Skip to main content
Log in

Multi-objective traveling salesman problem: an ABC approach

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Using the concept of swap operation and swap sequence on the sequence of paths of a Traveling Salesman Problem(TSP) Artificial Bee Colony (ABC) algorithm is modified to solve multi-objective TSP. The fitness of a solution is determined using a rule following the dominance property of a multi-objective optimization problem. This fitness is used for the selection process of the onlooker bee phase of the algorithm. A set of rules is used to improve the solutions in each phase of the algorithm. Rules are selected according to their performance using the roulette wheel selection process. At the end of each iteration, the parent solution set and the solution sets after each phase of the ABC algorithm are combined to select a new solution set for the next iteration. The combined solution set is divided into different non-dominated fronts and then a new solution set, having cardinality of parent solution set, is selected from the upper-level non-dominated fronts. When some solutions are required to select from a particular front then crowding distances between the solutions of the front are measured and the isolated solutions are selected for the preservation of diversity. Different standard performance metrics are used to test the performance of the proposed approach. Different sizes standard benchmark test problems from TSPLIB are used for the purpose. Test results show that the proposed approach is efficient enough to solve multi-objective TSP.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Ariyasingha IDID, Fernando TGI (2015) Performance analysis of the multi-objective ant colony optimization algorithms for the traveling salesman problem. Swarm and Evolutionary Computation 23:11–26

    Google Scholar 

  2. Beed RS, Sarkar S, Roy A, Chatterjee S (2017) A study of the genetic algorithm parameters for solving multi-objective travelling salesman problem. In: International conference on information technology (ICIT). IEEE, pp 23–29

  3. Changdar C, Mahapatr GS, Pal RK (2014) An efficient genetic algorithm for multi-objective solid travelling salesman problem under fuzziness. Swarm and Evolutionary Computation 15:27–37

    Google Scholar 

  4. Cornu M, Cazenave T, Vanderpooten D (2017) Perturbed decomposition algorithm applied to the multi-objective traveling salesman problem. Computers & Operations Research 79:314–330

    MathSciNet  MATH  Google Scholar 

  5. Coello CAC, Cortés NC (2005) Solving multiobjective optimization problems using an artificial immune system. Genetic Programming and Evolvable Machines 6(2):163–190

    Google Scholar 

  6. de Souza MZ, Pozo ATR (2014) A GPU implementation of MOEA/d-ACO for the multiobjective traveling salesman problem. In: 2014 Brazilian conference on intelligent systems. IEEE, pp 324–329

  7. Deb K (2001) Multi-objective optimization using evolutionary algorithm, vol 16. Wiley, New York

    MATH  Google Scholar 

  8. Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Google Scholar 

  9. Fonseca CM, Fleming PJ (1993) Genetic algorithms for multi-objective optimization: formulation discussion and generalization. In: Icga, vol 93, pp 416–423

  10. Gottlieb J, Raidl GR (2006) Evolutionary computation in combinatorial optimization (6 conf.) Springer, New York

    Google Scholar 

  11. Hansen MP (2000) Use of substitute scalarizing functions to guide a local search based heuristic: the case of moTSP. Journal of Heuristics 6(3):419–431

    MATH  Google Scholar 

  12. Horn J, Nafpliotis N, Goldberg DE (1994) A niched Pareto genetic algorithm for multi-objective optimization. In: Proceedings of the first IEEE conference on evolutionary computation. IEEE World Congress on Computational Intelligence, pp 82–87

  13. Iqbal S, Kaykobad M, Rahman MS (2015) Solving the multi-objective vehicle routing problem with soft time windows with the help of bees. Swarm and Evolutionary Computation 24:50–64

    Google Scholar 

  14. Hameed IA (2019) Multi-objective solution of traveling salesman problem with time. In: International conference on advanced machine learning technologies and applications. Springer , pp 121–132

  15. Jaszkiewicz A (2002) Genetic local search for multi-objective combinatorial optimization. European Journal of Operational Research 137(1):50–71

    MathSciNet  MATH  Google Scholar 

  16. Ke L, Zhang Q, Battiti R (2013) MOEA/D-ACO: a multiobjective evolutionary algorithm using decomposition and antcolony. IEEE Transactions on Cybernetics 43(6):1845–1859

    Google Scholar 

  17. Kiran MS, Hakli H, Gunduz M, Uguz H (2015) Artificial bee colony algorithm with variable search strategy for continuous optimization. Inf Sci 300:140–157

    MathSciNet  Google Scholar 

  18. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation 214(1):108–132

    MathSciNet  MATH  Google Scholar 

  19. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization 39(3):459–471

    MathSciNet  MATH  Google Scholar 

  20. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization (vol 200, pp 1–10). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department

  21. Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57

    Google Scholar 

  22. Karaboga D, Gorkemli B (2011) A combinatorial artificial bee colony algorithm for traveling salesman problem. In: 2011 international symposium on innovations in intelligent systems and applications. IEEE, pp 50–53

  23. Khanra A, Pal T, Maiti MK, Maiti M (2019) Multi-objective four dimensional imprecise TSP solved with a hybrid multi-objective ant colony optimization-genetic algorithm with diversity. Journal of Intelligent & Fuzzy Systems 36(1):47–65

