Usage of swarm intelligence strategies during projection of parallel neuroevolution methods for neuromodel synthesis

Authors

DOI:

https://doi.org/10.15587/2706-5448.2020.214769

Keywords:

neuroevolution method, genetic algorithm, swarm intelligence, parallel system, high-performance computing

Abstract

The paper proposes the ways to apply swarm intelligence strategies to parallelize neuroevolution methods for synthesizing artificial neural networks. The proposed approaches will solve a number of problems that usually arise during designing high-performance computing related to the synthesis of neural networks. The object of research is the process of developing a parallel approach for the neuroevolution synthesis of artificial neural networks, namely, the use of swarm intelligence strategies to solve a number of problems in designing a method that would use the resources of a parallel computer system.

One of the most problematic areas is the highly adaptive nature and significant operating time of neuroevolution methods. One way to solve these problems is to use parallel computer systems and distributed computing. However, a number of questions arise when designing a parallel neuroevolution method.

During research a number of tasks were solved, which included the analysis and study of neuroevolution methods for synthesizing artificial neural networks and problems of their parallelization. Attention is also paid to swarm intelligence methods, which have gained popularity recently and show good results.

The new method developed during the work was based on strategies for organizing work with swarm particles. Thus, sub-populations distributed between threads and individuals were analyzed as individual particles that interact with each other and depend on the local environment. Classical genetic operators were modified by criterion mechanisms to improve adaptability.

During the experiments, the developed method was compared with classical methods. During the work, special attention was paid not only to the characteristics of the resulting neuromodels, but also to the load on the processor during Operation. The developed method showed acceptable results for all comparisons. The new approach has significantly improved the quality level of the parallel neuroevolution synthesis method, allowing to evenly use the capabilities of computing nodes in a parallel system

Author Biographies

Serhii Leoshchenko, Zaporizhzhia Polytechnic National University, 64, Zhukovskogo str., Zaporizhzhia, Ukraine, 69063

PhD student

Department of Software Tools

Andrii Oliinyk, Zaporizhzhia Polytechnic National University, 64, Zhukovskogo str., Zaporizhzhia, Ukraine, 69063

PhD, Associate Professor

Department of Software Tools

Sergey Subbotin, Zaporizhzhia Polytechnic National University, 64, Zhukovskogo str., Zaporizhzhia, Ukraine, 69063

Doctor of Technical Sciences, Professor, Head of Department

Department of Software Tools

Tetiana Zaiko, Zaporizhzhia Polytechnic National University, 64, Zhukovskogo str., Zaporizhzhia, Ukraine, 69063

PhD, Associate Professor

Department of Software Tools

References

  1. Albrigtsen, S. I., Imenes, A., Goodwin, M., Jiao, L., Nunavath, V.; Pimenidis, E., Jayne, C. (Eds.) (2018). Neuroevolution of Actively Controlled Virtual Characters – An Experiment for an Eight-Legged Character. Engineering Applications of Neural Networks. EANN 2018. Communications in Computer and Information Science. Vol. 893. Cham: Springer, 94–105. doi: http://doi.org/10.1007/978-3-319-98204-5_8
  2. Bohrer, J., Grisci, B., Dorn, M. (2020). Neuroevolution of Neural Network Architectures Using CoDeepNEAT and Keras Computer Science. Arxiv. Available at: https://arxiv.org/abs/2002.04634
  3. Bergel, A. (2020). Neuroevolution. Agile Artificial Intelligence in Pharo. Berkeley: Apress, 283–294. doi: http://doi.org/10.1007/978-1-4842-5384-7_14
  4. Mason, K., Duggan, J., Howley, E. (2018). Watershed management using neuroevolution. Modeling Earth Systems and Environment, 4 (4), 1445–1448. doi: https://doi.org/10.1007/s40808-018-0508-z
  5. Arellano, W. R., Silva, P. A., Molina, M. F., Ronquillo, S., Ortega-Zamorano, F.; Rojas, I., Joya, G., Catala, A. (Eds.). (2019). Red-Black Tree Based NeuroEvolution of Augmenting Topologies. Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science. Vol. 11507. Cham: Springer, 678–686. doi: http://doi.org/10.1007/978-3-030-20518-8_56
  6. Gonçalves, I., Seca, M., Castelli, M.; Banzhaf, W., Goodman, E., Sheneman, L., Trujillo, L., Worzel, B. (Eds.) (2020). Explorations of the Semantic Learning Machine Neuroevolution Algorithm: Dynamic Training Data Use, Ensemble Construction Methods, and Deep Learning Perspectives. Genetic Programming Theory and Practice XVII. Genetic and Evolutionary Computation. Cham: Springer, 39–62. doi: http://doi.org/10.1007/978-3-030-39958-0_3
  7. Hoseini Alinodehi, S. P., Moshfe, S., Saber Zaeimian, M., Khoei, A., Hadidi, K. (2016). High-Speed General Purpose Genetic Algorithm Processor. IEEE Transactions on Cybernetics, 46 (7), 1551–1565. doi: http://doi.org/10.1109/tcyb.2015.2451595
  8. Hou, N., He, F., Zhou, Y., Chen, Y., Yan, X. (2018). A Parallel Genetic Algorithm With Dispersion Correction for HW/SW Partitioning on Multi-Core CPU and Many-Core GPU. IEEE Access, 6, 883–898. doi: http://doi.org/10.1109/access.2017.2776295
  9. Cleghorn, C. W., Engelbrecht, A. P. (2017). Particle swarm stability: a theoretical extension using the non-stagnate distribution assumption. Swarm Intelligence, 12 (1), 1–22. doi: http://doi.org/10.1007/s11721-017-0141-x
  10. Nobile, M. S., Cazzaniga, P., Besozzi, D., Colombo, R., Mauri, G., Pasi, G. (2018). Fuzzy Self-Tuning PSO: A settings-free algorithm for global optimization. Swarm and Evolutionary Computation, 39, 70–85. doi: http://doi.org/10.1016/j.swevo.2017.09.001
  11. Kim, M.-A., Park, J. S., Lee, C. W., Choi, W.-I. (2019). Pneumonia severity index in viral community acquired pneumonia in adults. PLOS ONE, 14 (3), e0210102. doi: http://doi.org/10.1371/journal.pone.0210102
  12. Nugroho, E. D., Wibowo, M. E., Pulungan, R. (2017). Parallel implementation of genetic algorithm for searching optimal parameters of artificial neural networks. 3rd International Conference on Science and Technology – Computer (ICST). Yogyakarta, 136–141. doi: http://doi.org/10.1109/icstc.2017.8011867
  13. Parallel NEAT. Python Encyclopedia. Available at: https://neat-python.readthedocs.io/en/latest/_modules/parallel.html

Downloads

Published

2020-10-31

How to Cite

Leoshchenko, S., Oliinyk, A., Subbotin, S., & Zaiko, T. (2020). Usage of swarm intelligence strategies during projection of parallel neuroevolution methods for neuromodel synthesis. Technology Audit and Production Reserves, 5(2(55), 12–17. https://doi.org/10.15587/2706-5448.2020.214769

Issue

Vol. 5 No. 2(55) (2020): Information and control systems

Section

Information Technologies: Original Research

License

Copyright (c) 2020 Serhii Leoshchenko, Andrii Oliinyk, Sergey Subbotin, Tetiana Zaiko

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.