A Novel Hybrid Optimization Algorithm Based on ACO and GA and its Application

In allusion to the complementary of ant colony optimization (ACO) algorithm and genetic algorithm (GA), this paper proposes a novel hybrid ant colony genetic (NHACG) algorithm with recent patents based on integrating multi-population strategy and collaborative strategy. The solutions of the ACO algorithm is regarded as the initial population of the GA, and the ACO algorithm and GA are dynamically applied according to the objective function in the NHACG algorithm. When the population evolutionary is close to the stagnation, the ACO algorithm is applied. And the collaborative strategy is used to dynamically balance the global search ability and local search ability, and improve the convergence speed. In order to illuminate the validity of the NHACG algorithm in solving the complex optimization problems, some traveling salesman problems (TSP) are selected to test the effectiveness of the NHACG algorithm. The experimental results show that the proposed NHACG algorithm can obtain the global and local search ability, avoid the phenomena of the prematurity and effectively search for the optimum solutions.