Abstract
A research about the path planning problem has been popular topic nowadays and some effective algorithms have been developed to solve this kind of problem. However, the existing algorithms to solve the path problem can only find a single optimal path, cannot satisfactorily find multiple groups of optimal solutions at the same time, and it is very necessary to propose as many solutions as possible. So this paper carries out a research on the Multi-modal Multi-Objective Path Planning (MMOPP), the objective is to find all sets of Pareto optimal path solutions from the start point to the end point in a grid map. This paper proposes a multi-modal multi-objective ant colony path planning optimization algorithm based on matrix preprocessing technology and Dijkstra algorithm (MD-ACO). Firstly, a new method of storing maps that reduces the size of the map and reduces the size of the decision space has been proposed in this paper. Secondly, using the characteristics of the Dijkstra algorithm that can quickly find the optimal path, generate an initial feasible solution about the problem, and improve the problem that the initial pheromone of ant colony algorithm is insufficient and searching for solutions is slow. Thirdly, a reasonable threshold is set for the pheromone to avoid algorithm getting stuck in local optimal solution. Finally, the algorithm is tested on the MMOPP test sets to evaluate the performance of the algorithm, and the results show that MD-ACO algorithm can solve MMOPP and get the optimal solution set.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (Grant No. 62176191 and 62272355).
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Jing, J., Zhang, L., Shen, C., Zhang, K. (2023). Research on Multi-modal Multi-objective Path Planning by Improved Ant Colony Algorithm. In: Pan, L., Zhao, D., Li, L., Lin, J. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2022. Communications in Computer and Information Science, vol 1801. Springer, Singapore. https://doi.org/10.1007/978-981-99-1549-1_2
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