Multi-ant colony optimization algorithm based on hybrid recommendation mechanism | Applied Intelligence Skip to main content
Springer Nature Link
Log in

Multi-ant colony optimization algorithm based on hybrid recommendation mechanism

  • Published:
Applied Intelligence Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Traditional ant colony algorithm has the problems of slow convergence speed and easy to fall into local optimum when solving traveling salesman problem. To solve these problems, a multi-ant colony optimization algorithm based on hybrid recommendation mechanism is proposed. Firstly, a heterogeneous multi-ant colony strategy is proposed to balance the convergence and diversity of the algorithm. Secondly, a content-based recommendation strategy is proposed to dynamically divide the traveling salesman problem by self-organizing mapping clustering algorithm, which improves the convergence speed of the algorithm. Thirdly, a collaborative filtering recommendation mechanism based on a multi-attribute decision making model is proposed, including three recommendation strategies: the high-quality solution guidance recommendation strategy based on the convergence factor to improve the convergence of the algorithm; the pheromone fusion recommendation strategy based on the browsing factor to enrich the diversity of the subpopulations; the public path update recommendation strategy based on the population similarity to adaptively regulate the diversity of the algorithm. Finally, when the algorithm stagnates, the association rule-based recommendation strategy is used to help the ant colony jump out of the local optimum. The performance of the improved algorithm is tested on the traveling salesman problem library, and the experimental results show that the proposed algorithm significantly improves the convergence speed and solution accuracy, especially when solving large-scale problems.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Donati AV, Montemanni R, Casagrande N et al (2008) Time dependent vehicle routing problem with a multi ant colony system. Eur J Oper Res 185:1174–1191. https://doi.org/10.1016/j.ejor.2006.06.047

    Article  MathSciNet  MATH  Google Scholar 

  2. Yi N, Xu J, Yan L, Huang L (2020) Task optimization and scheduling of distributed cyber-physical system based on improved ant colony algorithm. Futur Gener Comput Syst Int J ESCIENCE 109:134–148. https://doi.org/10.1016/j.future.2020.03.051

    Article  Google Scholar 

  3. Yue W, Xi Y, Guan X (2019) A new searching approach using improved multi-ant colony scheme for multi-uavs in unknown environments. IEEE Access 7:161094–161102. https://doi.org/10.1109/ACCESS.2019.2949249

    Article  Google Scholar 

  4. Sharma AS, Kim DS (2021) Energy efficient multipath ant colony based routing algorithm for mobile ad hoc networks. Ad Hoc Netw 113:102396. https://doi.org/10.1016/j.adhoc.2020.102396

    Article  Google Scholar 

  5. Dorigo M, Maniezzo V, et al (1996) Ant system: optimization by a colony of cooperating agents. Syst Man, Cybern Part B Cybern IEEE Trans 26:29–41

    Article  Google Scholar 

  6. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Ec 1:53–66

    Google Scholar 

  7. Stutzle T, Hoos H (1997) MAX-MIN Ant System and local search for the traveling salesman problem. In: Proceedings of 1997 IEEE international conference on evolutionary computation (ICEC’97). pp 309–314

  8. Jiang WY, Lin Y, Chen M, Yu YY (2015) A co-evolutionary improved multi-ant colony optimization for ship multiple and branch pipe route design. Ocean Eng 102:63–70. https://doi.org/10.1016/j.oceaneng.2015.04.028

    Article  Google Scholar 

  9. Ning J, Zhang Q, Zhang C, Zhang B (2018) A best-path-updating information-guided ant colony optimization algorithm. Inf Sci (Ny) 433–434:142–162. https://doi.org/10.1016/j.ins.2017.12.047

    Article  MathSciNet  Google Scholar 

  10. Wang Y, Han Z (2021) Ant colony optimization for traveling salesman problem based on parameters optimization. Appl Soft Comput 107:107439. https://doi.org/10.1016/j.asoc.2021.107439

