Dynamic Path Planning Based on an Improved Ant Colony Optimization with Genetic Algorithm

Abstract

A study by using Adaptable Ant Colony Optimization Algorithm (AACO) to solve the path arrangement problem has been given in [1]. This algorithm can determine the priority of visit for different attractions, by using travel time and the distance between two attractions to determine the optimal path arrangement for visitors. Though, the probability function seems to be determined by some exponential function with fixed powers $\alpha$ and $\beta$, this study tries to apply GA (Genetic algorithm) to find optimal $\alpha$ and $\beta$ in each determination of the probability, and we name the new algorithm as Improved Ant Colony Optimization Algorithm (IAACO). The numerical outputs of IAACO show that it outperforms AACO and ACO by the total number of desire values and solid points, that means the rendered spots are among the most wantto-visit places. The variation of the parameters $\alpha$ and $\beta$ also shows that some best probability functions can be rendered by IAACO compared to the AACO and ACO (the original Ant Colony Optimization Algorithm) with fixed parameters only. It shows when the number of attractions are many, the performance of IAACO shows better results. A selectable choice of places is possible and their rendered route is illustrated in Google Map for users’ reference.