A robust multiobjective Harris' Hawks Optimization algorithm for the binary classification problem

The Harris' Hawks Optimization (HHO) is a recent metaheuristic inspired by the cooperative behavior of the hawks. These avians apply many intelligent techniques like surprise pounce (seven kills) while they are catching their prey according to the escaping patterns of the target. The HHO simulates these hunting patterns of the hawks to obtain the best/optimal solutions to the problems. In this study, we propose a new multiobjective HHO algorithm for the solution of the well-known binary classification problem. In this multiobjective problem, we reduce the number of selected features and try to keep the accuracy prediction as maximum as possible at the same time. We propose new discrete exploration (perching) and exploitation (besiege) operators for the hunting patterns of the hawks. We calculate the prediction accuracy of the selected features with four machine learning techniques, namely, Logistic Regression, Support Vector Machines, Extreme Learning Machines, and Decision Trees. To verify the performance of the proposed algorithm, we conduct comprehensive experiments on many benchmark datasets retrieved from the University of California, Irvine (UCI) Machine Learning Repository. Moreover, we apply it to a recent real-world dataset, i.e., a Coronavirus disease (COVID-19) dataset. Significant improvements are observed during the comparisons with state-of-the-art metaheuristic algorithms. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The Harris' Hawks Optimization (HHO) is a recent metaheuristic inspired by the cooperative behavior of hawks, which simulates their hunting patterns. A new multiobjective HHO algorithm is proposed in this study to address binary classification problems and reduce the number of selected features.