A New Hybrid Seagull Optimization Algorithm for Feature Selection

Abstract

Hybrid algorithms have attracted more and more attention in the field of optimization algorithms. In this paper, three hybrid algorithms are proposed to solve feature selection problems based on seagull optimization algorithm (SOA) and thermal exchange optimization (TEO). In the first algorithm, we take the roulette wheel to choose one of the two algorithms for located updating. Another method is to join the TEO algorithm for optimization after SOA algorithm iteration. The last method is to adopt TEO algorithm's heat exchange formula to improve the seagull attack mode of SOA algorithm, so as to improve the exploitation ability of SOA algorithm. The performance of proposed methods is evaluated on 20 standard benchmark datasets in the UCI repository and compared with three well-known hybrid optimization feature selection methods in the literature. The experimental results illustrate that the proposed algorithm has high efficiency in improving classification accuracy, ensuring the ability of hybrid SOA algorithm in feature selection and classification task information attribute selection, and reducing the CPU time.