Improved Ant Colony Genetic Algorithm for Solving Traveling Salesman Problem

Abstract

A new improved ant colony genetic algorithm(IACGA) is proposed to solve the problems of the basic ant colony optimization algorithm(ACO), such as the slow convergence rate and the local optimal solution. Based on the ACO model, local pheromone increment is updated differently according to the path quality constructed by ants, the current optimal path pheromone residual factor is adjusted adaptively, pheromone updating rules are improved, genetic operator is embedded and genetic algorithm(GA) is dynamically fused. It makes full use of the positive feedback mechanism of ACO and the searching ability of GA to realize the dynamic fusion of ACO and GA. The experimental results show that compared with the ACO and the GA, the improved algorithm can find the global optimal solution quickly, and the solution quality is relatively good, which improves the efficiency of the ant colony algorithm in solving the traveling salesman problem(TSP).