2021, Volume 5
2020, Volume 4
2019, Volume 3
2018, Volume 2
2017, Volume 1
Volume 5 , Issue 2 , December 2021 , Pages: 56 - 65
Artificial Corona-Inspired Optimization Algorithm: Theoretical Foundations, Analysis, and Applications
Alia Youssef Gebreel, Operations Research, Faculty of Graduate Studies for Statistical Research, Cairo University, Cairo, Egypt
Received: Jul. 28, 2021;       Accepted: Aug. 18, 2021;       Published: Aug. 27, 2021
DOI: 10.11648/j.ajai.20210502.12        View        Downloads  
One of the important parts of computer science is Artificial Intelligence (AI). It deals with the development of machines that can take decisions like humans on their own. Currently, AI can solve many difficult real-world problems because it works much better and faster than humans. Researchers of operations research also are turning their heads towards AI instead of traditional systems. Meanwhile, there are several AI models to solve mathematical optimization problems. They depend heavily on a random search, but many of their solutions have been efficient at finding absolute optimum. This means that it is necessary to choose another optimization model to get quite the optimum value. This paper introduces an artificially intelligent algorithm in order to find the optimal solution for a given computational problem that minimizes or maximizes a particular function. It is inspired by the corona virus that spreads throughout the world and infects healthy people. Its structure simulates the stages of virus transmission and treatment. Because the starting point is so important for converging to the global optimum, corona virus approach has guided researchers to select the starting point and parameters. Actually, this point depends on three real numbers as the corona virus affects three main parts of the human body (nose, throat, respiratory). The proposed algorithm has been found to be an optimal key to different applications. It doesn't require any derivative information and it is simple in implementation with few parameters setting. Finally, some numerical examples are presented to illustrate the algorithm studied here. The computational results show that it has high performance in finding an optimal solution within reasonable time.
Artificial Intelligent Algorithms, Corona Virus (CV), Optimal Solution
To cite this article
Alia Youssef Gebreel, Artificial Corona-Inspired Optimization Algorithm: Theoretical Foundations, Analysis, and Applications, American Journal of Artificial Intelligence. Vol. 5, No. 2, 2021, pp. 56-65. doi: 10.11648/j.ajai.20210502.12
Copyright © 2021 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
[ 1 ]
Ahmed T. Sahlol, DaliaYousri, Ahmed A. Ewees, Mohammed A. A. Al qaness, Robertas Damasevicius, & Mohamed Abd Elaziz (2020). COVID 19 image classification using deep features and fractional order marine predators algorithm. Scientific Reports, Nature research, 10: 15364, -020-71294-2, PP. 1-15.
[ 2 ]
F. Martı´nez-A´ lvarez, G. Asencio-Corte´ s, J. F. Torres, D. Gutie´rrez-Avile´ s, L. Melgar-Garcı´a, R. Pe´rez-Chaco´ n, C. Rubio-Escudero, J. C. Riquelme, & A. Troncoso (2020). Coronavirus optimization algorithm: A bioinspired meta heuristic based on the COVID-19 propagation model, Big Data, Mary Ann Liebert, Inc., DOI: 10.1089/big.2020.0051, 8 (4), (Research gate).
[ 3 ]
Hayat Ouassou, Loubna Kharchoufa, Mohamed Bouhrim, Nour Elhouda Daoudi, Hamada Imtara, Noureddine Bencheikh, Amine ELbouzidi, & Mohamed Bnouham (2020). The pathogenesis of coronavirus disease 2019 (COVID-19): Evaluation and prevention. Journal of Immunology Research, Hindawi, 1155/2020/1357983, PP. 1-7.
[ 4 ]
Mohammed A. A. Al-qaness, Ahmed A. Ewees, Hong Fan, & Mohamed Abd El Aziz (2020). Optimization method for forecasting confirmed cases of COVID-19 in China. Journal of Clinical. Medicine, DOI: 10.3390/jcm9030674, 9 (674), PP. 1-15.
[ 5 ]
Tanu Singhal (2020). A Review of Coronavirus Disease-2019 (COVID-19), The Indian Journal of Pediatrics, 87 (4), PP. 281–286.
[ 6 ]
Colin R. Reeves (1996). Heuristic search methods: A review, Conference Paper,
[ 7 ]
Krishna Nand Patel, Scahin Raina, & Saurabh Gupta (2020). Artificial intelligence and its models, Journal of Applied Science and Computations (JASC), 7 (2), PP. 95-97.
[ 8 ]
Prithwish Chakraborty, Gourab Ghosh Roy, Swagatam Das, Dhaval Jain, & Ajith Abraham (2009). An improved harmony search algorithm with differential mutation operator, Fundamenta Informaticae, 95, PP. 1–26.
[ 9 ]
Reza Sirjani, Azah Mohamed, & Hussain Shareef (2012). Heuristic optimization techniques to determine optimal capacitor placement and sizing in radial distribution networks: A comprehensive review, Przeglad Elektrotechniczny, ISSN 0033-2097, R. 88 NR 7a.
