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A similarity-based mechanism to control genetic algorithm and local search hybridization to solve traveling salesman problem

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Abstract

A big shortcoming of the simple genetic algorithm is that whenever it converges to a local optimum, its performance is continuously deteriorating, especially in the highly nonlinear problems such as traveling salesman problem (TSP). Therefore, some heuristics such as local search are needed to help genetic algorithm (GA) loops escaping such situations. The critical point in such hybridization is the determining a suitable time for applying local search to the GA population. In this study, a new hybridization of GA and local search based on a new similarity-based control mechanism is proposed, and its behavior on different TSP instances is compared with simple GA. The experimental results show that the proposed hybrid algorithm yields the optimal tour length in most of the cases, especially in the TSP instances with higher complexity.

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References

  1. Baraglia R, Hidalgo JI, Perego R (2001) A hybrid heuristic for the traveling salesman problem. IEEE Trans Evol Comput 5:613–622

    Article  Google Scholar 

  2. Banzhaf W (1990) The “molecular” traveling salesman. Biol Cybern 64:7–14

    Article  MATH  Google Scholar 

  3. Chang PC, Huang WH, Ting CJ (2010) Dynamic diversity control in genetic algorithm for mining unsearched solution space in TSP problems. Expert Syst Appl 37:1863–1878

    Article  Google Scholar 

  4. Changdar C, Mahapatra GS, Pal RK (2014) An efficient genetic algorithm for multi-objective solid travelling salesman problem under fuzziness. Swarm Evol Comput 15:27–37

    Article  Google Scholar 

  5. Davendra D (2010) Traveling salesman problem, theory and applications. InTech, Croatia

  6. Eisen M, Spellman P, Brown P, Botstein D (1998) Cluster analysis and display of genomewide expression data. Proc Nat Acad Sci USA 95:14863–14868

    Article  Google Scholar 

  7. Fox MS, McMahon MB (1987) Genetic operators for sequencing problems, foundations of genetic algorithms, First Workshop on the Foundations of Genetic Algorithms and Classifier Systems, Los Altos, pp. 284–300, 1987

  8. Goldberg DE, Lingle DE (1985) Alleles, Loci and the TSP. Proceedings of the First International Conference on Genetic Algorithms and Their Applications, pp. 154–159

  9. Grefenstette JJ, Gopal R, Rosmaita BJ, Gucht DV (1985) Genetic algorithms for the traveling salesman problem, Proceedings of the 1st International Conference on Genetic Algorithms, pp. 160–168

  10. Grötschel M, Jünger M, Reinelt G (1991) Optimal control of plotting and drilling machines: a case study. Math Methods Oper Res 35(1):61–84

    Article  MATH  Google Scholar 

  11. Larranaga P, Kuijpers CMH, Murga RH, Inza I, Dizdarevic S (1999) Genetic algorithms for the travelling salesman problem: a review of representations and operators. Artif Intell Rev 13:129–170

    Article  Google Scholar 

  12. Ledesma JR, Gonzalez JJS (2012) Solving school bus routing using the multiple vehicle traveling purchaser problem: a branch-and-cut approach. Comput Oper Res 39(2):391–404

    Article  MathSciNet  MATH  Google Scholar 

  13. Li J, Sun Q, Zhou M, Dai X (2013) A new multiple traveling salesman problem and its genetic algorithm-based solution, 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 627–632

  14. Lidd ML (1991) The travelling salesman problem domain application of a fundamentally new approach to utilizing genetic algorithms. Technical Report, MITRE Corporation

  15. Lin S (1965) Computer solutions of the traveling salesman problem. Bell Syst Tech J 44(10):2245–2269

    Article  MATH  Google Scholar 

  16. Michalewicz Z (1994) Genetic Algorithms + Data Structures = Evolution Program. Springer, Berlin

    Google Scholar 

  17. Nguyen HV, Bai L (2010) Cosine similarity metric learning for face verification, 10th Asian Conference on Computer Vision, Queenstown, New Zealand, pp. 709–720

  18. Park J, Kim BI (2010) The school bus routing problem: a review. Eur J Oper Res 202(2):311–319

    Article  MATH  Google Scholar 

  19. Ratliff HD, Rosenthal AS (1983) Order-picking in a rectangular warehouse: a solvable case for the travelling salesman problem. Oper Res 31:507–521

    Article  MATH  Google Scholar 

  20. Reese A (2009) Random number generators in genetic algorithms for unconstrained and constrained optimization. J Nonlinear Anal 71:679–692

    Article  MathSciNet  Google Scholar 

  21. Reinelt G (1991) TSPLIB—a traveling salesman problem library. ORSA J Comput 3:376–384

    Article  MATH  Google Scholar 

  22. Tanimoto T (1958) An elementary mathematical theory of classification and prediction. Int. Rpt, IBM Corp

    Google Scholar 

  23. Theodoridis S, Koutroumbas K (2009) Pattern recognition. Academic Press, London

    Google Scholar 

  24. Vidal T, Crainic TG, Gendreau M, Prins C (2014) A unified solution framework for multi-attribute vehicle routing problems. Eur J Oper Res 234(3):657–673

    Article  MathSciNet  Google Scholar 

  25. Wang Y (2014) The hybrid genetic algorithm with two local optimization strategies for traveling salesman problem. Comput Ind Eng 70:124–133

    Article  Google Scholar 

  26. Wei JD, Lee DT (2004) A new approach to the traveling salesman problem using genetic algorithms with priority encoding. Evol Comput 2:1457–1464

    Google Scholar 

  27. Yun Y, Moon C, Kim D (2009) Hybrid genetic algorithm with adaptive local search scheme for solving multistage-based supply chain problems. Comput Ind Eng 56:821–838

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran

    Marjan Kuchaki Rafsanjani & Sadegh Eskandari

  2. Department of Mathematics, Shahid Bahonar University of Kerman, Kerman, Iran

    Arsham Borumand Saeid

Authors
  1. Marjan Kuchaki Rafsanjani
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  2. Sadegh Eskandari
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  3. Arsham Borumand Saeid
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Correspondence to Marjan Kuchaki Rafsanjani.

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Kuchaki Rafsanjani, M., Eskandari, S. & Borumand Saeid, A. A similarity-based mechanism to control genetic algorithm and local search hybridization to solve traveling salesman problem. Neural Comput & Applic 26, 213–222 (2015). https://doi.org/10.1007/s00521-014-1717-7

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  • DOI: https://doi.org/10.1007/s00521-014-1717-7

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