Optimal Choice of Parameters for Coronavirus Herd Immunity Optimizer

Basic reproduction rate is an important control parameter for coronavirus herd immunity optimizer (CHIO), which decides the CHIO operators and has a direct impact on the performance and convergence of the algorithm. The improper choice of basic reproduction rate may lead to bad optimization results. Six methods to change the parameter value dynamically with iterations are proposed. The effects of the proposed methods were systematically investigated on both unimodal and multimodal functions. The experimental results show that the method based on exponential decreasing can improve the optimization performance of CHIO in terms of both accuracy and robustness.