Skip to main content
SpringerLink
Log in

A Survey on Ant Colony Optimization for Solving Some of the Selected NP-Hard Problem

  • Conference paper
  • First Online:
Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM 2019)

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 10))

Abstract

This paper analyses various ant colony optimization (ACO) based techniques for solving some of the selected intractable problems. ACO is one of the popularly used techniques in the field of meta-heuristic techniques that gave acceptable solutions to intractable problems like Travelling Salesperson (TS), Subset Selection (SS), Minimum Vertex Cover (MVC), and 0/1 Knapsack in tolerable amount of time. We have reviewed literature on the usage of aforesaid meta-heuristic algorithms for solving the intractable problems like TS, SS, MVC, and 0/1-Knapsack. A review of several ACO for NP-Hard problems with different instances shows that ACO algorithm demonstrates significant effectiveness and robustness in solving intractable problems.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Osman, I.H., Laporte, G.: Meta-heuristics: a bibliography. Ann. Oper. Res. 63, 513–623 (1996)

    Article  Google Scholar 

  2. Papadimitriou, C.H.: Computational Complexity. Addison-Wesley Inc., Boston (1994)

    MATH  Google Scholar 

  3. Ausiello, G., Crescenzi, P., Gambosi, G., Kann, V., Marchetti-Spaccamela, A., Protasi, M.: Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties. Springer, Heidelberg (1999)

    Book  Google Scholar 

  4. Asif, M., Baig, R.: Solving NP-complete problem using ACO algorithm. In: Proceedings of International Conference on Emerging Technologies, pp. 13–16. IEEE (2009)

    Google Scholar 

  5. Dorig, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)

    Book  Google Scholar 

  6. Dorigo, M., Blum, C.: Ant colony optimization theory: a survey. Theor. Comput. Sci. 344(2–3), 243–278 (2005)

    Article  MathSciNet  Google Scholar 

  7. Dorigo, M., Caro, G.D., Gambardella, L.: Ant algorithms for discrete optimization. Artif. Life 5, 137–172 (1999)

    Article  Google Scholar 

  8. Dorigo, M., Bonabeau, E., Theraulaz, G.: Ant algorithm and stigmergy. Future Gener. Comput. Syst. 16(8), 851–871 (2000)

    Article  Google Scholar 

  9. Li, B., Wang, L., Song, W.: Ant colony optimization for the traveling salesman problem based on ants with memory. In: Proceedings of Fourth International Conference on Natural Computation. IEEE (2009)

    Google Scholar 

  10. www.math.uwaterloo.ca/tsp/history/index.html

  11. https://kids.kiddle.co/Travelling_salesman_problem

  12. Asif, M., Baig, R.: Solving NP-complete problem using ACO algorithm. In: Proceedings of International Conference on Emerging Technologies, pp. 13–16 (2009)

    Google Scholar 

  13. Raghavendra, B.V.: Solving traveling salesmen problem using ant colony optimization algorithm. J. Appl. Comput. Math. JACM 4(6), 260 (2015)

    Google Scholar 

  14. Chen, H., Tan, G., Qian, G., Chen, R.: Ant colony optimization with tabu table to solve TSP problem. In: Proceedings of the 37th Chinese Control Conference, 25–27 July, pp. 2523–2527. IEEE (2018)

    Google Scholar 

  15. Mueller, C., Kiehne, N.: Hybrid approach for TSP based on neural networks and ant colony optimization. In: Symposium Series on Computational Intelligence, pp. 1431–1435. IEEE (2015)

    Google Scholar 

  16. Valdez, F., Chaparro, I.: Ant colony optimization for solving the TSP symmetric with parallel processing. In: Proceedings of Joint IFSA World Congress and NAFIPS Annual Meeting, pp. 1192–1196. IEEE (2013)

    Google Scholar 

  17. Salem, A., Sleit, A.: Analysis of ant colony optimization algorithm solutions for travelling salesman problem. Proc. Int. J. Sci. Eng. Res. 9(2) (2018)

    Google Scholar 

  18. Cao, J., Guojun, L., Shang, Y., Weng, N., Chang, C., Liu, Y.: An ensemble classifier based on feature selection using ant colony optimization. In: Proceedings of High Performance Extreme Computing Conference (HPEC). IEEE (2018). 978-1-5386-5989-2

    Google Scholar 

  19. Rajoo, R.R., Salam, R.A.: Ant colony optimization based subset feature selection in speech processing: constructing graphs with degree sequences. Proc. Int. J. Adv. Sci. Eng. Inf. Technol. 8(4–2), 1728 (2018)

    Article  Google Scholar 

  20. Crawford, B., Carlos, C., Monfroy, E.: An ant-based solver for subset problems. In: Proceedings of International Conference on Advances in Computing, Control, and Telecommunication Technologies, pp. 268–270. IEEE Xplore (2009)

    Google Scholar 

  21. Sharma, S., Buddhiraju, K.M.: A novel ant colony optimization based training subset selection algorithm for hyper spectral image classification. In: Proceedings of International Geosciences and Remote Sensing Symposium, pp. 5748–5751. IEEE (2018)

