Multi-objective Ant Colony Optimization Algorithm for Shortest Route Problem

A novel Multi-objective Ant Colony Optimization algorithm for shortest route problem (MACO) is proposed. Firstly, the pheromone on every path segment is initialized to an initial value and ants are randomly distributed among cities. Secondly, self-adaptive operator is used, namely in prophase we use higher probability to explore more search space and to collect useful global information; otherwise in anaphase we use higher probability to accelerate convergence. MACO algorithm adopts self-adaptive operator to make the search scope reduced in anaphase, thus the search time of this algorithm is reduced greatly. Real shortest route results demonstrate the superiority of MACO in this paper.