A Proposal of Good Practice in the Formulation and Comparison of Meta-heuristics for Solving Routing Problems

  • Eneko Osaba
  • Roberto Carballedo
  • Fernando Diaz
  • Enrique Onieva
  • Asier Perallos
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 299)


Researchers who investigate in any field related to computational algorithms (defining new algorithms or improving existing ones) find large difficulties when evaluating their work. Comparisons among different scientific works in this area is often difficult, due to the ambiguity or lack of detail in the presentation of the work or its results. In many cases, a replication of the work done by others is required, which means a waste of time and a delay in the research advances. After suffering this problem in many occasions, a simple procedure has been developed to use in the presentation of techniques and its results in the field of routing problems. In this paper this procedure is described in detail, and all the good practices to follow are introduced step by step. Although these good practices can be applied for any type of combinatorial optimization problem, the literature of this study is focused in routing problems. This field has been chosen due to its importance in the real world, and its great relevance in the literature.


Meta-heuristics Routing Problems Combinatorial Optimization Intelligent Transportation Systems Good Practice Proposal 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Papadimitriou, C.: The new faces of combinatorial optimization. Journal of Combinatorial Optimization 7422(1), 19–23 (2012)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Korte, B., Vygen, J.: Combinatorial optimization: theory and algorithms, vol. 21. Springer (2012)Google Scholar
  3. 3.
    Lawler, E., Lenstra, J., Kan, A., Shmoys, D.: The traveling salesman problem: a guided tour of combinatorial optimization, vol. 3. Wiley, New York (1985)zbMATHGoogle Scholar
  4. 4.
    Laporte, G.: The vehicle routing problem: An overview of exact and approximate algorithms. European Journal of Operational Research 59(3), 345–358 (1992)CrossRefzbMATHGoogle Scholar
  5. 5.
    Lenstra, J., Kan, A.: Complexity of vehicle routing and scheduling problems. Networks 11(2), 221–227 (1981)CrossRefGoogle Scholar
  6. 6.
    Onieva, E., Alonso, J., Pérez, J., Milanés, V., De Pedro, T.: Autonomous car fuzzy control modeled by iterative genetic algorithms. In: IEEE International Conference on Fuzzy Systems, pp. 1615–1620 (2009)Google Scholar
  7. 7.
    Zachariadis, E.E., Kiranoudis, C.T.: An effective local search approach for the vehicle routing problem with backhauls. Expert Systems with Applications 39(3), 3174–3184 (2012)CrossRefGoogle Scholar
  8. 8.
    Simić, D., Simić, S.: Hybrid artificial intelligence approaches on vehicle routing problem in logistics distribution. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.-B. (eds.) HAIS 2012, Part III. LNCS, vol. 7208, pp. 208–220. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Onieva, E., Milanés, V., Villagra, J., Pérez, J., Godoy, J.: Genetic optimization of a vehicle fuzzy decision system for intersections. Expert Systems with Applications 39(18), 13148–13157 (2012)CrossRefGoogle Scholar
  10. 10.
    Stoean, R., Stoean, C.: Modeling medical decision making by support vector machines, explaining by rules of evolutionary algorithms with feature selection. Expert Systems with Applications 40(7), 2677–2686 (2013)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Gao, J., Sun, L., Gen, M.: A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Computers & Operations Research 35(9), 2892–2907 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    Joo, C.M., Kim, B.S.: Genetic algorithms for single machine scheduling with time-dependent deterioration and rate-modifying activities. Expert Systems with Applications 40(8), 3036–3043 (2013)CrossRefGoogle Scholar
  13. 13.
    Kirkpatrick, S., Gellat, C., Vecchi, M.: Optimization by simmulated annealing. Science 220(4598), 671–680 (1983)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Glover, F.: Tabu search, part i. INFORMS Journal on Computing 1(3), 190–206 (1989)CrossRefzbMATHGoogle Scholar
  15. 15.
    Goldberg, D.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Professional (1989)Google Scholar
  16. 16.
    De Jong, K.: Analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan, Michigan, USA (1975)Google Scholar
  17. 17.
    Dorigo, M., Blum, C.: Ant colony optimization theory: A survey. Theoretical Computer Science 344(2), 243–278 (2005)CrossRefzbMATHMathSciNetGoogle Scholar
  18. 18.
    Kennedy, J., Eberhart, R., et al.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, vol. 4, pp. 1942–1948 (1995)Google Scholar
  19. 19.
    Garcia-Gonzalo, E., Fernandez-Martinez, J.: A brief historical review of particle swarm optimization (pso). Journal of Bioinformatics and Intelligent Control 1(1), 3–16 (2012)CrossRefGoogle Scholar
  20. 20.
    Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE Congress on Evolutionary Computation, pp. 4661–4667 (2007)Google Scholar
  21. 21.
    Osaba, E., Diaz, F., Onieva, E.: Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Applied Intelligence, 1–22 (2014)Google Scholar
  22. 22.
    Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N.: A comprehensive survey: artificial bee colony (abc) algorithm and applications. Artificial Intelligence Review, 1–37 (2012)Google Scholar
  23. 23.
    Osaba, E., Diaz, F., Onieva, E.: A novel meta-heuristic based on soccer concepts to solve routing problems. In: Proceeding of the Fifteenth Annual Conference Companion on Genetic and Evolutionary Computation Conference Companion, pp. 1743–1744. ACM (2013)Google Scholar
  24. 24.
    Jiang, K., Song, B., Shi, X., Song, T.: An overview of membrane computing. Journal of Bioinformatics and Intelligent Control 1(1), 17–26 (2012)CrossRefGoogle Scholar
  25. 25.
    Glover, F., Gutin, G., Yeo, A., Zverovich, A.: Construction heuristics for the asymmetric tsp. European Journal of Operational Research 129(3), 555–568 (2001)CrossRefzbMATHMathSciNetGoogle Scholar
  26. 26.
    Larranaga, P., Kuijpers, C.M.H., Murga, R.H., Inza, I., Dizdarevic, S.: Genetic algorithms for the travelling salesman problem: A review of representations and operators. Artificial Intelligence Review 13(2), 129–170 (1999)CrossRefGoogle Scholar
  27. 27.
    Mladenović, N., Urošević, D., Hanafi, S.: Variable neighborhood search for the travelling deliveryman problem. A Quarterly Journal of Operations Research 11(1), 57–73 (2013)zbMATHMathSciNetGoogle Scholar
  28. 28.
    Bräysy, O., Gendreau, M.: Vehicle routing problem with time windows, part i: Route construction and local search algorithms. Transportation Sciences 39(1), 104–118 (2005)CrossRefGoogle Scholar
  29. 29.
    Cordeau, J.F., Desaulniers, G., Desrosiers, J., Solomon, M.M., Soumis, F.: Vrp with time windows. The Vehicle Routing Problem 9, 157–193 (2002)CrossRefMathSciNetGoogle Scholar
  30. 30.
    Chen, A.L., Yang, G.K., Wu, Z.M.: Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem. Journal of Zhejiang University Science A 7(4), 607–614 (2006)CrossRefzbMATHGoogle Scholar
  31. 31.
    Wang, C.H., Lu, J.Z.: A hybrid genetic algorithm that optimizes capacitated vehicle routing problems. Expert Systems with Applications 36(2), 2921–2936 (2009)CrossRefGoogle Scholar
  32. 32.
    Leung, S., Zhang, Z., Zhang, D., Hua, X., Lim, M.: A meta-heuristic algorithm for heterogeneous fleet vehicle routing problems with two-dimensional loading constraints. European Journal of Operational Research 225(2), 199–210 (2013)CrossRefGoogle Scholar
  33. 33.
    Solomon, M.: Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations Research 35(2), 254–265 (1987)CrossRefzbMATHMathSciNetGoogle Scholar
  34. 34.
    Julstrom, B.A.: Very greedy crossover in a genetic algorithm for the traveling salesman problem. In: ACM Symposium on Applied Computing, pp. 324–328 (1995)Google Scholar
  35. 35.
    Davis, L.: Applying adaptive algorithms to epistatic domains. In: Proceedings of the International Joint Conference on Artificial Intelligence, vol. 1, pp. 161–163 (1985)Google Scholar
  36. 36.
    Osaba, E., Onieva, E., Carballedo, R., Diaz, F., Perallos, A.: An adaptive multi-crossover population algorithm for solving routing problems. In: Terrazas, G., Otero, F.E.B., Masegosa, A.D. (eds.) NICSO 2013. SCI, vol. 512, pp. 123–135. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  37. 37.
    Syswerda, G.: Schedule optimization using genetic algorithms. In: Handbook of Genetic Algorithms, pp. 332–349 (1991)Google Scholar
  38. 38.
    Ray, S., Bandyopadhyay, S., Pal, S.: New operators of genetic algorithms for traveling salesman problem. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, pp. 497–500 (2004)Google Scholar
  39. 39.
    Burke, E., Kendall, G.: Comparison of meta-heuristic algorithms for clustering rectangles. Computers & Industrial Engineering 37(1), 383–386 (1999)CrossRefGoogle Scholar
  40. 40.
    Ahmed, Z.H.: Genetic algorithm for the traveling salesman problem using sequential constructive crossover operator. International Journal of Biometrics and Bioinformatics 3(6), 96 (2010)Google Scholar
  41. 41.
