Optimal Parameter Tuning of Coronavirus Herd Immunity Optimizer

DOI: 10.14416/j.ind.tech.2022.08.002


  • Sirichai Yodwangjai Department of Industrial Engineering, College of Industrial Technology, King Mongkut’s University of Technology North Bangkok
  • Kittipong Malampong Thai-German Pre-Engineering School, College of Industrial Technology, King Mongkut’s University of Technology North Bangkok


Coronavirus herd immunity optimizer, Optimal parameter, Design of experiment


The Coronavirus Herd Immunity Optimizer (CHIO) is a recently developed meta-heuristic optimization algorithm that was inspired by the herd immunity concept. It simulates the behavior of a natural entity and was motivated by the appearance of a pathogenic coronavirus. The CHIO mimics the mechanism of obtaining natural immunity against through the application of herd psychology, which is considered to be one of the methods of acquiring immunity from infectious diseases. The objectives of this article are to review CHIO, and to find the parameter that impacts the result. The algorithm has parameters that include the basic reproduction rate (BRr), the maximum age of infected cases (Maxage), the size of the population (HIS), and the maximum of iteration (MaxIter). The CHIO experiment is designed to test with 3k Full Factorial Design and analyzed analysis of variance (ANOVA). The parameter is tuned to find optimal parameters in each benchmark function. This article presents and compares performances between CHIO and method in Al-Betar et al.’s paper with different twenty-three benchmark test functions. The results showed that 12 out of 23 benchmarks function in the best solution. Moreover, the results achieved by parameter tuning of CHIO are compared against the results of Al-Betar et al.’s paper with 3 benchmark functions.


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E.G. Talbi, Metaheuristics: From design to implementation, John Wiley & Sons, Inc., NJ, USA, 2009.

M. Batsyn, I. Bychkov, L. Komosko and A. Nikolaev, Tabu search for fleet size and mix vehicle routing problem with hard and soft time windows, International Conference on Network Analysis, Proceeding, 2016, 3-18.

S. Kirkpatrick, C.D. Gelatt and M.P. Vecchi, Optimization by simulated annealing, Science, 1983, 220(4598), 671-680.

M. Dorigo, M. Birattari and T. Stutzle, Ant colony optimization, IEEE computational intelligence magazine, 2006, 1(4), 28-39.

R.C. Eberhart and Y. Shi, Particle swarm optimization: Developments, applications and resources, The 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), Proceeding, 2001, 81-86.

W.Y, Szeto, Y. Wu and S.C. Ho, An artificial bee colony algorithm for the capacitated vehicle routing problem, European Journal of Operational Research, 2011, 215(1), 126-135.

E. Rashedi, H. Nezamabadi-Pour and S. Saryazdi, GSA: A gravitational search algorithm, Information sciences, 2009, 179(13), 2232-2248.

H. Eskandar, A. Sadollah, A. Bahreininejad and M. Hamdi, Water cycle algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems, Computers and Structures, 2012, 110-111, 151-166.

B.M. Baker and M.A. Ayechew, A genetic algorithm for the vehicle routing problem, Computers and Operations Research, 2003, 30(5), 787-800.

Z. Zhang, O. Che, B. Cheang, A. Lim and H. Qin, A memetic algorithm for the multiperiod vehicle routing problem with profit, European Journal of Operational Research, 2013, 229(3), 573-584.

D. Simon, Biogeography-based optimization, IEEE transactions on evolutionary computation, 2008, 12(6), 702-713.

A.S. Azad, M.S.A. Rahaman, J. Watada, P. Vasant, J.A.G. Vintaned, Optimization of the hydropower energy generation using meta-heuristic approaches: A review, Energy Reports, 2020, 6, 2230-2248.

M.A. Al-Betar, Z.A.A. Alyasseri, M.A. Awadallah and I.A. Doush, Coronavirus herd immunity optimizer (CHIO), Neural Computing and Applications, 2021, 33, 5011-5042.

M. Alweshah, Coronavirus herd immunity optimizer to solve classification problems, Soft Computing, 2022, 1-21.

A.S. Mahboob, H.S. Shahhoseini, M.R.O. Moghaddam and S. Yousefi, A coronavirus herd immunity optimizer for intrusion detection system, The 29th Iranian Conference on Electrical Engineering (ICEE), Proceeding, 2021, 579-585.

Y. Arıkuşu and N. Bayhan, Design of coronavirus herd immunity optimization based PID controller for an automatic voltage regulator, TOK 2021 Otomatik Kontrol Ulusal Kongresi, Proceeding, 2021, 177-182.

S. Amini, S. Ghasemi, H. Golpira and A. Anvari-Moghaddam, Coronavirus herd immunity optimizer (CHIO) for transmission expansion planning, 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), Proceeding, 2021, 1-6.

M.M. Refaat, S.H.A. Aleem, Y. Atia, Z.M. Ali and M.M. Sayed, AC and DC transmission line expansion planning using coronavirus herd immunity optimizer, The 22nd International Middle East Power Systems Conference (MEPCON 2021), Proceeding, 2021, 313-318.

M. Alqarni, Sodium sulfur batteries allocation in high renewable penetration microgrids using coronavirus herd immunity optimization, Ain Shams Engineering Journal, 2022, 13(2), 101590.

C. Kumar, D.M. Mary and T. Gunasekar, MOCHIO: A novel multi-objective coronavirus herd immunity optimization algorithm for solving brushless direct current wheel motor design optimization problem, Automatika, 2022, 63(1), 149-170.

S.N. Makhadmeh, M.A. Al-Betar, M.A. Awadallah, A.K. Abasi, Z.A.A. Alyasseri, I.A. Doush, O.A. Alomari, R. Damasevicius, A. Zajanckauskas and M.A. Mohammed, A modified coronavirus herd immunity optimizer for the power scheduling problem, Mathematics, 2022, 10(3), 315.

A. Naderipour, A. Abdullah, M.H. Marzbali and S.A. Nowdeh, An improved corona-virus herd immunity optimizer algorithm for network reconfiguration based on fuzzy multi-criteria approach, Expert Systems with Applications, 2022, 187, 115914.

Z.M. Ali, S.H.A Aleem, A.l. Omar and B.S. Mahmoud, Economical-environmental-technical operation of power networks with high penetration of renewable energy systems using multi-objective coronavirus herd immunity algorithm, Mathematics, 2022, 10, 1201.

L.M. Dalbah, M.A. Al-Betar, M.A. Awadallah and R.A. Zitar, A coronavirus herd immunity optimization (CHIO) for travelling salesman problem, International Conference on Innovative Computing and Communications, Proceeding, 2022, 717-729.

L.M. Dalbah, M.A. Al-Betar, M.A. Awadallah and R.A. Zitar, A modified coronavirus herd immunity optimizer for capacitated vehicle routing problem, Journal of King Saud University-Computer and Information Sciences, 2022, 34(8), 4782-4795

M. Alweshah, S. Alkhalaileh, M.A. Al-Betar and A.A. Bakar, Coronavirus herd immunity optimizer with greedy crossover for feature selection in medical diagnosis, Knowledge-Based Systems, 2022, 235, 107629

M. Jamil and X. Yang, A literature survey of benchmark functions for global optimisation problems, International Journal of Mathematical Modelling and Numerical Optimisation, 2013, 4(2), 150-194.






บทความวิจัย (Research article)