    Article  Google Scholar 

  11. Yan Y, Sohn H, Reyes G (2017) A modified ant system to achieve better balance between intensification and diversification for the traveling salesman problem. Appl Soft Comput J 60:256–267. https://doi.org/10.1016/j.asoc.2017.06.049

    Article  Google Scholar 

  12. Tuani AF, Keedwell E, Collett M (2020) Heterogenous adaptive ant colony optimization with 3-opt local search for the travelling salesman problem. Appl Soft Comput 97:106720. https://doi.org/10.1016/j.asoc.2020.106720

    Article  Google Scholar 

  13. Yueshun H, Ping D (2012) A study of a new multi-ant colony optimization algorithm BT—advances in information technology and industry applications. In: Zeng D (ed). Springer Berlin Heidelberg, Berlin, Heidelberg, pp 155–161

  14. Pan H, You X, Liu S, Zhang D (2021) Pearson correlation coefficient-based pheromone refactoring mechanism for multi-colony ant colony optimization. Appl Intell 51:752–774. https://doi.org/10.1007/s10489-020-01841-x

    Article  Google Scholar 

  15. Shunmugapriya P, Kanmani S (2017) A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC Hybrid). Swarm Evol Comput 36:27–36. https://doi.org/10.1016/j.swevo.2017.04.002

    Article  Google Scholar 

  16. Jiang C, Wan Z, Peng Z (2020) A new efficient hybrid algorithm for large scale multiple traveling salesman problems. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2019.112867

    Article  Google Scholar 

  17. Wang Y, Wang L, Chen G et al (2020) An improved ant colony optimization algorithm to the periodic vehicle routing problem with time window and service choice. Swarm Evol Comput. https://doi.org/10.1016/j.swevo.2020.100675

    Article  Google Scholar 

  18. Awadallah MA, Al-Betar MA, Bolaji AL et al (2020) Island artificial bee colony for global optimization. Soft Comput 24:13461–13487. https://doi.org/10.1007/s00500-020-04760-8

    Article  Google Scholar 

  19. Glover F (2010) Heuristics for integer programming using surrogate constraints. Decis Sci 8:156–166

    Article  Google Scholar 

  20. Ezugwu AES, Adewumi AO, Frîncu ME (2017) Simulated annealing based symbiotic organisms search optimization algorithm for traveling salesman problem. Expert Syst Appl 77:189–210. https://doi.org/10.1016/j.eswa.2017.01.053

    Article  Google Scholar 

  21. Greiner R (1996) PALO: a probabilistic hill-climbing algorithm. Artif Intell 84:177–208

    Article  MathSciNet  Google Scholar 

  22. Wang ZZ, Sobey A (2020) A comparative review between Genetic Algorithm use in composite optimisation and the state-of-the-art in evolutionary computation. Compos Struct 233:111739. https://doi.org/10.1016/j.compstruct.2019.111739

    Article  Google Scholar 

  23. Schwefel H-P (1977) Evolutionsstrategien für die numerische Optimierung BT - Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie: Mit einer vergleichenden Einführung in die Hill-Climbing- und Zufallsstrategie. In: Schwefel H-P (ed). Birkhäuser Basel, Basel, pp 123–176

  24. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3:82–102. https://doi.org/10.1109/4235.771163

    Article  Google Scholar 

  25. Oliveira SMD, Bezerra L, Stützle T, et al (2021) A computational study on ant colony optimization for the traveling salesman problem with dynamic demands. Comput Oper Res 105359

  26. Zhong Y, Lin J, Wang L, Zhang H (2018) Discrete comprehensive learning particle swarm optimization algorithm with Metropolis acceptance criterion for traveling salesman problem. Swarm Evol Comput 42:77–88. https://doi.org/10.1016/j.swevo.2018.02.017

    Article  Google Scholar 

  27. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471

    Article  MathSciNet  Google Scholar 

  28. Pan W-T (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Syst 26:69–74. https://doi.org/10.1016/j.knosys.2011.07.001

