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Imperial competitive algorithm with policy learning for the traveling salesman problem

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Abstract

The traveling salesman problem (TSP) is one of the most studied combinatorial optimization problems. In this paper, we present the new idea of combining the imperial competitive algorithm with a policy-learning function for solving the TSP problems. All offspring of each country are defined as representing feasible solutions for the TSP. All countries can grow increasingly strong by learning the effective policies of strong countries. Weak countries will generate increasingly excellent offspring by learning the policies of strong countries while retaining the characteristics of their own country. Imitating these policies will enable the weak countries to produce improved offspring; the solutions generated will, therefore, acquire a favorable scheme while maintaining diversity. Finally, experimental results for TSP instances from the TSP library have shown that our proposed algorithm can determine the salesman’s tour with more effective performance levels than other known methods.

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Authors and Affiliations

  1. Department of Information Management, Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Chung-Li, Taiwan

    Meng-Hui Chen & Pei-Chann Chang

  2. Department of Information Management, Cheng Shiu University, Kaohsiung, Taiwan

    Shih-Hsin Chen

Authors
  1. Meng-Hui Chen
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  2. Shih-Hsin Chen
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  3. Pei-Chann Chang
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Correspondence to Pei-Chann Chang.

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There is no conflict of interest with this research as known by the authors.

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Communicated by V. Loia.

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Chen, MH., Chen, SH. & Chang, PC. Imperial competitive algorithm with policy learning for the traveling salesman problem. Soft Comput 21, 1863–1875 (2017). https://doi.org/10.1007/s00500-015-1886-z

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