Soft Computing

, Volume 21, Issue 7, pp 1863–1875

Imperial competitive algorithm with policy learning for the traveling salesman problem

Methodologies and Application

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 

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Meng-Hui Chen
    • 1
  • Shih-Hsin Chen
    • 2
  • Pei-Chann Chang
    • 1
  1. 1.Department of Information Management, Innovation Center for Big Data and Digital ConvergenceYuan Ze UniversityChung-LiTaiwan
  2. 2.Department of Information ManagementCheng Shiu UniversityKaohsiungTaiwan

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