Skip to main content
SpringerLink
Log in

Multi-population-based Algorithms with Different Migration Topologies and Their Improvement by Population Re-initialization

  • Conference paper
  • First Online:
Artificial Intelligence and Soft Computing (ICAISC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14125))

Included in the following conference series:

Abstract

In this paper, the possibilities of improving the performance of multi-population-based algorithms were tested. In the proposed approach, it was decided to test various methods and parameters of the population re-initialization mechanism, aimed at improving the diversity of individuals and preventing premature convergence, which is associated with a possible improvement in obtained results. In addition to the standard approach with random re-initialization of a new population, it was decided to test an approach in which selected populations are initialized with the use of modified individuals from better-performing populations. This approach has not been thoroughly tested so far, in particular for many different migration topologies and different population-based algorithms. The presented approach was specifically tested for the MNIA algorithm, eliminating the need to select one specific algorithm for the optimization. The simulations were performed for typical benchmark functions. The results of the simulations allow us to conclude that the proposed approach, depending on the parameters, improved the optimization process.

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 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Akhmedova, S., Stanovov, V., Semenkin, E.: Soft island model for population-based optimization algorithms. In: Tan, Y., Shi, Y., Tang, Q. (eds.) ICSI 2018. LNCS, vol. 10941, pp. 68–77. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93815-8_8

    Chapter  Google Scholar 

  2. Akhtar, T., Shoemaker, C.A.: Efficient multi-objective optimization through population-based parallel surrogate search. arXiv preprint arXiv:1903.02167 (2019)

  3. Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE Congress on Evolutionary Computation, pp. 4661–4667 (2007)

    Google Scholar 

  4. Bilski, J., Smola̧g, J., Kowalczyk, B., Grzanek, K., Izonin, I.: Fast computational approach to the Levenberg-Marquardt algorithm for training feedforward neural networks. J. Artif. Intell. Soft Comput. Res. 13(2), 45–61 (2023)

    Google Scholar 

  5. Birbil, Şİ, Fang, S.C., Sheu, R.L.: On the convergence of a population-based global optimization algorithm. J. Global Optim. 30, 301–318 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  6. Chen, M.R., Zeng, G.Q., Lu, K.D.: Constrained multi-objective population extremal optimization based economic-emission dispatch incorporating renewable energy resources. Renewable Energy 143, 277–294 (2019)

    Article  Google Scholar 

  7. Cpałka, K., Łapa, K., Rutkowski, L.: A multi-population-based algorithm with different ways of subpopulations cooperation. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2022. LNCS, vol. 13588, pp. 205–218. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-23492-7_18

    Chapter  Google Scholar 

  8. Cui, H., Li, X., Gao, L.: An improved multi-population genetic algorithm with a greedy job insertion inter-factory neighborhood structure for distributed heterogeneous hybrid flow shop scheduling problem. Expert Syst. Appl. 222, 119805 (2023)

    Article  Google Scholar 

  9. Dang, D.C., Eremeev, A., Lehre, P.K.: Escaping local optima with non-elitist evolutionary algorithms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 14, pp. 12275–12283 (2021)

    Google Scholar 

  10. Dziwiński, P., Przybył, A., Trippner, P., Paszkowski, J., Hayashi, Y.: hardware implementation of a Takagi-Sugeno neuro-fuzzy system optimized by a population algorithm. J. Artif. Intell. Soft Comput. Res. 11(3), 243–266 (2021)

    Article  Google Scholar 

  11. Feoktistov, V.: Differential Evolution, pp. 1–24. Springer, New York (2006). https://doi.org/10.1007/978-0-387-36896-2

    Book  MATH  Google Scholar 

  12. Fernandes, C.M., Rosa, A.C., Laredo, J.L., Merelo, J.J., Cotta, C.: Dynamic models of partially connected topologies for population-based metaheuristics. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2018)

    Google Scholar 

  13. Gabryel, M., Lada, D., Filutowicz, Z., Patora-Wysocka, Z., Kisiel-Dorohinicki, M., Chen, G.: Detecting anomalies in advertising web traffic with the use of the variational autoencoder. J. Artif. Intell. Soft Comput. Res. 12(4), 255–256 (2022)

