A metaheuristic approach based on coronavirus herd immunity optimiser for breast cancer diagnosis

As one of the important concepts in epidemiology, herd immunity was recommended to control the COVID-19 pandemic. Inspired by this technique, the Coronavirus Herd Immunity Optimiser has recently been introduced, demonstrating promising results in addressing optimisation problems. This particular algorithm has been utilised to address optimisation problems widely; However, there is room for enhancement in its performance by making modifications to its parameters. This paper aims to improve the Coronavirus Herd Immunity Optimisation algorithm to employ it in addressing breast cancer diagnosis problem through feature selection. For this purpose, the algorithm was discretised after the improvements were made. The Opposition-Based Learning approach was applied to balance the exploration and exploitation stages to enhance performance. The resulting algorithm was employed in the diagnosis of breast cancer, and its performance was evaluated on ten benchmark functions. According to the simulation results, it demonstrates superior performance in comparison with other well-known approaches of the similar nature. The results demonstrate that the new approach performs well in diagnosing breast cancer with high accuracy and less computational complexity and can address a variety of real-world optimisation problems.