In this paper, an adaptive improved ant colony algorithm based on population information entropy(AIACSE) is proposed to improve the optimization ability of the algorithm. The diversity of the population in the iterative process is described by the information entropy. The non-uniform distribution initial pheromone is constructed to reduce the blindness of the search at the starting phase. The pheromone diffusion model is used to enhance the exploration and collaboration capacity between ants. The adaptive parameter adjusting strategy and the novel pheromone updating mechanism based on the evolutionary characteristics of the population are designed to achieve a better balance between exploration of the search space and exploitation of the knowledge during the optimization progress. The performance of AIACSE is evaluated on the path planning of mobile robots. Friedman's test is further conducted to check the significant difference in performance between AIACSE and the other selected algorithms. The experimental results and statistical tests demonstrate that the presented approach significantly improves the performance of the ant colony system (ACS) and outperforms the other algorithms used in the experiments.INDEX TERMS Ant colony optimization, path planning, mobile robot, grid map, pheromone diffusion model, parameter adjusting strategy, pheromone updating strategy, population information entropy.

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Path planning of mobile robots in complex environments is the most challenging research. A hybrid approach combining the enhanced ant colony system with the local optimization algorithm based on path geometric features, called EACSPGO, has been presented in this study for mobile robot path planning. Firstly, the simplified model of pheromone diffusion, the pheromone initialization strategy of unequal allocation, and the adaptive pheromone update mechanism have been simultaneously introduced to enhance the classical ant colony algorithm, thus providing a significant improvement in the computation efficiency and the quality of the solutions. A local optimization method based on path geometric features has been designed to further optimize the initial path and achieve a good convergence rate. Finally, the performance and advantages of the proposed approach have been verified by a series of tests in the mobile robot path planning. The simulation results demonstrate that the presented EACSPGO approach provides better solutions, adaptability, stability, and faster convergence rate compared to the other tested optimization algorithms.

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