    Article  Google Scholar 

  14. Gupta, A., Lanctot, M., Lazaridou, A.: Dynamic population-based meta-learning for multi-agent communication with natural language. Adv. Neural. Inf. Process. Syst. 34, 16899–16912 (2021)

    Google Scholar 

  15. Harrison, K.R., Engelbrecht, A.P., Ombuki-Berman, B.M.: Self-adaptive particle swarm optimization: a review and analysis of convergence. Swarm Intell. 12, 187–226 (2018)

    Article  Google Scholar 

  16. Holly, S., Nieße, A.: Dynamic communication topologies for distributed heuristics in energy system optimization algorithms, pp. 191–200 (2021)

    Google Scholar 

  17. Karaboga, D., Aslan, S.: A new emigrant creation strategy for parallel artificial bee colony algorithm. In: 9th International Conference on Electrical and Electronics Engineering (ELECO), pp. 689–694 (2015). https://doi.org/10.1109/eleco.2015.7394477

  18. Kazikova, A., Pluhacek, M., Senkerik, R., Viktorin, A.: Proposal of a new swarm optimization method inspired in bison behavior. In: Matoušek, R. (ed.) MENDEL 2017. AISC, vol. 837, pp. 146–156. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-97888-8_13

    Chapter  Google Scholar 

  19. Kavoosi, M., Dulebenets, M. A., Mikijeljević, M.: A universal island-based metaheuristic for effective berth scheduling. In: XXIII International Conference on Material Handling, Constructions and Logistics (MHCL 2019) (2019)

    Google Scholar 

  20. Kumar, D., Sharma, D.: Feature map augmentation to improve scale invariance in convolutional neural networks. J. Artif. Intell. Soft Comput. Res. 13(1), 51–74 (2023)

    Article  Google Scholar 

  21. Kupfer, E., Le, H.T., Zitt, J., Lin, Y.C., Middendorf, M.: A hierarchical simple probabilistic population-based algorithm applied to the dynamic TSP. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8. IEEE (2021)

    Google Scholar 

  22. Laktionov, I., Vovna, O., Kabanets, M.: Information technology for comprehensive monitoring and control of the microclimate in industrial greenhouses based on fuzzy logic. J. Artif. Intell. Soft Comput. Res. 13(1), 19–35 (2023)

    Article  Google Scholar 

  23. Lambora, A., Gupta, K., Chopra, K.: Genetic algorithm-a literature review. In: 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), pp. 380–384. IEEE (2019)

    Google Scholar 

  24. Li, C., Nguyen, T.T., Yang, M., Yang, S., Zeng, S.: Multi-population methods in unconstrained continuous dynamic environments: the challenges. Inf. Sci. 296, 95–118 (2015)

    Article  Google Scholar 

  25. Liang, J.J., Qu, B.Y., Suganthan, P.N., Hernández-Díaz, A.G.: Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical report 201212(34), pp. 281–295 (2013)

    Google Scholar 

  26. Lynn, N., Ali, M.Z., Suganthan, P.N.: Population topologies for particle swarm optimization and differential evolution. Swarm Evol. Comput. 39, 24–35 (2018)

    Article  Google Scholar 

  27. Łapa, K., Cpałka, K., Kisiel-Dorohinicki, M., Paszkowski, J., Debski, M., Le, V.H.: Multi-population-based algorithm with an exchange of training plans based on population evaluation. J. Artif. Intell. Soft Comput. Res. 12(4), 239–253 (2022)

    Article  Google Scholar 

  28. Łapa, K., Cpałka, K., Laskowski, Ł, Cader, A., Zeng, Z.: Evolutionary algorithm with a configurable search mechanism. J. Artif. Intell. Soft Comput. Res. 10(3), 151–171 (2020)