[ 10 ]
Mohammed Azmi Al-Betar, Zaid Abdi Alkareem Alyasseri, Mohammed A. Awadallah, & Iyad Abu Doush, (2020). Coronavirus herd immunity optimizer (CHIO), Neural Computing and Applications, Springer-Verlag London Ltd.,, PP. 1-32.
[ 11 ]
Gaurav Dhiman, V. Vinoth Kumar, A Kaur, & A. Sharma (2021). DON: Deep learning and optimization based framework for detection of novel coronavirus disease using X ray images. Interdisciplinary Sciences: Computational Life Sciences, https://doi. org/10.1007/s12539- 021- 00418- 7, PP. 1-13.
[ 12 ]
Alia Youssef Gebreel (2018). An overview of genetic algorithm, bacterial foraging algorithm, and harmony search algorithm, Global Scientific Journals, 6 (9), PP. 165-189.
[ 13 ]
P. D. D. Dominic, Ahmad Kamil, P. Parthiban, & M. Punniyamoorthy (2009). A comparative representation approach to modern heuristic search methods in a job shop. The International Journal of Applied Management and Technology, 6 (2), PP. 39-60.
[ 14 ]
Timo Pukkala, & Mikko Kurttila (2004). Examining the performance of six heuristic optimisation techniques in different forest planning problems, Silva Fennica, research articles, 39 (1), PP. 67-80.
[ 15 ]
Julia Borghoff, Lars R. Knudsen, & Krystian Matusiewicz, (2011). Hill climbing algorithms and trivium, Springer-Verlag Berlin Heidelberg, PP. 57-73.
[ 16 ]
Sheldon H. Jacobson, & Enver Yücesan, (2004). Analyzing the performance of generalized hill climbing algorithms, Journal of Heuristics, Kluwer Academic Publishers, Manufactured in the Netherlands, 10, PP. 387-405.
[ 17 ]
Andreas Fink, & Stefan Voss (1998). Applications of modern heuristic search methods to pattern sequencing problems, Computers & Operations Research, PP. 1-20.
[ 18 ]
Baran Hekimoğlu, & Serdar Ekinci (2020). Optimally designed PID controller for a DC-DC buck converter via a hybrid whale optimization algorithm with simulated annealing,, DOI: 10.5152/Electrica.19034, 20 (1), PP. 19-27.
[ 19 ]
K. Y. Lee, & M. A. El-Sharkawi (2008). Modern heuristic optimization techniques with applications to power systems, Power System Analysis, Computing, and Economics Committee, IEEE Power Engineering Society.
[ 20 ]
Alia Youssef Gebreel (2016). An adaptive interactive multi-objective optimization approach based on decision neural network, International Journal of Scientific & Engineering Research, 7 (8), PP. 1178-1185.
[ 21 ]
K. M. Bakwad, S. S. Pattnaik, B. S. Sohi, S. Devi, B. K. Panigrahi, & Sastry V. R. Gollapudi (2010). Multimodal function optimization using synchronous bacterial foraging optimization technique, IETE Journal of Research, 56 (2), PP. 80-87.
[ 22 ]
M. Fesanghary, E. Damangir, and I. Soleimani (2009). Design optimization of shell, & tube heat exchangers using global sensitivity analysis and harmony search algorithm, Applied Thermal Engineering, 29, PP. 1026–1031.
[ 23 ]
Girish P Potdar, & R C Thool (2014). Comparison of various heuristic search techniques for finding shortest path, International Journal of Artificial Intelligence & Applications (IJAIA), 5 (4), PP. 63-74.
[ 24 ]
Dimitri P. Bertseka (2009). Convex optimization theory, Massachusetts Institute of Technology.
[ 25 ]
Mokhtar S. Bazaraa, John J. Jarvis, & Hanif D. Sherali (2010). Linear programming and network flows, Fourth edition, A John Wiley & Sons, Inc., Publication.
[ 26 ]
David G. Luenberger, & Yinyu Ye (2008). Linear and nonlinear programming, (Chapter 3-The simplex method), PDF, Third edition, PP. 73.
[ 27 ]
Zong Woo Geem (2008). Novel derivative of harmony search algorithm for discrete design variables, Applied Mathematics and Computation, 199, PP. 223–230.
[ 28 ]
Quan-Ke Pan, P. N. Suganthan, M. Fatih Tasgetiren, & J. J. Liang (2010). A self-adaptive global best harmony search algorithm for continuous optimization problems. Applied Mathematics and Computation, 216, PP. 830–848.
[ 29 ]
M. Mahdavi, M. Fesanghary, & E. Damangir (2007). An improved harmony search algorithm for solving optimization problems", Science Direct, Applied Mathematics and Computation, 188, PP. 1567–1579.
[ 30 ]
Valdimir Sevasty Anov (2013). Hybrid multi-gradient explorer algorithm for global multi-objective optimization, American Institute of Aeronautics and Astronautics, 10 (94-99), PP. 1-15, eartius, Inc.
Browse journals by subject