A New Fast Ant Colony Optimization Algorithm: The Saltatory Evolution Ant Colony Optimization Algorithm

Various studies have shown that the ant colony optimization (ACO) algorithm has a good performance in approximating complex combinatorial problems such as traveling salesman problem (TSP) for real-world applications. However, disadvantages long running time and easy stagnation still restrict its further wide application many fields. In this study, saltatory evolution (SEACO) is proposed to increase speed. Different from past research, study innovatively starts perspective of near-optimal path identification refines domain knowledge by quantitative analysis model using pheromone matrix data traditional ACO algorithm. Based on knowledge, prediction built predict evolutionary trend so fundamentally save time. Extensive experiment results library (TSPLIB) database demonstrate solution quality SEACO better than algorithm, it more suitable large-scale sets within specified window. This means can provide promising direction deal with about slow speed low accuracy