Skip to main content
SpringerLink
Log in

Explicit memory based ABC with a clustering strategy for updating and retrieval of memory in dynamic environments

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The Artificial Bee Colony (ABC) algorithm is considered as one of the swarm intelligence optimization algorithms. It has been extensively used for the applications of static type. Many practical and real-world applications, nevertheless, are of dynamic type. Thus, it is needed to employ some optimization algorithms that could solve this group of the problems that are of dynamic type. Dynamic optimization problems in which change(s) may occur through the time are tougher to face than static optimization problems. In this paper, an approach based on the ABC algorithm enriched with explicit memory and population clustering scheme, for solving dynamic optimization problems is proposed. The proposed algorithm uses the explicit memory to store the aging best solutions and employs clustering for preserving diversity in the population. Using the aging best solutions and keeping the diversity in population of the candidate solutions in the environment help speed-up the convergence of the algorithm. The proposed approach has been tested on Moving Peaks Benchmark. The Moving Peaks Benchmark is a suitable function for testing optimization algorithms and it is considered as one of the best representative of dynamic environments. The experimental study on the Moving Peaks Benchmark shows that the proposed approach is superior to several other well-known and state-of-the-art algorithms in dynamic environments.

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

Access this article

Log in via an institution

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

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

Similar content being viewed by others

References

  1. Branke J (1999) Memory enhanced evolutionary algorithms for changing optimization problems. Proc Congr Evol Comput 3:1875–1882

    Google Scholar 

  2. Yang S (2006) Associative memory scheme for genetic algorithms in dynamic environments. In: Proceedings of EvoWorkshops: Appl. Evol. Comput., LNCS 3907, pp 788–799

  3. Yang S, Yao X (2008) Population-based incremental learning with associative memory for dynamic environments. IEEE Trans Evol Comput 12(5):542–561

    Article  Google Scholar 

  4. Cobb HG, Grefenstette JJ (1993) Genetic algorithms for tracking changing environments. In: Proceedings of the 5th international conference on genetic algorithms, pp 523–530

  5. Grefenstette JJ (1992) Genetic algorithms for changing environments. In: Proceedings of the 2nd international conference on parallel problem solving from nature, pp 137–144

  6. Yang S (2008) Genetic algorithms with memory and elitism-based immigrants in dynamic environment. Evol Comput 16(3):385–416

    Article  Google Scholar 

  7. Ramsey CL, Grefenstette JJ (1993) Case-based initialization of genetic algorithms. In: Forrest S (ed) Proceedings of the fifth international conference on genetic algorithms. Morgan Kaufmann, pp 84–91

  8. Louis SJ, Xu Z (1996) Genetic algorithms for open shop scheduling and re-scheduling. In: Cohen ME, Hudson DL (eds) Proceedings of the eleventh international conference on computers and their applications (ISCA), pp 99–102

  9. Yang S, Tinos R (2007) A hybrid immigrants scheme for genetic algorithms in dynamic environments. Int J Autom Comput 3(4):243–254

    Article  Google Scholar 

  10. Goldberg DE, Smith RE (1987) Non-stationary function optimization using genetic algorithms with dominance and diploidy. In: Grefenstette JJ (ed) Proceedings of the second international conference on genetic algorithms (ICGA 1987). Lawrence Erlbaum Associates, pp 5968

  11. Ryan C (1997) Diploidy without dominance. In: Nordic workshop on genetic algorithms, pp 45–52

  12. Ryan C (1997) Dyploidy without dominance. In: Alander JT (ed) Proceedings of the nordic workshop on genetic algorithms, pp 63–70

  13. Lewis EHJ, Ritchie G (1998) A comparison of dominance mechanisms and simple mutation on non-stationary problems. In: Schoenauer M, Deb K, Rudolf G, Yao X, Lutton E, Merelo JJJ, Schwefel H-P (eds) Proceedings of the parallel problem solving from nature (PPSN V), vol 1917 of Lecture notes on computer science. Springer, pp 139–148

  14. Uyar AS, Harmanci AE (1999) Investigation of new operators for a diploid genetic algorithm. In: Proceedings of SPIE’s annual meeting

  15. Uyar AS, Harmanci AE (2005) A new population based adaptive dominance change mechanism for diploid genetic algorithms in dynamic environments. Soft Comput 9(11):803–814

    Article  Google Scholar 

  16. Yang S (2006) Dominance learning in diploid genetic algorithms for dynamic optimization problems. In: Keijzer M et al. (eds) Proceedings of the eighth international genetic and evolutionary computation. Conference (GECCO 2006). ACM Press, pp 1435–1436