    Article  Google Scholar 

  29. Ma, X., et al.: A survey on cooperative co-evolutionary algorithms. IEEE Trans. Evol. Comput. 23(3), 421–441 (2018)

    Article  Google Scholar 

  30. Ma, H., Shen, S., Yu, M., Yang, Z., Fei, M., Zhou, H.: Multi-population techniques in nature inspired optimization algorithms: a comprehensive survey. Swarm Evol. Comput. 44, 365–387 (2019)

    Article  Google Scholar 

  31. Medina, A., Tosca P.G., Ramírez-Torres, J.: A Comparative Study of Neighborhood Topologies for Particle Swarm Optimizers, pp. 152–159 (2009)

    Google Scholar 

  32. Meng, Q., Wu, J., Ellis, J., Kennedy, P.J.: Dynamic island model based on spectral clustering in genetic algorithm. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1724–1731. IEEE (2017)

    Google Scholar 

  33. Ming, M., Trivedi, A., Wang, R., Srinivasan, D., Zhang, T.: A dual-population-based evolutionary algorithm for constrained multiobjective optimization. IEEE Trans. Evol. Comput. 25(4), 739–753 (2021)

    Article  Google Scholar 

  34. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  35. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Article  Google Scholar 

  36. Mishra, D.K., Shinde, V., Bharadwaj, S.K.: A convergence study of firefly algorithm. Int. J. Res. Sci. Eng. (IJRISE) 2(03), 17–25 (2022). ISSN 2394-8299

    Article  Google Scholar 

  37. Mousavirad, S.J., Schaefer, G., Jalali, S.M.J., Korovin, I.: A benchmark of recent population-based metaheuristic algorithms for multi-layer neural network training. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pp. 1402–1408 (2020)

    Google Scholar 

  38. Najmeh, S.J., Salwani, A., Abdul, R.H.: Multi-population cooperative bat algorithm-based optimization of artificial neural network model. Inf. Sci. 294, 628–644 (2015)

    Article  MathSciNet  Google Scholar 

  39. Osaba, E., Diaz, F., Onieva, E.: Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Appl. Intell. 41, 145–166 (2014)

    Article  Google Scholar 

  40. Pawłowska, J., Rydzewska, K., Wierzbicki, A.: Using cognitive models to understand and counteract the effect of self-induced bias on recommendation algorithms. J. Artif. Intell. Soft Comput. Res. 13(2), 73–94 (2023)

    Article  Google Scholar 

  41. Price, D., Radaideh, M.I.: Animorphic ensemble optimization: a large-scale island model. Neural Comput. Appl. 35(4), 3221–3243 (2023)

    Article  Google Scholar 

  42. Sahu, A., Panigrahi, S.K., Pattnaik, S.: Fast convergence particle swarm optimization for functions optimization. Procedia Technol. 4, 319–324 (2012)

    Article  Google Scholar 

  43. Sanu, M., Jeyakumar, G.: Empirical performance analysis of distributed differential evolution for varying migration topologies. Int. J. Appl. Eng. Res. 10, 11919–11932 (2015)

    Google Scholar 

  44. Skakovski, A., Jȩdrzejowicz, P.: A multisize no migration island-based differential evolution algorithm with removal of ineffective islands. IEEE Access 10, 34539–34549 (2022)

    Article  Google Scholar 

  45. Słowik, A., Cpałka, K.: Guest editorial: hybrid approaches to nature-inspired population-based intelligent optimization for industrial applications. IEEE Trans. Industr. Inf. 18(1), 542–545 (2021)

    Article  Google Scholar 

  46. Słowik, A., Cpałka, K., Łapa, K.: Multipopulation nature-inspired algorithm (MNIA) for the designing of interpretable fuzzy systems. IEEE Trans. Fuzzy Syst. 28(6), 1125–1139 (2019)

    Article  Google Scholar 

  47. Szczypta, J., Przybył, A., Cpałka, K.: Some aspects of evolutionary designing optimal controllers. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS (LNAI), vol. 7895, pp. 91–100. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38610-7_9

    Chapter  Google Scholar 

  48. Thiruvady, D., Nguyen, S., Shiri, F., Zaidi, N., Li, X.: Surrogate-assisted population based ACO for resource constrained job scheduling with uncertainty. Swarm Evol. Comput. 69, 101029 (2022)

