Ant Colony Algorithm with n-α-Measure and Migration Learning.

To address the problem that the ant colony algorithm in solving Traveling Salesman Problem (TSP) has a slow convergence speed and easily falls into local optimum, an Ant Colony Algorithm with n- α -measure and Migration Learning is proposed. Firstly, the n- α -measure is proposed to map the least squares to the distance matrix of TSP nodes for improving the connection between nodes; second, multiple parameters in the ant colony algorithm are extended in multiple dimensions to improve the control of the algorithm over the pheromone; finally, a communication channel is established between multiple populations, and a transfer learning mechanism is proposed to exchange information. The simulation experiments of multiple cases in TSPLIB show that the improved algorithm balances diversity and the convergence speed, and effectively improves the quality of the solution. [ABSTRACT FROM AUTHOR]