Particle guided metaheuristic algorithm for global optimization and feature selection problems

Optimization problems can be seen in numerous fields of practical studies. One area making waves in the application of optimization methods is data mining in machine learning. An important preprocessing technique of data mining where irrelevant variables are discarded from the datasets and holding onto variables with important information is referred to as feature selection (FS). FS is critical to tackling the ‘curse of dimensionality’ by reducing the number of features, minimizing computational expensiveness and maximizing the accuracy of the machine learning models. Swarm Intelligence (SI)-based meta-heuristic algorithms (MAs) have been widely employed to solve several optimization problems like FS. However, common drawbacks identified with these algorithms include getting trapped in local optima, especially in situations where the search space is large (high dimensional space). This study proposes a new hybrid SI-based MA called Particle Swarm-guided Bald Eagle Search (PS-BES). The algorithm utilizes the speed of Particle Swarm to guide Bald Eagles in their search to ensure a smooth transition of the algorithm from exploration to exploitation. Additionally, we introduce the Attack-Retreat-Surrender technique, a new local-optima escape technique to enhance the balance between diversification and intensification of PS-BES. To establish the outstanding performance of the proposed algorithm, PS-BES is comprehensively analyzed utilizing 26 Benchmark functions. Further, the practicality of PS-BES is highlighted by its binary version for feature selection and evaluated using 27 classification datasets from the UCI repository. The results prove the overall superiority of PS-BES and bPS-BES as opposed the 10 state-of-the-art algorithms employed in the study.