  17. Yang S (2007) Explicit memory schemes for evolutionary algorithms in dynamic environments. In: Yang S, Ong Y-S, Jin Y (eds) Evolutionary computation in dynamic and uncertain environments, vol 51 of Studies in computational intelligence, pp 3–28

    Google Scholar 

  18. Ramsey CL, Grefenstette JJ (1993) Case-based initialization of genetic algorithms. In: Forrest S (ed) Proceedings of the fifth international conference on genetic algorithms. Morgan Kaufmann, pp 84–91

  19. Louis SJ, Xu Z (1996) Genetic algorithms for open shop scheduling and rescheduling. In: Cohen ME, Hudson DL (eds) Proceedings of the eleventh international conference on computers and their applications (ISCA), pp 99–102

  20. Trojanowski K, Michalewicz Z (1999) Searching for optima in non-stationary environments. In: Proceedings of the IEEE congress on evolutionary computation (CEC 1999). IEEE Press, pp 1843–1850

  21. Barlow GJ, Smith SF (2008) A memory enhanced evolutionary algorithm for dynamic scheduling problems. In: Springer (ed) Applications of evolutionary computing, vol 4974 of Lecture notes in computer science, pp 606–615

    Google Scholar 

  22. Ryan C (1997) Dyploidy without dominance. In: Alander JT (ed) Proceedings of the nordic workshop on genetic algorithms, pp 63–70

  23. Bird S, Li X (2007) Using regression to improve local convergence. In: Proceedings of congress on evolutionary computation, pp 592–599

  24. Karaboga D, Basturk B (2009) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471

    Article  MathSciNet  Google Scholar 

  25. Branke J (1999) Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the IEEE congress on evolutionary computation (CEC 1999). IEEE Press, pp 1875–1882

  26. Yang S, Li C (2009) A clustering particle swarm optimizer for dynamic optimization. In: Proceedings of congress on evolutionary computation, pp 439–446

  27. Blackwell T, Branke J, Li X (2008) Particle swarms for dynamic optimization problems. In: Swarm intelligence. Springer, Berlin, pp 193–217

    Google Scholar 

  28. Blackwell TM, Branke J (2006) Multiswarms, exclusion, and anticon vergence in dynamic environments. IEEE Trans Evol Comput 10(4):459–472

    Article  Google Scholar 

  29. Lung RI, Dumitrescu D (2010) Evolutionary swarm cooperative optimization in dynamic environments. Nat Comput 9(1):83–94

    Article  MathSciNet  Google Scholar 

  30. Lung RI, Dumitrescu D (2007) A collaborative model for tracking optima in dynamic environments. In: Proceedings of congress on evolutionary computation, pp 564–567

  31. Blackwell TM, Branke J (2006) Multiswarms, exclusion, and anticon vergence in dynamic environments. IEEE Trans Evol Comput 10(4):459–472

    Article  Google Scholar 

  32. Li X (2004) Adaptively choosing neighborhood bests using species in a particle swarm optimizer for multimodal function optimization. In: Proceedings of genetic and evolutionary computation conference, pp 105–116

    Chapter  Google Scholar 

  33. Yang S, Li C (2012) A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments. IEEE Trans 4:16

    Google Scholar 

  34. Liu L, Yang S, Wang D (2010) Particle swarm optimization with composite particles in dynamic environments. IEEE Trans Syst Man Cybern B Cybern 40(6):1634–1648

    Article  Google Scholar 

  35. Kamosi M, Hashemi AB, Meybodi MR (2010) A hibernating multiswarm optimization algorithm for dynamic environments. In: Proceedings of world congress on NaBIC, pp 363–369

  36. Woldesenbet YG, Yen GG (2009) Dynamic evolutionary algorithm with variable relocation. IEEE Trans Evol Comput 13(3):500–513

    Article  Google Scholar 

  37. Yang S, Li C (2008) Fast multi-swarm optimization for dynamic optimization problems. In: Proceedings of international conference on natural computation, vol 7, no 3, pp 624–628

  38. Hashemi B, Meybodi M (2009) Cellular PSO: a PSO for dynamic environments. In: Advances in computation and intelligence. Lecture notes in computer science, vol 5821, pp 422–433

    Chapter  Google Scholar 

  39. Wang H, Yang S, Ip WH, Wang D (2012) A memetic particle swarm optimization algorithm for dyanamic multi modal optimization problems. Int J Syst Sci 43(7):1268–1283

