Coyote Optimization Algorithm (COA)

The Coyote Optimization Algorithm (COA) is a nature-inspired metaheuristic for global optimization proposed by Juliano Pierezan and Leandro dos Santos Coelho (2018).

PhD Thesis available on: https://acervodigital.ufpr.br/handle/1884/70871

Matlab version available on: https://www.mathworks.com/matlabcentral/fileexchange/68373-coa

Python version available on: https://github.com/jkpir/COA

R version available on: https://github.com/jkpir/COA

Publications

Original COA publication:

Pierezan, J. and Coelho, L. S. "Coyote Optimization Algorithm: A new metaheuristic for global optimization problems", Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil, Jul. 2018, pp. 2633-2640.
https://ieeexplore.ieee.org/document/8477769

Cultural COA publication:

Pierezan, J.; Maidl, G.; Yamao, E. M.; Coelho, L. S. and Mariani, V. C. "Cultural coyote optimization algorithm applied to a heavy duty gas turbine operation", Energy Conversion and Management, Vol. 199, Nov. 2019.
https://www.sciencedirect.com/science/article/pii/S0196890419309239

Binary COA publication:

Souza, R.C.T.; Macedo, C.A.; Coelho, L.S.; Pierezan, J. and Mariani, V.C. "Binary Coyote Optimization Algorithm for Feature Selection", Pattern Recognition, Vol. 107, Nov. 2020.
https://www.sciencedirect.com/science/article/abs/pii/S0031320320302739

Chaotic COA publications:

Pierezan, J.; Coelho, L. S.; Mariani, V. C.; Segundo, E. H. V. and Prayogo, D. "Chaotic coyote algorithm applied to truss optimization problems", Computers & Structures, Vol. 242, Jan. 2020.
https://www.sciencedirect.com/science/article/pii/S0045794920301565

Tong, H.; Pierezan, J.; Xu, Y; and Coelho, L. S. "Chaotic coyote optimization algorithm", Journal of Ambient Intelligence and Humanized Computing, 2021.
https://link.springer.com/article/10.1007/s12652-021-03234-5

Multiobjective COA publication:

Pierezan, J.; Coelho, L.S.; Mariani, V.C. and Lebensztajn, L. "Multiobjective Coyote Algorithm Applied to Electromagnetic Optimization", 22nd International Conference on the Computation of Electromagnetic Fields (COMPUMAG), 15-19 July 2019.
https://ieeexplore.ieee.org/document/9032768

Authors

Juliano Pierezan

Juliano Pierezan received the B.S. degree in Electrical Engineering from Pontifical Catholic University of Paraná (PUCPR, Brazil) in 2014, the M.S. degree in Electrical Engineering from Federal University of Paraná (UFPR, Brazil) in 2016 and the doctoral degree in Electrical Engineering from Federal University of Parana (UFPR, Brazil) in 2020. He is a graduation professor in the Salesian Pontifical University (UNISAL, Brazil). His research interests include optimization, computational intelligence and machine learning.
https://www.linkedin.com/in/pierezanj
https://www.researchgate.net/profile/Juliano_Pierezan
https://scholar.google.com.br/citations?user=6S89BfgAAAAJ

Leandro dos Santos Coelho

Leandro dos Santos Coelho received the B.S. degree in Computer Science and also Electrical Engineering from Federal University of Santa Maria (UFSM, Brazil) in 1994 and 1999, respectively, the M.S. degree in Computer Science from the Federal University of Santa Catarina (UFSC, Brazil) in 1997, the doctoral degree in Electrical Engineering from Federal University of Santa Catarina (UFSC, Brazil) in 2000 and post-doctoral research in the Universitá degli Studi di Padova (Padova, Italy) in 2019. He is a Professor in Pontifical Catholic University of Parana (PUCPR, Brazil), and an Associate Professor in the Electrical Engineering Department, Federal University of Parana (UFPR, Brazil). His research interests include nonlinear artificial intelligence, data science, machine learning, deep learning and metaheuristics.
https://www.linkedin.com/in/leandro-dos-santos-coelho-54559214/
https://www.researchgate.net/profile/Leandro_Coelho
https://scholar.google.com/citations?user=0X7VkC4AAAAJ&hl=en