    Google Scholar 

  22. Fidanova, S., Atanassov, K., Marinov, P.: Start strategies of ACO applied on subset problems. In: Dimov, I., Dimova, S., Kolkovska, N. (eds.) Proceedings of International Conference on Numerical Methods and Applications, pp. 248–255. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  23. Abd-Alsabour, N.: Binary ant colony optimization for subset problems. In: Dehuri, S., Jagadev, A., Panda, M. (eds.) Multi-objective Swarm Intelligence. Studies in Computational Intelligence, vol. 592, pp. 105–121. Springer, Heidelberg (2015)

    Google Scholar 

  24. Nemati, S., Basiri, M.E., Ghasem-Aghaee, N., Aghdam, M.H.: A novel ACO–GA hybrid algorithm for feature selection in protein function prediction. Proc. Expert Syst. Appl. 36, 12086–12094 (2009)

    Article  Google Scholar 

  25. Jensen, R., Shen, Q.: Webpage classification with ACO-enhanced fuzzy-rough feature selection. In: Greco, S., et al. (eds.) RSCTC 2006. LNAI, vol. 4259, pp. 147–156. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  26. Changdar, C., Mahapatra, G.S., Pal, R.K.: Solving 0-1 knapsack problem by continuous ACO algorithm. Int. J. Comput. Intell. Stud. 2(3/4), 333 (2013)

    Article  Google Scholar 

  27. Chaharsooghi, S.K., Amir, H., Kermani, M.: Ant intelligent multi-colony multi-objective ant colony optimization (ACO) for the 0-1 knapsack problem. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp. 1195–1202 (2008)

    Google Scholar 

  28. Alzaqebah, A., Abu-Shareha, A.A.: Ant colony system algorithm with dynamic pheromone updating for 0/1 knapsack problem. Proc. Int. J. Intell. Syst. Appl. IJISA 11(2), 9–17 (2019)

    Google Scholar 

  29. Samanta, S., Chakraborty, S., Acharjee, S., Mukherjee, A., Dey, N.: Solving 0/1 knapsack problem using any weight lifting algorithm. In: IEEE International Conference on Computational Intelligence and Computing Research (2013)

    Google Scholar 

  30. Kumar, A., Rasool, A., Hajela, G.: Parallel ant colony algorithm for multi-dimensional 0-1 knapsack problem based on message passing interface (MPI). Proc. Int. J. Comput. Sci. Inf. Secur. (IJCSIS) 1(8), 613–620 (2016)

    Google Scholar 

  31. Fidanova, S., Atanassov, K., Marinov, P., Parvathi, R.: Ant colony optimization for multiple knapsacks problem with controlled starts. BIO-Automation 13(4), 271–280 (2009)

    Google Scholar 

  32. Bouamama, S., Blum, C., Fages, J.G.: An algorithm based on ant colony optimization for the minimum connected dominating set problem. Appl. Soft Comput. J. 80, 672–686 (2019)

    Article  Google Scholar 

  33. Mehrabi, A.D., Mehrabi, S., Mehrabi, A.: A pruning based ant colony algorithm for minimum vertex cover problem. In: International Joint Conference on Computational Intelligence, IJCCI, pp. 281–286 (2009)

    Google Scholar 

  34. Jovanovica, R., Tubab, M.: An ant colony optimization algorithm with improved pheromone correction strategy for the minimum weight vertex cover problem. J. Appl. Soft Comput. 11, 5360–5366 (2011)

    Article  Google Scholar 

  35. Chen, J., Kanj, I.A., Xia, G.: Improved parameterized upper bounds for vertex cover. In: Královič, R., Urzyczyn, P. (eds.) Mathematical Foundations of Computer Science 2006. LNCS, vol. 4162, pp. 238–249. Springer, Heidelberg (2006)

    Google Scholar 

  36. Shyu, S.J.: An ant colony optimization algorithm for the minimum weight vertex cover problem. Ann. Oper. Res. 131, 283–304 (2004)

    Article  MathSciNet  Google Scholar 

  37. Ni, X.: Optimization research of railway passenger transfer scheme based on ant colony algorithm. In: 6th International Conference on Computer-Aided Design Manufacturing Modeling and Simulation, API Conference Proceedings (2018)

    Google Scholar 

  38. Bouzbita, S., El Afia, A., Faizi, R.: Parameter adaptation for ant colony system algorithm using Hidden Markov Model for TSP problems. In: Proceedings of LOPAL Conference, pp. 2–5. ACM (2018)

    Google Scholar 

  39. Shetty, A., Shetty, A., Puthusseri, K.S., Shankaramani, R.: An improved ant colony optimization algorithm: Minion Ant (MAnt) and its application on TSP. In: Symposium Series on Computational Intelligence, SSCI. IEEE (2018). 978-1-5386-9276-9/2018

    Google Scholar 

  40. Qi, L., Yao, W., Chang, J.: A large scale transactional service selection approach based on skyline and ant colony optimization algorithm. Int. J. Technol. Eng. Stud. 4(3), 95–101 (2018)

