A fast Ant Colony Optimization for traveling salesman problem

In this paper, we present an efficient method for speeding up Ant Colony Optimization (ACO), called Pattern Reduction Enhanced Ant Colony Optimization (PREACO). The proposed algorithm is motivated by the observation that many of the computations of ACO on its convergence process are essentially redundant and thus can be eliminated to reduce its computation time. To evaluate the performance of the proposed algorithm, we use it to solve the the traveling salesman problem (TSP). Moreover, we compare the proposed algorithm with several state-of-the-art ACO-based algorithms. Our simulation results indicate that the proposed algorithm can reduce the computation time of ACO algorithms we evaluated up to 99.21% or by a factor of 126.58 while limiting the degradation of the quality of the solution to a very small percentage compared to ACO algorithms themselves.