    Article  Google Scholar 

  40. Blackwell T, Branke J, Li X (2008) Particle swarms for dynamic optimization problems. In: Swarm S, Yang C, Li A (eds) Clustering particle swarm optimizer for locating and intelligence. Springer, Berlin, pp 193–217

  41. Yazdani D, Sepas-Moghaddam A, Dehban A, Horta N (2016) A novel approach for optimization in dynamic environments based on modified artificial fish swarm algorithm. Int J Comput Intell Appl 15(2):1650010 (23 pages)

    Article  Google Scholar 

  42. Nasiri B, Meybodi MR (2016) Improved speciation-based firefly algorithm in dynamic and uncertain environment. Int J Bio-Inspir Comput (in press)

  43. Kordestani JK, Rezvanian A, Meybodi MR (2014) CDEPSO: a bi-population hybrid approach for dynamic optimization problems. Appl Intell 40:682–694

    Article  Google Scholar 

  44. Yazdani D, Nasiri B, Sepas-Moghaddam A, Meybodi M, Akbarzadeh-Totonchi M (2014) MNAFSA: a novel approach for optimization in dynamic environments with global changes. Swarm Evolut Comput 18:38–53

    Article  Google Scholar 

  45. Shams KN, Abdullah S, Turky A, Kendall G (2016) An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems. In: Knowledge-based systems ⋅ 104 April 2016 with 138 reads. https://doi.org/10.1016/j.knosys.2016.04.005

    Article  Google Scholar 

  46. Mohammadpour M, Parvin H, Sina M (2018) Chaotic genetic algorithm based on explicit memory with a new strategy for updating and retrieval of memory in dynamic environments. J AI Data Min 6:191–205 (in press)

  47. Rezvanian A, Meybodi MR (2010) Tracking extrema in dynamic environments using a learning Automata-Based immune algorithm, grid and distributed computing. Control Autom 121:216–225

    MATH  Google Scholar 

  48. Xin Y, Ke T, Xin Y (2011) Immigrant schemes for evolutionary algorithms in dynamic environments: adapting the replacement rate. Science in China Series F - Information Sciences II:543–552

    Google Scholar 

  49. Baktash N, Mahmoudi F, Meybodi MR (2012) Cellular PSO-ABC: a new hybrid model for dynamic environment. Int J Comput Theory Eng 4(3):365–368

    Article  Google Scholar 

  50. Yang S (2007) Explicit memory schemes for evolutionary algorithms in dynamic environments.. In: Evolutionary computation in dynamic and uncertain environments, vol 51. Springer, Heidelberg, pp 3–28

    Google Scholar 

  51. Yazdani D, Akbarzadeh-Totonchi MR, Nasiri B, Meybodi MR (2012) A new artificial fish swarm algorithm for dynamic optimization problems. In: IEEE congress on evolutionary computation (CEC), pp 1–8

  52. Saxena N, Mishra KK (2017) Improved multi-objective particle swarm optimization algorithm for optimizing watermark strength in color image watermarking. Appl Intell 47(2):362–381

    Article  Google Scholar 

  53. Sharma B, Prakash R, Tiwari S, Mishra KK (2017) A variant of environmental adaptation method with real parameter encoding and its application in economic load dispatch problem. Appl Intell 47(2):409–429

    Article  Google Scholar 

  54. Tripathi A, Saxena N, Mishra KK, Misra AK (2017) A nature inspired hybrid optimisation algorithm for dynamic environment with real parameter encoding. IJBIC 10(1):24–32

    Article  Google Scholar 

Download references

Acknowledgements

We want to be thankful of Yasooj Branch, Islamic Azad University, Yasooj, Iran, for supporting this research.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran

    Hamid Parvin

  2. Young Researchers and Elite Club, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran

    Hamid Parvin

  3. Department of Electrical Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran

    Samad Nejatian

  4. Young Researchers and Elite Club, Yasooj Branch, Islamic Azad University, Yasooj, Iran

    Samad Nejatian & Majid Mohamadpour

  5. Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran

    Majid Mohamadpour

Authors
  1. Hamid Parvin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samad Nejatian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Majid Mohamadpour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Samad Nejatian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Parvin, H., Nejatian, S. & Mohamadpour, M. Explicit memory based ABC with a clustering strategy for updating and retrieval of memory in dynamic environments. Appl Intell 48, 4317–4337 (2018). https://doi.org/10.1007/s10489-018-1197-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-018-1197-z

Keywords

Search

Navigation