Novel Meta-Heuristic Algorithm for Feature Selection, Unconstrained Functions and Engineering Problems

This paper proposes a Sine Cosine hybrid optimization algorithm with Modified Whale Optimization Algorithm (SCMWOA). The goal is to leverage the strengths of WOA and SCA solve problems continuous binary decision variables. SCMWOA first tested on nineteen datasets from UCI Machine Learning Repository different numbers attributes, instances, classes for feature selection. It then employed several benchmark functions classical engineering case studies. applied solving constrained problems. two examples are welded beam design tension/compression spring design. results emphasize that outperforms comparative algorithms provides better accuracy compared other algorithms. statistical analysis tests, including one-way variance (ANOVA) Wilcoxon’s rank-sum, confirm performs better.