Research on an Improved Ant Colony Algorithm Fusion with Genetic Algorithm for Route Planning

Abstract

This article focuses on an improved ant colony algorithm for solving the problem of route planning. An ant colony algorithm (ACO) fusion with genetic algorithm (GA) is proposed and verified by simulations. The genetic algorithm is used to calculate the initial pheromone according to the route nodes distribution. The output of GA is considered as prior information fed to ACO algorithm by selecting, crossing and variation evolution operations. Comparing with the ACO and GA routing algorithms individually under the condition of 10,20,40 nodes distribution within the region of interest based on MATLAB simulation, the fusion ACO-GA algorithm achieves the shortest route and fitness evolution results. Experimental tests have indicated that with the interior nodes of the interesting region increasing, the route distance obtained by the improved algorithm is shorter than GA and ACO algorithms both. Moreover, the proposed ant colony algorithm fusion with GA has fewer iterations to obtain the optimal solution, and the calculation time cost is decreased obviously.