Algorithms 2014, 7(2), 229-242; doi:10.3390/a7020229
Article

Application of Imperialist Competitive Algorithm on Solving the Traveling Salesman Problem

1,2email, 1,2,* email and 1,2email
Received: 9 March 2014; in revised form: 5 May 2014 / Accepted: 5 May 2014 / Published: 13 May 2014
(This article belongs to the Special Issue Bio-inspired Algorithms for Combinatorial Problems)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: The imperialist competitive algorithm (ICA) is a new heuristic algorithm proposed for continuous optimization problems. The research about its application on solving the traveling salesman problem (TSP) is still very limited. Aiming to explore its ability on solving TSP, we present a discrete imperialist competitive algorithm in this paper. The proposed algorithm modifies the original rules of the assimilation and introduces the 2-opt algorithm into the revolution process. To examine its performance, we tested the proposed algorithm on 10 small-scale and 2 large-scale standard benchmark instances from the TSPLIB and compared the experimental results with that obtained by two other ICA-based algorithms and six other existing algorithms. The proposed algorithm shows excellent performance in the experiments and comparisons.
Keywords: discrete imperialist competitive algorithm; traveling salesman problem; 2-opt algorithm; numerical experiments
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MDPI and ACS Style

Xu, S.; Wang, Y.; Huang, A. Application of Imperialist Competitive Algorithm on Solving the Traveling Salesman Problem. Algorithms 2014, 7, 229-242.

AMA Style

Xu S, Wang Y, Huang A. Application of Imperialist Competitive Algorithm on Solving the Traveling Salesman Problem. Algorithms. 2014; 7(2):229-242.

Chicago/Turabian Style

Xu, Shuhui; Wang, Yong; Huang, Aiqin. 2014. "Application of Imperialist Competitive Algorithm on Solving the Traveling Salesman Problem." Algorithms 7, no. 2: 229-242.

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