A self-adaptive control strategy of population size for ant colony optimization algorithms

Ant colony optimization (ACO) algorithms often have a lower search efficiency for solving travelling salesman problems (TSPs). According to this shortcoming, this paper proposes a universal self-adaptive control strategy of population size for ACO algorithms. By decreasing the number of ants dynamically based on the optimal solutions obtained from each interaction, the computational efficiency of ACO algorithms can be improved dramatically. Moreover, the proposed strategy can be easily combined with various ACO algorithms because it's independent of operation details. Two well-known ACO algorithms, i.e., ant colony system (ACS) and max-min ant system (MMAS), are used to estimate the performance of our proposed strategy. Some experiments in both synthetic and benchmark datasets show that the proposed strategy reduces the computational cost under the condition of finding the same approximate solutions.