    Google Scholar 

  24. Khan I, Pal S, Maiti MK (2019) A hybrid PSO-GA algorithm for traveling salesman problems in different environments. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems 27(05):693–717

    Google Scholar 

  25. Khan I, Maiti MK (2019) A swap sequence based artificial bee colony algorithm for traveling salesman problem. Swarm and Evolutionary Computation 44:428–438

    Google Scholar 

  26. Khan I, Maiti MK, Maiti M (2017) Coordinating particle swarm optimization, ant colony optimization and K-Opt algorithm for traveling salesman problem. In: International conference on mathematics and computing. Springer, Singapore, pp 103–119

  27. Khan I, Maiti MK (2018) A novel hybrid algorithm for generalized traveling salesman problems in different environments. Vietnam Journal of Computer Science 5(1):27–43

    Google Scholar 

  28. (1985) The traveling salesman problem: a guided tour of combinatorial optimization. Wiley-Interscience Series in Discrete Mathematics

  29. Lin S, Kernighan BW (1973) An effective heuristic algorithm for the traveling-salesman problem. Operations Research 21(2):498–516

    MathSciNet  MATH  Google Scholar 

  30. Li H, Zhang Q (2008) Multi-objective optimization problems with complicated Pareto sets, MOEA/d and NSGA-II. IEEE Transactions on Evolutionary Computation 13(2):284–302

    Google Scholar 

  31. Maity S, Roy A, Maiti M (2016) An imprecise multi-objective genetic algorithm for uncertain constrained multi-objective solid travelling salesman problem. Expert Systems With Applications 46:196–223

    Google Scholar 

  32. Moraes DH, Sanches DS, da Silva Rocha J, Garbelini JMC, Castoldi MF (2019) A novel multi-objective evolutionary algorithm based on subpopulations for the bi-objective traveling salesman problem. Soft Comput 23(15):6157–6168

    Google Scholar 

  33. Martin-Moreno R, Vega-Rodriguez MA (2018) Multi-objective artificial bee colony algorithm applied to the bi-objective orienteering problem. Knowl-Based Syst 154:93–101

    Google Scholar 

  34. Michalewicz Z, Hartley SJ (1996) Genetic algorithms+ data structures= evolution programs. Math Intell 18(3):71

    Google Scholar 

  35. Pan QK, Tasgetiren MF, Suganthan PN, Chua TJ (2011) A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Information sciences 181(12):2455–2468

    MathSciNet  Google Scholar 

  36. Psychas ID, Delimpasi E, Marinakis Y (2015) Hybrid evolutionary algorithms for the multiobjective traveling salesman problem. Expert Syst Appl 42(22):8956–8970

    Google Scholar 

  37. Reinelt G (1991) TSPLIB—a traveling salesman problem library. ORSA Journal on Computing 3(4):376–384

    MATH  Google Scholar 

  38. Samanlioglu F, Ferrell WG Jr, Kurz ME (2008) A memetic random-key genetic algorithm for a symmetric multi-objective traveling salesman problem. Computers & Industrial Engineering 55(2):439–449

    Google Scholar 

  39. Singh A (2009) An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem. Appl Soft Comput 9(2):625–631

    Google Scholar 

  40. Srinivas N, Deb K (1994) Multi-objective optimization using non dominated sorting in genetic algorithms. Evolutionary Computation 2(3):221–248

    Google Scholar 

  41. Tang L, Zhou Y, Xiang Y, Lai X (2016) A multi-objective artificial bee colony algorithm combined with a local search method. International Journal on Artificial Intelligence Tools 25(03):1650009

    Google Scholar 

  42. Wong LP, Low MYH, Chong CS (2010) Bee colony optimization with local search for traveling salesman problem. International Journal on Artificial Intelligence Tools 19(03):305–334

    Google Scholar 

  43. Wang KP, Huang L, Zhou CG, Pang W (2003) Particle swarm optimization for traveling salesman problem. In: Proceedings of the 2003 international conference on machine learning and cybernetics, vol 3. IEEE, pp 1583–1585

  44. Zhou A, Qu BY, Li H, Zhao SZ, Suganthan PN, Zhang Q (2011) Multi-objective evolutionary algorithms: a survey of the state of the art. Swarm and Evolutionary Computation 1(1):32–49

    Google Scholar 

  45. Zou W, Zhu Y, Chen H, Zhang B (2011) Solving multiobjective optimization problems using artificial bee colony algorithm. Discrete dynamics in nature and society

  46. Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving The strength Pareto evolutionary algorithm. TIK-report, 103

  47. Zitzler E (1999) Evolutionary algorithms for multi-objective optimization: methods and applications (vol 63). Ithaca: Shaker

  48. Zitzler E, Thiele L (1998) Multi-objective optimization using evolutionary algorithms—a comparative case study. In: International conference on parallel problem solving from nature. Springer, Berlin, pp 292–301

  49. Zitzler E, Thiele L (1999) Multi-objective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 3(4):257–271

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Indadul Khan.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khan, I., Maiti, M.K. & Basuli, K. Multi-objective traveling salesman problem: an ABC approach. Appl Intell 50, 3942–3960 (2020). https://doi.org/10.1007/s10489-020-01713-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-020-01713-4

Keywords

Navigation