A hybridized parallel bats algorithm for combinatorial problem of traveling salesman

Trong The Nguyen, Yu Qiao, Jeng Shyang Pan, Shu Chuan Chu, Kuo Chi Chang, Xingsi Xue, Thi Kien Dao

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


Metaheuristic algorithms have been applied widely for real-world problems in many fields, e.g., engineering, financial, healthcare. Bats algorithm (BA) is a recent metaheuristic algorithm with considering as a robust optimization method that can outperform existing algorithms. However, when dealing with complicated combinatorial problems such as traveling salesman problems (TSP), the BA can be fallen in a local optimum. This paper proposes a new hybridizing Parallel BA (HPBA) with a mutation in local-search to escape such its drawback scenario for TSP. A graph theory mutation method is used to embed for hybridizing BA with exploiting similarities among individuals. The proposed method is extensively evaluated in TSP with series instances of the benchmark from TSPLIB to test its performance. The compared experimental result with the previous method and the best-known solutions (B.K.S) in the literature shows that the proposed approach offers competitive results.

Original languageEnglish
Pages (from-to)5811-5820
Number of pages10
JournalJournal of Intelligent and Fuzzy Systems
Issue number5
Publication statusPublished - 29 May 2020
Externally publishedYes


  • hybridized parallel bats algorithm
  • metaheuristic algorithms
  • Transportation applications


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