    Albayrak, M., Allahverdi, N.: Development a new mutation operator to solve the traveling salesman problem by aid of genetic algorithms. Expert Systems with Applications 38(3), 1313–1320 (2011)CrossRefGoogle Scholar
  42. 42.
    Osaba, E., Carballedo, R.: A methodological proposal to eliminate ambiguities in the comparison of vehicle routing problem solving techniques. In: Proceedings of the 4th International Joint Conference on Computational Intelligence, pp. 310–313 (2012)Google Scholar
  43. 43.
    Reinelt, G.: Tsplib, a traveling salesman problem library. INFORMS Journal on Computing 3(4), 376–384 (1991)CrossRefzbMATHMathSciNetGoogle Scholar
  44. 44.
    Wang, L., Zhang, J., Li, H.: An improved genetic algorithm for tsp. In: International Conference on Machine Learning and Cybernetics, vol. 2, pp. 925–928 (2007)Google Scholar
  45. 45.
    Thamilselvan, R., Balasubramanie, P.: A genetic algorithm with a tabu search (gta) for traveling salesman problem. International Journal of Recent Trends in Engineering 1(1), 607–610 (2009)Google Scholar
  46. 46.
    Yan, X., Zhang, C., Luo, W., Li, W., Chen, W., Liu, H.: Solve traveling salesman problem using particle swarm optimization algorithm. International Journal of Computer Science Issues 9(2), 264–271 (2012)Google Scholar
  47. 47.
    Sallabi, O.M., El-Haddad, Y.: An improved genetic algorithm to solve the traveling salesman problem. World Academy of Science, Engineering and Technology 52, 471–474 (2009)Google Scholar
  48. 48.
    Nemati, K., Shamsuddin, S., Kamarposhti, M.: Using imperial competitive algorithm for solving traveling salesman problem and comparing the efficiency of the proposed algorithm with methods in use. Australian Journal of Basic and Applied Sciences 5(10), 540–543 (2011)Google Scholar
  49. 49.
    Tsubakitani, S., Evans, J.R.: Optimizing tabu list size for the traveling salesman problem. Computers & Operations Research 25(2), 91–97 (1998)CrossRefzbMATHGoogle Scholar
  50. 50.
    Ray, S.S., Bandyopadhyay, S., Pal, S.K.: New genetic operators for solving TSP: Application to microarray gene ordering. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 617–622. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  51. 51.
    Nikolić, M., Teodorović, D.: Empirical study of the bee colony optimization (bco) algorithm. Expert Systems with Applications 40(11), 4609–4620 (2013)CrossRefGoogle Scholar
  52. 52.
    Osaba, E., Onieva, E., Carballedo, R., Diaz, F., Perallos, A., Zhang, X.: A multi-crossover and adaptive island based population algorithm for solving routing problems. Journal of Zhejiang University Science C 14(11), 815–821 (2013)CrossRefGoogle Scholar
  53. 53.
    Pullan, W.: Adapting the genetic algorithm to the travelling salesman problem. In: IEEE Congress on Evolutionary Computation, vol. 2, pp. 1029–1035 (2003)Google Scholar
  54. 54.
    Osaba, E., Onieva, E., Diaz, F., Carballedo, R., Perallos, A.: Comments on ”Albayrak, M., & Allahverdy N. Development a new mutation operator to solve the traveling salesman problem by aid of genetic algorithms. Expert Systems with Applications 38(3), 1313–1320 (2011), A proposal of good practice. Expert Systems with Applications 41(4), 1530–1531 (2014)Google Scholar
  55. 55.
    Osaba, E., Carballedo, R., Diaz, F., Perallos, A.: Discussion related to ”Wang, C.-H., & Lu, J.-Z. A hybrid genetic algorithm that optimizes capacitated vehicle routing problem. Expert Systems with Applications 36(2), 2921–2936 (2009), Expert Systems with Applications 40(14), 5425–5426 (2013)Google Scholar
  56. 56.
    Corchado, E., Baruque, B.: Wevos-visom: An ensemble summarization algorithm for enhanced data visualization. Neurocomputing 75(1), 171–184 (2012)CrossRefGoogle Scholar
  57. 57.
    Corchado, E., Wozniak, M., Abraham, A., de Carvalho, A.C.P.L.F., Snásel, V.: Recent trends in intelligent data analysis. Neurocomputing 126, 1–2 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Eneko Osaba
    • 1
  • Roberto Carballedo
    • 1
  • Fernando Diaz
    • 1
  • Enrique Onieva
    • 1
  • Asier Perallos
    • 1
  1. 1.Deusto Institute of Technology (DeustoTech)University of DeustoBilbaoSpain

Personalised recommendations