    Google Scholar 

  41. Shemi, P.M., Jibukumar, M.G., Sabu, M.K.: A novel relay selection algorithm using ant colony optimization with artificial noise for secrecy enhancement in cooperative networks. Int. J. Commun. Syst. 31(14), 3739 (2018)

    Article  Google Scholar 

  42. Mansoura, I.B., Alayaa, I.: Indicator based ant colony optimization for multi-objective knapsack problem. In: International Conference on Knowledge Based and Intelligent Information and Engineering Systems (2015). Procedia Computer Science Vol. 60, pp. 448 – 457, Science Direct

    Article  Google Scholar 

  43. Tao, L.R., Jian, L.X.: MapReduce-based ant colony optimization algorithm for multi-dimensional knapsack problem. Appl. Mech. Mater. J. AMM 380-384, 1877–1880 (2013)

    Google Scholar 

  44. Yang, J., Shia, X., Marchese, M., Liang, Y.: An ant colony optimization method for generalized TSP problem. Prog. Nat. Sci. 18, 1417–1422 (2008)

    Article  MathSciNet  Google Scholar 

  45. Bhardwaj, G., Pandey, M.: Parallel implementation of travelling salesman problem using ant colony optimization. Int. J. Comput. Appl. Technol. Res. 3(6), 385–389 (2014)

    Google Scholar 

  46. Menezes, B.A.M., Kuchen, H., Amorim Neto, H.A., de Lima Neto, F.B.: Parallelization strategies for GPU-based ant colony optimization solving the traveling salesman problem. In: Proceedings of IEEE Congress on Evolutionary Computation (2019)

    Google Scholar 

  47. Fallahzadeh, O., Dehghani-Bidgoli, Z., Assarian, M.: Raman spectral feature selection using ant colony optimization for breast cancer diagnosis. Lasers Med. Sci. 33(8), 1799–1806 (2018)

    Article  Google Scholar 

  48. Naseer, A., Shahzad, W., Ellahi, A.: A hybrid approach for feature subset selection using ant colony optimization and multi-classifier ensemble. Int. J. Adv. Comput. Sci. Appl. IJACSA 9(1), 306–313 (2018)

    Google Scholar 

  49. Peng, H., Ying, C., Tan, S., Hu, B., Sun, Z.: An improved feature selection algorithm based on ant colony optimization. IEEE Access 6, 69203–69209 (2018)

    Article  Google Scholar 

  50. Iqbal, S., Bari, F.Md., Rahman, M.S.: A novel ACO technique for fast and near optimal solutions for the multi-dimensional multi-choice knapsack problem. In: 13th International Conference on Computer and Information Technology. IEEE Xplore (2010)

    Google Scholar 

  51. Zouari, W., Alaya, I., Tagina, M.: A hybrid ant colony algorithm with a local search for the strongly correlated knapsack problem. In: 14th International Conference on Computer Systems and Applications, pp. 527–533. IEEE (2017). IEEE Access

    Google Scholar 

  52. Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy (1992). (in Italian)

    Google Scholar 

  53. Dorigo, M., Blum, C.: Ant colony optimization theory: a survey. Theoret. Comput. Sci. 344(2–3), 243–278 (2005)

    Article  MathSciNet  Google Scholar 

  54. Dorigo, M., Caro, G.D.: The ant colony optimization meta-heuristi. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization. McGraw-Hill, New York (1999)

    Google Scholar 

  55. Dorigo, M., Caro, G.D., Gambardella, L.: Ant algorithms for discrete optimization. Artif. Life 5, 137–172 (1999)

    Article  Google Scholar 

  56. Dorigo, M., Gambardella, L.: Ant colony system: a cooperative learning approach to the travelling salesman problem. IEEE Trans. Evol. Comput. 1, 53–66 (1997)

    Article  Google Scholar 

  57. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. 26(1), 28–41 (1996)

    Article  Google Scholar 

  58. Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. 26, 29–41 (1996)

    Article  Google Scholar 

  59. Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information and Communication Technology, Fakir Mohan University, Vyasa Vihar, Balasore, 756019, Odisha, India

    Akshaya Kumar Mandal & Satchidananda Dehuri

Authors
  1. Akshaya Kumar Mandal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Satchidananda Dehuri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Akshaya Kumar Mandal .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Technology, Fakir Mohan University, Balasore, Odisha, India

    Satchidananda Dehuri

  2. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India

    Bhabani Shankar Prasad Mishra

  3. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India

    Pradeep Kumar Mallick

  4. Department of Computer Science, Yonsei University, Seoul, Korea (Republic of)

    Sung-Bae Cho

  5. Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

    Margarita N. Favorskaya

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mandal, A.K., Dehuri, S. (2020). A Survey on Ant Colony Optimization for Solving Some of the Selected NP-Hard Problem. In: Dehuri, S., Mishra, B., Mallick, P., Cho, SB., Favorskaya, M. (eds) Biologically Inspired Techniques in Many-Criteria Decision Making. BITMDM 2019. Learning and Analytics in Intelligent Systems, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-39033-4_9

Download citation

Publish with us

Policies and ethics

Search

Navigation