Skip to main content
Log in

On the impact of information-sharing model between subpopulations in the Island-based evolutionary algorithms: search manager framework as a case study

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The island model is an effective alternative to implement a standalone, hybrid, or parallel evolutionary algorithm that has been focused in the last decade. To make this model more efficient, several important issues exist that should be considered. One of them is the information-sharing strategy between subpopulations that its effect on the performance of an island-based evolutionary algorithm has not been considered in the literature. Most of the studies utilize just the migration model without any assumption. In this study, we investigate three different information-sharing models on one of the recently proposed island-based hybridization frameworks, called Search Manager, and practically show why the migration model has been adopted in most of the island-based evolutionary algorithms. The obtained results on CEC 2005 benchmark suite show that although the migration model is a good choice, it is hard to claim that it is the most suitable one for an island-based algorithm. In fact, there is no global information-sharing model and which one improves the performance of an island-based algorithm depends on the search strategy and the optimization problem.

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

Similar content being viewed by others

Notes

  1. All the implemented source code is available on the GitHub: https://github.com/yousefabdi/MSM.

References

  1. Rocke DM (2000) Genetic algorithms+ data structures = evolution programs 3rd. J Am Stat Assoc 95(449):347

    Article  Google Scholar 

  2. Banzhaf W, Nordin P, Keller RE, Francone FD (1998). Genetic programming: an introduction: on the automatic evolution of computer programs and its applications. Morgan Kaufmann Publishers Inc.

  3. Rechenberg I (1973) Evolution strategy: optimization of technical systems by means of biological evolution. Fromman-Holzboog, Stuttgart 104:15–16

    Google Scholar 

  4. Pant M, Zaheer H, Garcia-Hernandez L, Abraham A (2020) Differential Evolution: a review of more than two decades of research. Eng Appl Artif Intell 90:103479

    Article  Google Scholar 

  5. Jain M, Saihjpal V, Singh N, Singh SB (2022) An overview of variants and advancements of PSO algorithm. Appl Sci 12(17):8392

    Article  Google Scholar 

  6. Dorigo M, Stützle T (2019) Ant colony optimization: overview and recent advances. Handbook of metaheuristics. Springer International Publishing, Cham, pp 311–351

    Chapter  Google Scholar 

  7. Rao RV (2016) Teaching-learning-based optimization algorithm. Teaching learning based optimization algorithm. Springer, Cham, pp 9–39

    Chapter  Google Scholar 

  8. Mirjalili S, Mirjalili SM, Saremi S, Mirjalili S (2020) Whale optimization algorithm: theory, literature review, and application in designing photonic crystal filters. Springer International Publishing, Cham, Nature-Inspired Optimizers, pp 219–238

    Google Scholar 

  9. Dash CSK, Saran A, Sahoo P, Dehuri S, Cho SB (2016) Design of self-adaptive and equilibrium differential evolution optimized radial basis function neural network classifier for imputed database. Pattern Recogn Lett 80:76–83

    Article  Google Scholar 

  10. Abdi Y, Feizi-Derakhshi MR (2020) Hybrid multi-objective evolutionary algorithm based on search manager framework for big data optimization problems. Appl Soft Comput 87:105991. https://doi.org/10.1016/j.asoc.2019.105991

    Article  Google Scholar 

  11. Elsayed SM, Sarker RA, Essam DL (2011) Multi-operator based evolutionary algorithms for solving constrained optimization problems. Comput Oper Res 38(12):1877–1896

    Article  MathSciNet  MATH  Google Scholar 

  12. Elsayed SM, Sarker RA, Essam DL, Hamza NM (2014) Testing united multi-operator evolutionary algorithms on the CEC2014 real-parameter numerical optimization. In 2014 IEEE congress on evolutionary computation (CEC) (pp 1650–1657). IEEE

  13. Abadlia H, Smairi N, Ghedira K (2018) A hybrid Immigrants schema for particle swarm optimization algorithm. Procedia Comput Sci 126:105–115