    Article  Google Scholar 

  29. Askarzadeh A (2014) Bird mating optimizer: an optimization algorithm inspired by bird mating strategies. Commun Nonlinear Sci Numer Simul 19:1213–1228. https://doi.org/10.1016/j.cnsns.2013.08.027

    Article  MathSciNet  MATH  Google Scholar 

  30. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  31. Xin-She Y (2010) A new metaheuristic bat-inspired algorithm, nature inspired cooperative strategies for optimization (NISCO 2010)

  32. Saji Y, Riffi ME (2016) A novel discrete bat algorithm for solving the travelling salesman problem. Neural Comput Appl 27:1853–1866. https://doi.org/10.1007/s00521-015-1978-9

    Article  Google Scholar 

  33. Hatamlou A (2013) Black hole: A new heuristic optimization approach for data clustering. Inf Sci (Ny) 222:175–184. https://doi.org/10.1016/j.ins.2012.08.023

  34. Hatamlou A (2018) Solving travelling salesman problem using black hole algorithm. SOFT Comput 22:8167–8175. https://doi.org/10.1007/s00500-017-2760-y

    Article  Google Scholar 

  35. Akhand MAH, Ayon SI, Shahriyar SA et al (2020) Discrete spider monkey optimization for travelling salesman problem. Appl Soft Comput J 86:105887. https://doi.org/10.1016/j.asoc.2019.105887

    Article  Google Scholar 

  36. Shi YH (2011) Brain Storm Optimization Algorithm. Adv SWARMIntell PT I 6728:303–309

    Google Scholar 

  37. Wu C, Fu X (2020) An agglomerative greedy brain storm optimization algorithm for solving the TSP. IEEE Access 8:201606–201621. https://doi.org/10.1109/ACCESS.2020.3035899

    Article  Google Scholar 

  38. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey WolfOptimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  39. Panwar K, Deep K (2021) Discrete Grey Wolf Optimizer for symmetric travelling salesman problem. Appl Soft Comput 105:107298. https://doi.org/10.1016/j.asoc.2021.107298

    Article  Google Scholar 

  40. Mavrovouniotis M, Li C, Yang S (2017) A survey of swarm intelligence for dynamic optimization: algorithms and applications. Swarm Evol Comput 33:1–17. https://doi.org/10.1016/j.swevo.2016.12.005

    Article  Google Scholar 

  41. Zhang D, You X, Liu S, Yang K (2019) Multi-colony ant colony optimization based on generalized Jaccard similarity recommendation strategy. IEEE Access 7:157303–157317. https://doi.org/10.1109/ACCESS.2019.2949860

    Article  Google Scholar 

  42. Yu J, You X, Liu S (2020) Dynamic density clustering ant colony algorithm with filtering recommendation backtracking mechanism. IEEE Access 8:154471–154484. https://doi.org/10.1109/ACCESS.2020.3002817

    Article  Google Scholar 

  43. Kanna SKR, Sivakumar K, Lingaraj N (2021) Development of deer hunting linked earthworm optimization algorithm for solving large scale traveling salesman problem. Knowledge-Based Syst 227:107199. https://doi.org/10.1016/j.knosys.2021.107199

    Article  Google Scholar 

  44. Li S, You X, Liu S (2021) Multiple ant colony optimization using both novel LSTM network and adaptive Tanimoto communication strategy. Appl Intell 51:5644–5664. https://doi.org/10.1007/s10489-020-02099-z

    Article  Google Scholar 

  45. Ebadinezhad S (2020) DEACO: adopting dynamic evaporation strategy to enhance ACO algorithm for the traveling salesman problem. Eng Appl Artif Intell 92:103649. https://doi.org/10.1016/j.engappai.2020.103649

    Article  Google Scholar 

  46. Tongur V, Ülker E (2019) PSO-based improved multi-flocks migrating birds optimization (IMFMBO) algorithm for solution of discrete problems. Soft Comput 23:5469–5484. https://doi.org/10.1007/s00500-018-3199-5

