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A Biogeography-Based Optimization with a Greedy Randomized Adaptive Search Procedure and the 2-Opt Algorithm for the Traveling Salesman Problem

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  • Cheng-Hsiung Tsai

    (Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan)

  • Yu-Da Lin

    (Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Magong 88046, Taiwan)

  • Cheng-Hong Yang

    (Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
    Department of Business Administration, Tainan University of Technology, Tainan 71002, Taiwan
    Ph.D. Program in Biomedical Engineering, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
    School of Dentistry, Kaohsiung Medical University, Kaohsiung 80708, Taiwan)

  • Chien-Kun Wang

    (Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan)

  • Li-Chun Chiang

    (Department of Political Science, National Cheng Kung University, Tainan 701401, Taiwan)

  • Po-Jui Chiang

    (Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan)

Abstract

We develop a novel method to improve biogeography-based optimization (BBO) for solving the traveling salesman problem (TSP). The improved method is comprised of a greedy randomized adaptive search procedure, the 2-opt algorithm, and G2BBO. The G2BBO formulation is derived and the process flowchart is shown in this article. For solving TSP, G2BBO effectively avoids the local minimum problem and accelerates convergence by optimizing the initial values. To demonstrate, we adopt three public datasets (eil51, eil76, and kroa100) from TSPLIB and compare them with various well-known algorithms. The results of G2BBO as well as the other algorithms perform close enough to the optimal solutions in eil51 and eil76 where simple TSP coordinates are considered. In the case of kroa100, with more complicated coordinates, G2BBO shows greater performance over other methods.

Suggested Citation

  • Cheng-Hsiung Tsai & Yu-Da Lin & Cheng-Hong Yang & Chien-Kun Wang & Li-Chun Chiang & Po-Jui Chiang, 2023. "A Biogeography-Based Optimization with a Greedy Randomized Adaptive Search Procedure and the 2-Opt Algorithm for the Traveling Salesman Problem," Sustainability, MDPI, vol. 15(6), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5111-:d:1096569
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    References listed on IDEAS

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    5. Hadidi, Amin, 2015. "A robust approach for optimal design of plate fin heat exchangers using biogeography based optimization (BBO) algorithm," Applied Energy, Elsevier, vol. 150(C), pages 196-210.
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