    Article  Google Scholar 

  49. Turky, A.M., Abdullah, S.: A multi-population harmony search algorithm with external archive for dynamic optimization problems. Inf. Sci. 272, 84–95 (2014)

    Article  Google Scholar 

  50. Wang, H., Zuo, L.L., Liu, J., Yi, W.J., Niu, B.: Ensemble particle swarm optimization and differential evolution with alternative mutation method. Nat. Comput. 19, 699–712 (2020)

    Article  MathSciNet  Google Scholar 

  51. Wright, L.G., et al.: Deep physical neural networks trained with backpropagation. Nature 601(7894), 549–555 (2022)

    Article  Google Scholar 

  52. Wu, G., Mallipeddi, R., Suganthan, P.N.: Ensemble strategies for population-based optimization algorithms-a survey. Swarm Evol. Comput. 44, 695–711 (2019)

    Article  Google Scholar 

  53. Vafashoar, R., Meybodi, M.R.: A multi-population differential evolution algorithm based on cellular learning automata and evolutionary context information for optimization in dynamic environments. Appl. Soft Comput. 88, 106009 (2020)

    Article  Google Scholar 

  54. Vlachas, P.R., et al.: Backpropagation algorithms and reservoir computing in recurrent neural networks for the forecasting of complex spatiotemporal dynamics. Neural Netw. 126, 191–217 (2020)

    Article  Google Scholar 

  55. Xiao, L., Zuo, X.: Multi-DEPSO: a DE and PSO based hybrid algorithm in dynamic environments. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1–7. IEEE (2012)

    Google Scholar 

  56. Xu, Y., et al.: A multi-population multi-objective evolutionary algorithm based on the contribution of decision variables to objectives for large-scale multi/many-objective optimization. IEEE Trans. Cybern. (2022)

    Google Scholar 

  57. Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 210–214. IEEE (2009)

    Google Scholar 

  58. Zalasiński, M., Cpałka, K., Hayashi, Y.: New fast algorithm for the dynamic signature verification using global features values. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS (LNAI), vol. 9120, pp. 175–188. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19369-4_17

    Chapter  Google Scholar 

  59. Zalasiński, M., Cpałka, K., Przybyszewski, K., Yen, G.G.: On-line signature partitioning using a population based algorithm. J. Artif. Intell. Soft Comput. Res. 10(1), 5–13 (2020)

    Article  Google Scholar 

  60. Zalasinski, M., Cpalka, K., Laskowski, L., Wunsch, D.C., Przybyszewski, K.: An algorithm for the evolutionary-fuzzy generation of on-line signature hybrid descriptors (2020)

    Google Scholar 

  61. Zalasiński, M., et al.: Evolutionary algorithm for selecting dynamic signatures partitioning approach. J. Artif. Intell. Soft Comput. Res. 12(4), 267–279 (2022)

    Article  Google Scholar 

  62. Zhou, Y., Li, S., Pedrycz, W., Feng, G.: ACDB-EA: adaptive convergence-diversity balanced evolutionary algorithm for many-objective optimization. Swarm Evol. Comput. 75, 101145 (2022)

    Article  Google Scholar 

Download references

Acknowledgement

The project financed under the program of the Polish Minister of Science and Higher Education under the name “Regional Initiative of Excellence” in the years 2019-2023 project number 020/RID/2018/19 the amount of financing PLN 12,000,000.

Author information

Authors and Affiliations

  1. Czȩstochowa University of Technology, 42-201, Czȩstochowa, Poland

    Krystian Łapa

Authors
  1. Krystian Łapa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Krystian Łapa .

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Krakow, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Łapa, K. (2023). Multi-population-based Algorithms with Different Migration Topologies and Their Improvement by Population Re-initialization. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2023. Lecture Notes in Computer Science(), vol 14125. Springer, Cham. https://doi.org/10.1007/978-3-031-42505-9_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-42505-9_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42504-2

  • Online ISBN: 978-3-031-42505-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation