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Global path planning approach based on ant colony optimization algorithm

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Abstract

Ant colony optimization (ACO) algorithm was modified to optimize the global path. In order to simulate the real ant colonies, according to the foraging behavior of ant colonies and the characteristic of food, conceptions of neighboring area and smell area were presented. The former can ensure the diversity of paths and the latter ensures that each ant can reach the goal. Then the whole path was divided into three parts and ACO was used to search the second part path. When the three parts pathes were adjusted, the final path was found. The valid path and invalid path were defined to ensure the path valid. Finally, the strategies of the pheromone search were applied to search the optimum path. However, when only the pheromone was used to search the optimum path, ACO converges easily. In order to avoid this premature convergence, combining pheromone search and random search, a hybrid ant colony algorithm(HACO) was used to find the optimum path. The comparison between ACO and HACO shows that HACO can be used to find the shortest path.

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Wen Zhi-qiang Doctoral candidate  (文志强) & Cai Zi-xing  (蔡自兴)

  2. Department of Computer Science and Technology, Hunan University of Technology, Zhuzhou, 412008, China

    Wen Zhi-qiang Doctoral candidate  (文志强)

Authors
  1. Wen Zhi-qiang Doctoral candidate  (文志强)
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  2. Cai Zi-xing  (蔡自兴)
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Correspondence to Wen Zhi-qiang Doctoral candidate  (文志强).

Additional information

Foundation item: Projects(60234030, 60404021) supported by the National Natural Science Foundation of China

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Wen, Zq., Cai, Zx. Global path planning approach based on ant colony optimization algorithm. J Cent. South Univ. Technol. 13, 707–712 (2006). https://doi.org/10.1007/s11771-006-0018-4

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  • DOI: https://doi.org/10.1007/s11771-006-0018-4

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