A novel ant colony system based on minimum 1-tree and hybrid mutation for TSP

Authors:
Chao-Xue Wang;Du-Wu Cui;Zhu-Rong Wang;Duo Chen
Affiliations:
School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China;School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China;School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China;School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Abstract

By applying a candidate set strategy based on minimum 1-tree and a self-adaptive hybrid mutation operator to the ant colony system, a novel ant colony system for TSP (MMACS) is proposed. Under the condition that all the edges in the global optimal tour are nearly all contained in the candidate sets, the candidate set strategy based on minimum 1-tree can limit the selection scope of ants at each step to six cities and thus substantially reduce the size of search space. Meanwhile, the self-adaptive hybrid mutation operator that consists of inversion mutation, insertion mutation and swap mutation can effectively prevent MMACS from being trapped in local optimal areas. The simulation of TSP shows that MMACS can avoid the premature convergence phenomenon effectively while greatly increasing the convergence speed. Although MMACS takes TSP as an example for explaining its mechanism, its ideas can be used for other related algorithms.