    Article  Google Scholar 

  47. Choong SS, Wong LP, Lim CP (2019) An artificial bee colony algorithm with a modified choice function for the traveling salesman problem. Swarm Evol Comput 44:622–635. https://doi.org/10.1016/j.swevo.2018.08.004

    Article  Google Scholar 

  48. Zhang H, You X (2019) Multi-population ant colony optimization algorithm based on congestion factor and co-evolution mechanism. IEEE Access 7:158160–158169. https://doi.org/10.1109/ACCESS.2019.2950214

    Article  Google Scholar 

  49. Dahan F, El Hindi K, Mathkour H, Alsalman H (2019) Dynamic flying ant colony optimization (DFACO) for solving the traveling salesman problem. Sensors (Switzerland). https://doi.org/10.3390/s19081837

    Article  Google Scholar 

  50. Gülcü Ş, Mahi M, Baykan ÖK, Kodaz H (2018) A parallel cooperative hybrid method based on ant colony optimization and 3-Opt algorithm for solving traveling salesman problem. Soft Comput 22:1669–1685. https://doi.org/10.1007/s00500-016-2432-3

    Article  Google Scholar 

  51. Alipour MM, Razavi SN, Feizi Derakhshi MR, Balafar MA (2018) A hybrid algorithm using a genetic algorithm and multiagent reinforcement learning heuristic to solve the traveling salesman problem. Neural Comput Appl 30:2935–2951. https://doi.org/10.1007/s00521-017-2880-4

    Article  Google Scholar 

  52. Zhong Y, Lin J, Wang L, Zhang H (2017) Hybrid discrete artificial bee colony algorithm with threshold acceptance criterion for traveling salesman problem. Inf Sci (Ny) 421:70–84. https://doi.org/10.1016/j.ins.2017.08.067

    Article  MathSciNet  Google Scholar 

  53. Lin Y, Bian Z, Liu X (2016) Developing a dynamic neighborhood structure for an adaptive hybrid simulated annealing—tabu search algorithm to solve the symmetrical traveling salesman problem. Appl Soft Comput J 49:937–952. https://doi.org/10.1016/j.asoc.2016.08.036

    Article  Google Scholar 

  54. Deng W, Zhao H, Zou L et al (2017) A novel collaborative optimization algorithm in solving complex optimization problems. Soft Comput 21:4387–4398. https://doi.org/10.1007/s00500-016-2071-8

    Article  Google Scholar 

  55. Osaba E, Yang XS, Diaz F et al (2016) An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems. Eng Appl Artif Intell 48:59–71. https://doi.org/10.1016/j.engappai.2015.10.006

    Article  Google Scholar 

  56. Yong W (2015) Hybrid max-min ant system with four vertices and three lines inequality for traveling salesman problem. Soft Comput 19:585–596. https://doi.org/10.1007/s00500-014-1279-8

    Article  Google Scholar 

  57. Mahi M, Baykan ÖK, Kodaz H (2015) A new hybrid method based on particle swarm optimization, ant colony optimization and 3-Opt algorithms for traveling salesman problem. Appl Soft Comput J 30:484–490. https://doi.org/10.1016/j.asoc.2015.01.068

    Article  Google Scholar 

Download references

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61673258, Grant 61075115 and the Shanghai Natural Science Foundation under Grant 19ZR1421600.

Author information

Authors and Affiliations

  1. College of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China

    Yifan Liu & Xiaoming You

  2. School of Management, Shanghai University of Engineering Science, Shanghai, 201620, China

    Sheng Liu

Authors
  1. Yifan Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xiaoming You
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Sheng Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Xiaoming You.

Ethics declarations

Conflict of interest

The authors have declared no conflict of interest.

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

Liu, Y., You, X. & Liu, S. Multi-ant colony optimization algorithm based on hybrid recommendation mechanism. Appl Intell 52, 8386–8411 (2022). https://doi.org/10.1007/s10489-021-02839-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02839-9

Keywords