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International Workshop on Soft Computing Models in Industrial and Environmental Applications

SOCO 2020: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020) pp 530–539Cite as

Hybrid Genetic Algorithms and Tour Construction and Improvement Algorithms Used for Optimizing the Traveling Salesman Problem

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1268))

Abstract

The traveling salesman problem (TSP) aims at finding the shortest tour that passes through each vertex in a given graph exactly once. To address TSP, many exact and approximate algorithms have been proposed. In this paper, we propose three new algorithms for TSP based on a genetic algorithm (GA) and an order crossover operator. In the first algorithm, a generic version of a GA with random population is introduced. In the second algorithm, after the random population is introduced, the selected parents are improved with a 2-OPT algorithm and processed further with a GA. Finally, in the third algorithm, the initial solutions are obtained with a nearest neighbor algorithm (NNA) and a nearest insertion algorithm (NIA); afterwards they are improved with a 2-OPT and processed further with a GA. Our approach differs from previous papers for using a GA for TSP in two ways. First, every successive generation of individuals is generated based primarily on 4 best parents from the previous generation regardless the number of individuals in each population. Second, we have proposed the new hybridization between GA, NNA, NIA and 2-OPT. The overall results demonstrate that the proposed GAs offer promising results, particularly for large-sized instances.

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Authors and Affiliations

  1. Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000, Novi Sad, Serbia

    Vladimir Ilin, Dragan Simić & Svetislav D. Simić

  2. Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 1–9, 21000, Novi Sad, Serbia

    Svetlana Simić

Authors
  1. Vladimir Ilin
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  2. Dragan Simić
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  3. Svetislav D. Simić
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  4. Svetlana Simić
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Corresponding author

Correspondence to Vladimir Ilin .

Editor information

Editors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Álvaro Herrero

  2. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Carlos Cambra

  3. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Daniel Urda

  4. Technological Institute of Castilla y León, Burgos, Spain

    Javier Sedano

  5. Department of Industrial Engineering, University of A Coruña, La Coruña, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Ilin, V., Simić, D., Simić, S.D., Simić, S. (2021). Hybrid Genetic Algorithms and Tour Construction and Improvement Algorithms Used for Optimizing the Traveling Salesman Problem. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_51

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