    Article  Google Scholar 

  14. Abadlia, H, Smairi N, Ghedira K (2017) Particle swarm optimization based on dynamic island model. In 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI) (pp 709–716). IEEE

  15. Alba E, Tomassini M (2002) Parallelism and evolutionary algorithms. IEEE Trans Evol Comput 6(5):443–462

    Article  Google Scholar 

  16. Eiben AE, Smith JE (2015) Introduction to evolutionary computing. Springer-Verlag, Berlin Heidelberg

    Book  MATH  Google Scholar 

  17. Hodashinsky IA (2021) Methods for improving the efficiency of swarm optimization algorithms. Surv Autom Remote Control 82(6):935–967

    Article  MathSciNet  MATH  Google Scholar 

  18. Talbi EG (2002) A taxonomy of hybrid metaheuristics. J Heuristics 8(5):541–564. https://doi.org/10.1023/A:1016540724870

    Article  Google Scholar 

  19. Sato M, Fukuyama Y, Iizaka T, Matsui T (2019) Total optimization of energy networks in a smart city by multi-population global-best modified brain storm optimization with migration. Algorithms 12(1):15

    Article  MathSciNet  MATH  Google Scholar 

  20. Skolicki Z, De Jong K (2004) Improving evolutionary algorithms with multi-representation island models. In: Yao X et al (eds) Parallel problem solving from nature, vol 3242. Lecture Notes in Computer Science. Springer, pp 420–429

    Google Scholar 

  21. Ruciński M, Izzo D, Biscani F (2010) On the impact of the migration topology on the island model. Parallel Comput 36(10–11):555–571

    Article  MATH  Google Scholar 

  22. Del Ser J, Osaba E, Molina D, Yang XS, Salcedo-Sanz S, Camacho D, Das S, Suganthan PN, Coello CA, Coello FH (2019) Bio-inspired computation: where we stand and what’s next. Swarm Evolut Comput 48:220–250

    Article  Google Scholar 

  23. Skakovski A, Jędrzejowicz P (2019) An island-based differential evolution algorithm with the multi-size populations. Expert Syst Appl 126:308–320

    Article  Google Scholar 

  24. Al-Betar MA, Awadallah MA, Khader AT, Abdalkareem ZA (2015) Island-based harmony search for optimization problems. Expert Syst Appl 42(4):2026–2035

    Article  Google Scholar 

  25. Li C, Yang S (2008) An island based hybrid evolutionary algorithm for optimization. In: Asia-Pacific Conference on Simulated Evolution and Learning. Springer, Berlin, pp 180–189

    Chapter  Google Scholar 

  26. Abed-alguni BH, Barhoush M (2018) Distributed grey wolf optimizer for numerical optimization problems. Jordan J Comput Inf Tech (JJCIT) 4(03):21

    Google Scholar 

  27. Turgut MS, Turgut OE, Eliiyi DT (2020) Island-based crow search algorithm for solving optimal control problems. Appl Soft Comput 90:106170

    Article  Google Scholar 

  28. Abdi Y, Seyfari Y (2018) Search manager: a framework for hybridizing different search strategies. Int J Adv Comput Sci Appl 9:525–540

    Google Scholar 

  29. Yazawa K, Tamura K, Yasuda K, Motoki M, Ishigame A (2011) Cluster-structured particle swarm optimization with interaction and adaptation. Electron Commun Jpn 94(11):9–17

    Article  Google Scholar 

  30. Nalepa J, Blocho M (2015) Co-operation in the parallel memetic algorithm. Int J Parallel Prog 43(5):812–839

    Article  Google Scholar 

  31. Bruhn JG (1997) The organization as a person: analogues for intervention. Clin Sociol Rev 15(1):7

    MathSciNet  Google Scholar 

  32. Lu C, Gao L, Yi J (2018) Grey wolf optimizer with cellular topological structure. Expert Syst Appl 107:89–114

    Article  Google Scholar 

  33. Luque G, Alba E (2010). Selection pressure and takeover time of distributed evolutionary algorithms. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation (pp 1083–1088)

