Improved Ant Colony Optimization Algorithm by Potential Field Concept 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 algorithm 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 potential field scheme. We also propose that control parameters of the ACO algorithm are changed to converge into the optimal solution rapidly when a certain number of iterations have been reached. To improve the performance of ACO algorithm, we use a ranking selection method for pheromone update. In the simulation, we apply the proposed ACO algorithm to general path planning problems. At the last, we compare the performance with the conventional ACO algorithm.