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An ant colony optimization algorithm with adaptive greedy strategy to optimize path problems

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Abstract

Path planning problems have attracted the interest of more and more researchers due to its widespread existence in recent years. For example, express delivery and food delivery, courier often need to get to their destinations as quickly as possible under the constraints of time, economy etc. Therefore, the path planning issue under various constraints becomes more challenging. In order to solve these problems better, a new ant colony optimization algorithm based on adaptive greedy strategy (GSACO) is proposed this paper. In the process of continuous iteration, the control parameters of the algorithm are constantly adjusted and changed, which can expand the diversity of the population. In the process of ant colony search, the preference degree of ant colony is continuously changed by the greedy strategy, then the ant colony continuously explores the places with high pheromone concentration, and finally the convergence speed of the algorithm is accelerated. The experimental results show that GSACO algorithm has better performance than traditional heuristic algorithm and other evolutionary-based methods. The GSACO algorithm proposed in this paper can effectively accelerate the convergence speed, obtain the global optimal solution faster, and better solve the path planning problem under various constraints.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61903089 and 62066019), the JiangXi Provincial Natural Science Foundation (Grant Nos. 20202BABL202020 and 20202BAB202014) and the Science Foundation of JiangXi University of Science and Technology (Grant No. JXXJBS18059).

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China

    Ying Huang

  2. School of Information Engineering, JiangXi University of Science and Technology, Ganzhou, China

    Wei Li, Le Xia & Soroosh Mahmoodi

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  1. Wei Li
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  2. Le Xia
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  3. Ying Huang
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  4. Soroosh Mahmoodi
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Correspondence to Ying Huang.

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Li, W., Xia, L., Huang, Y. et al. An ant colony optimization algorithm with adaptive greedy strategy to optimize path problems. J Ambient Intell Human Comput 13, 1557–1571 (2022). https://doi.org/10.1007/s12652-021-03120-0

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