An Ant Colony Optimization Algorithm Based on the Nearest Neighbor Node Choosing Rules and the Crossover Operator

The ant colony optimization algorithm (ACO) has a powerful capacity to find out solutions to combinatorial optimization problems, but it still has two defects, namely, it is slow in convergence speed and is prone to falling in the local optimal solution. Against the deficiencies of this algorithm, in this study we proposed an ACO based on basic ACO algorithm based on well-distributed on the initiation, the nearest neighbor node choosing rules and with crossover operator. In the initiation of the algorithm, the convergence speed of the ACO is increased by distributing the ant colony evenly in all the cities and adopting the nearest neighbor choosing node rule and making crossover computation among better individual ants at the end of each round of cycle when each ant chooses the next city. The experiment results indicate that the ACO proposed in this study is valid.