  34. Lässig J, Sudholt D (2013) Design and analysis of migration in parallel evolutionary algorithms. Soft Comput 17(7):1121–1144

    Article  MATH  Google Scholar 

  35. Fernandez F, Tomassini M, Vanneschi L (2003) An empirical study of multipopulation genetic programming. Genet Program Evolvable Mach 4(1):21–51

    Article  MATH  Google Scholar 

  36. Tomassini M. (2005) Spatially structured evolutionary algorithms: artificial evolution in space and time. Springer Science & Business Media

  37. Lardeux F, Goëffon A (2010) A dynamic island-based genetic algorithms framework. In: Simulated Evolution and Learning: 8th International Conference, SEAL 2010, Kanpur, India, December 1-4, 2010. Proceedings 8 (pp 156–165). Springer Berlin Heidelberg

  38. Al-Betar MA, Awadallah MA (2018) Island bat algorithm for optimization. Expert Syst Appl 107:126–145. https://doi.org/10.1016/j.eswa.2018.04.024

    Article  Google Scholar 

  39. Ono K, Hanada Y, Kumano M, Kimura M (2013) Island model genetic programming based on frequent trees. In 2013 IEEE congress on evolutionary computation (pp 2988–2995), IEEE, Doi: https://doi.org/10.1109/CEC.2013.6557933

  40. Kushida JI, Hara A, Takahama T, Kido A (2013) Island-based differential evolution with varying subpopulation size. In 2013 IEEE 6th international workshop on computational intelligence and applications (IWCIA) (pp 119–124). IEEE

  41. Munoz MA, Kirley M, Halgamuge SK (2013) The algorithm selection problem on the continuous optimization domain. Computational intelligence in intelligent data analysis. Springer, Berlin Heidelberg, pp 75–89

    Chapter  Google Scholar 

  42. Alissa M, Sim K, Hart E (2023) Automated algorithm selection: from feature-based to feature-free approaches. J Heuristics 29:1–38

    Article  Google Scholar 

  43. Kerschke P, Hoos HH, Neumann F, Trautmann H (2019) Automated algorithm selection: survey and perspectives. Evol Comput 27(1):3–45

    Article  Google Scholar 

  44. Wilcoxon F (1992) Individual comparisons by ranking methods. In: Kotz S, Johnson NL (eds) Breakthroughs in statistics, Springer series in statistics. Springer, New York

    Google Scholar 

  45. Abadlia H, Smairi N, Ghedira K (2017) Particle swarm optimization based on dynamic island model. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI) (pp 709–716). IEEE

  46. Attia MA, Arafa M, Sallam EA, Fahmy MM (2019) An enhanced differential evolution algorithm with multi-mutation strategies and self-adapting control parameters. Int J Intell Syst Appl 10(4):26

    Google Scholar 

  47. Al-Betar MA, Khader AT, Awadallah MA, Alawan MH, Zaqaibeh B (2013) Cellular harmony search for optimization problems. J Appl Math 2013:1–20

    Article  MathSciNet  Google Scholar 

  48. Balande U, Shrimankar D (2019) SRIFA: stochastic ranking with improved-firefly-algorithm for constrained optimization engineering design problems. Mathematics 7(3):250

    Article  Google Scholar 

Download references

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

Authors

Contributions

YA contributed to methodology, conceptualization, writing—original draft, designed the experiment, and provided software. MA was supervisor and involved in investigation.

Corresponding author

Correspondence to Mohammad Asadpour.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdi, Y., Asadpour, M. On the impact of information-sharing model between subpopulations in the Island-based evolutionary algorithms: search manager framework as a case study. J Supercomput 79, 14245–14286 (2023). https://doi.org/10.1007/s11227-023-05218-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-023-05218-y

Keywords

Navigation