Information interconnection Harris hawks hybrid optimization algorithm

In the biomedical field, optimization algorithms are widely used in datasets such as pneumonia and cancer, often combined with machine learning methods for feature selection of datasets. We propose the Information Interconnected Harris hawks optimization algorithm (IIHHO), which enhances the optimization performance from three aspects: information transmission mode, exploitation mode, and exploration mode. First, the hawk group is divided into three layers: the leading layer, the guiding layer, and the ordinary layer, and information is transmitted between the layers so that the next generation is distributed in a better position. Then, the exploitation and exploration methods of the algorithm are improved to balance the exploitation and exploration stages of the algorithm. In order to verify the effectiveness of the algorithm, a comparative experiment was conducted on the CEC2022 test function to compare it with other advanced optimization algorithms. Subsequently, a feature selection experiment was carried out using the UCI data set. The resultant feature subset demonstrated good comprehensive performance, underscoring the high efficiency of our algorithm.