Genetic algorithm and particle swarm applied in electric system optimization




Genetic Algorithm; Particle Swarm; Parallel Computing; Optimization; Electrical network.


This paper aims to present and run a composite model using Genetic Algorithm (GA) and Particle Swarm (PSO), with the assistance of parallel computing methods, to optimize the electrical distribution in a power grid based on an IEEE 14-bus system. The mathematical-computational modeling allows using the objective function to analyze the cost in relation to power or voltage as independent variables, and it is the bridge for the connection between the 2 implemented algorithms. The results presented in this article demonstrate that the methodology was implemented splendidly, in addition to obtaining an excellent computational cost and complying with the physical restrictions of network security, it also achieved global solutions in its optimization.


Costa, H. A. de O., Costa, D. C. L. & Meneses, L. A. de. (2021) Interdisciplinarity Applied to the Optimized Dispatch of Integrated Electricity and Natural Gas Networks using the Genetic Algorithm. Research, Society and Development, [S. l.], v. 10, n. 2, p. e42110212641. DOI: 10.33448/rsd-v10i2.12641.

Costa, D. C. L., Costa, H. A. de O., Castro, A. P. S.; Cruz, E. C., Azancort Neto, J. L. & Cruz, B. C. C. da . (2020). The dimensions of Mathematical and Computational Modeling prescribed to Environmental Management. Research, Society and Development, [S. l.], v. 9, n. 10, p. e6939109013. DOI: 10.33448/rsd-v9i10.9013.

Costa, D., Costa, H. & Neves, Lucas. (2019) Métodos Matemáticos Aplicados nas Engenharias via Sistemas Computacionais. SINEPEM – IFPA.

Chitero, J. G. M., Bonini Neto, A., Bonini, C. dos S. B., Heinrichs, R., Soares Filho, C. V., Mateus, G. P., Bisi, B. S., Costa, N. R., Piazentin, J. C., Meirelles, G. C. & Gabriel Filho, L. R. A. (2020). Analysis of the physical recovery of degraded soils via Artificial Neural Networks using a graphical interface. Research, Society and Development, [S. l.], v. 9, n. 7, p. e257973719. DOI: 10.33448/rsd-v9i7.3719.

Goldberg, D. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning (1st ed.), Addison-Wesley Professional.

Ghosh, M., Guha, R., Alam, I., Lohariwal, P., Jalan, D. & Sarkar, R. (2020). Binary Genetic Swarm Optimization: A Combination of GA and PSO for Feature Selection. Journal of Intelligent Systems, 29(1), 1598-1610. DOI: 10.1515/jisys-2019-0062.

Holland, J. H. (1962). Outline for a Logical Theory of Adaptive Systems. Journal of the ACM. 9(3), 297–314. DOI: 10.1145/321127.321128.

Hong, H., Panahi, M., Shirzadi, A., Ma, T., Liu, J., Zhu, A., Chen, W., Kougias, I. & Kazakis, N. (2018). Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution, Science of The Total Environment, 621, 1124-1141,

Kennedy, J. & Eberhart, R. (1995, 27 November-1 December). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, Perth, WA, Australia, (pp. 1942-1948), vol.4, doi:10.1109/ICNN.1995.488968.

Mascarenhas, T. A. T., Moriel Junior, J. G., Gomes, R. de S. R. & Mello, G. J. (2020). Appication of machine learning algorithms in the Classification of Specialized Knowledge of Physics Teachers. Research, Society and Development, [S. l.], v. 9, n. 11, p. e86191110584. DOI: 10.33448/rsd-v9i11.10584.

Mota, C. (2021, May 31). Como seca histórica no Brasil traz risco de inflação e racionamento de energia. BBC Brasil.

Pereira, A. S., Shitsuka, D. M., Parreira, F. J. & Shitsuka, R. (2018). Metodologia da pesquisa científica. Núcleo de Tecnologia Educacional – UFSM.

Pinto, G. L., Gabriel Filho, L. R. A., Bonini Neto, A. & Baptista, R. D. (2020). The Millennials Culture: behavioral mapping in estimating generations using a mathematical model and artificial intelligence. Research, Society and Development, [S. l.], v. 9, n. 9, p. e887997772. DOI: 10.33448/rsd-v9i9.7772.

R. Ouiddir., M. Rahli & L. Abdelhakem-Koridak. (2005). Economic Dispatch using a Genetic Algorithm: Application to Western Algeria's Electrical Power Network. Journal of Information Science and Engineering, 21(3), 659-668.

Schaffer, J., Caruana, R., Eshelman, L. & Das, R. (1989, 1 June). A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization. Proceedings of the 3rd International Conference on Genetic Algorithms. San Francisco, CA.

Sheppard, C. (2016). Genetic Algorithms with Python (1st ed.). Createspace Independent Publishing Platform

Shi, Y. & Eberhart, R. (1998, 4-9 May). A modified particle swarm optimizer. IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), Anchorage, AK, (pp. 69-73), doi:10.1109/ICEC.1998.699146.

Teles, W. de S., Machado, A. P., Cantos Júnior, P. C. C., Melo, C. M. de, Silva, M. H. S., Silva, R. N. da & Jeraldo, V. de L. S. (2021). Machine learning and automatic selection of attributes for the identification of Chagas disease from clinical and sociodemographic data. Research, Society and Development, [S. l.], v. 10, n. 4, p. e19310413879. DOI: 10.33448/rsd-v10i4.13879.

Wirsansky, E. (2020). Hands-On Genetic Algorithms with Python (1st ed.). Packt Publishing Ltd.

Zhou, Y., Li, Z., Zhou, H. & Li, Z.(2016, July 27-29). The application of PSO in the power grid: A review. 35th Chinese Control Conference (CCC), Chengdu, China, DOI: 10.1109/ChiCC.2016.7554948.




How to Cite

COSTA, H. A. de O.; GOMES, L. L.; COSTA, D. C. L. Genetic algorithm and particle swarm applied in electric system optimization . Research, Society and Development, [S. l.], v. 10, n. 10, p. e166101018871, 2021. DOI: 10.33448/rsd-v10i10.18871. Disponível em: Acesso em: 27 oct. 2021.