colony algorithm (ACA)