ACO-AFSA Algorithm in Function Optimization and Its Application

Abstract

ACO and AFSA as two kinds of new intelligent bionic algorithm, has better robustness, and the design method is simple and easy to understand. At present, in many engineering fields, they have been a pivotal position, but there are some defects in solving function optimization problems. Because of the lack of pheromone in the initial stage of ACO, the speed of the solution is relatively slow, and AFSA has the advantages of strong global convergence ability and fast speed. In this paper, based on the advantages and disadvantages of ACO and AFSA, two kinds of algorithms are integrated, and the advantage is enhanced, a better optimization algorithm is put forward: ACO-AFSA. The initial solution and improved state transition probabilities are obtained by AFSA, and the basic ACO is improved, the congestion degree of AFSA is introduced in the algorithm, makes most of ants’ initial optimization will not be randomly selected at a strong crowding restrictions. Experimental results show that the new algorithm can get the optimal solution with less number of iterations, which greatly saves computation time, and has higher accuracy and better convergence performance. Engineering, computing the shortest distance between the surface and plane of the problem has also been good results, the experimental results are satisfactory.