Imperial competitive algorithm with policy learning for the traveling salesman problem Methodologies and Application First Online: 01 October 2015 DOI :
10.1007/s00500-015-1886-z

Cite this article as: Chen, MH., Chen, SH. & Chang, PC. Soft Comput (2017) 21: 1863. doi:10.1007/s00500-015-1886-z
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.

Keywords Traveling salesman problem Imperial competitive algorithm Combinatorial optimization problems Artificial chromosomes Genetic algorithm

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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 2. Department of Information Management Cheng Shiu University Kaohsiung Taiwan