Improved ant colony optimization algorithm by path crossover for optimal path planning

In this paper, an improved Ant Colony Optimization (ACO) algorithm is proposed to solve path planning problems. These problems are to find a collision-free and optimal path from a start point to a goal point in environment of known obstacles. There are many ACO algorithms for path planning. However, it take a lot of time to get the solution and it is not to easy to obtain the optimal path every time. It is also difficult to apply to the complex and big size maps. Therefore, we study to solve these problems using the ACO algorithm improved by the path crossover scheme. The path crossover scheme is two-point crossover paths found by ants. The best path is stored and is compared with new path every time. The path crossover scheme is used at this time. When the two parts compared and exchanged, the better part updates the best path. We also propose that the pheromone update rule is modified as compared with previous our paper.