A New Hybrid Ant Colony Optimization Algorithm for the Traveling Salesman Problem

Authors:
Xiaoxia Zhang;Lixin Tang
Affiliations:
The Logistics Institute, Northeastern University, Shenyang, China;The Logistics Institute, Northeastern University, Shenyang, China
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Year:
2008

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Abstract

This paper presents a novel hybrid ant colony optimization approach (ACO&PR) to solve the traveling salesman problem (TSP). The main feature of this hybrid algorithm is to hybridize the solution construction mechanism of the ACO with path relinking (PR), an evolutionary method, which introduces progressively attributes of the guiding solution into the initial solution to obtain the high quality solution as quickly as possible. Moreover, the hybrid algorithm considers both solution diversification and solution quality, and it adopts the dynamic updating strategy of the reference set and the criterion function restricting the frequencies of using the path-relinking procedure to accelerate the convergence towards high-quality regions of the search space. Finally, the experimental results for benchmark TSP instances have shown that our proposed method is very efficient and competitive to solve the traveling salesman problem compared with the best existing methods in terms of solution quality.