Optimizing Feature Selection by Enhancing Particle Swarm Optimization with Orthogonal Initialization and Crossover Operator

Recent advancements in computational and database technologies have led to the exponential growth of large-scale medical datasets, significantly increasing data complexity and dimensionality in medical diagnostics. Efficient feature selection methods are critical for improving diagnostic accuracy, reducing computational costs, and enhancing the interpretability of predictive models. Particle Swarm Optimization (PSO), a widely used metaheuristic inspired by swarm intelligence, has shown considerable promise in feature selection tasks. However, conventional PSO often suffers from premature convergence and limited exploration capabilities, particularly in high-dimensional spaces. To overcome these limitations, this study proposes an enhanced PSO framework incorporating Orthogonal Initialization and a Crossover Operator (OrPSOC). Orthogonal Initialization ensures a diverse and uniformly distributed initial particle population, substantially improving the algorithm’s exploration capability. The Crossover Operator, inspired by genetic algorithms, introduces additional diversity during the search process, effectively mitigating premature convergence and enhancing global search performance. The effectiveness of OrPSOC was rigorously evaluated on three benchmark medical datasets—Colon, Leukemia, and Prostate Tumor. Comparative analyses were conducted against traditional filter-based methods, including Fast Clustering-Based Feature Selection Technique (Fast-C), Minimum Redundancy Maximum Relevance (MinRedMaxRel), and Five-Way Joint Mutual Information (FJMI), as well as prominent metaheuristic algorithms such as standard PSO, Ant Colony Optimization (ACO), Comprehensive Learning Gravitational Search Algorithm (CLGSA), and Fuzzy-Based CLGSA (FCLGSA). Experimental results demonstrated that OrPSOC consistently outperformed these existing methods in terms of classification accuracy, computational efficiency, and result stability, achieving significant improvements even with fewer selected features. Additionally, a sensitivity analysis of the crossover parameter provided valuable insights into parameter tuning and its impact on model performance. These findings highlight the superiority and robustness of the proposed OrPSOC approach for feature selection in medical diagnostic applications and underscore its potential for broader adoption in various high-dimensional, data-driven fields.