Evolutionary algorithm to traveling salesman problems

Yen Far Liao, Dun Han Yau, Chieh Li Chen

Research output: Contribution to journalArticlepeer-review

51 Citations (Scopus)


This paper proposed an improved version of the Particle Swarm Optimization (PSO) approach to solve Traveling Salesman Problems (TSP). This evolutionary algorithm includes two phases. The first phase includes Fuzzy C-Means clustering, a rule-based route permutation, a random swap strategy and a cluster merge procedure. This approach firstly generates an initial non-crossing route, such that the TSP can be solved more efficiently by the proposed PSO algorithm. The use of sub-cluster also reduces the complexity and achieves better performance for problems with a large number of cities. The proposed Genetic-based PSO procedure is then applied to solve the TSP with better efficiency in the second phase. The proposed Genetic-based PSO procedure is applied to TSPs with better efficiency. Fixed runtime performance was used to demonstrate the efficiency of the proposed algorithm for the cases with a large number of cities.

Original languageEnglish
Pages (from-to)788-797
Number of pages10
JournalComputers and Mathematics with Applications
Issue number5
Publication statusPublished - 2012 Sept

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Computational Theory and Mathematics
  • Computational Mathematics


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