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.
Keywords
Coronavirus herd immunity optimizer, Optimal parameter, Design of experiment
THE JOURNAL OF INDUSTRIAL TECHNOLOGY
Published by : Research and Academic Supports Division College of Industrial Technology, King Mongkut’s University of Technology North Bangkok Contributions welcome at : http://j.cit.kmutnb.ac.th/en/
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