A Slime Mold Fractional-Order Ant Colony Optimization Algorithm for Travelling Salesman Problems

Ziheng Rong, Xiaoling Gong, Xiangyu Wang, Wei Lv, Jian Wang

Abstract: In this paper, a novel algorithm of slime mold fractional-order ant colony optimization (SMFACO) for travelling salesman problems (TSPs) is proposed. The newly developed algorithm, SMFACO, takes full use of the long-term memory characteristics of the fractional calculus to balance exploration and exploitation. In addition, it considers the property of the slime mold model, which retains the critical path to avoid trapping into the local optima. To evaluate the performance of the SMFACO, we conduct comprehensive experiments on various data sets. According to the experimental results, the proposed algorithm outperforms its peer algorithms on solution quality, search efficiency and convergence speed.