Nature-Inspired Algorithms in Data Analytics: A Unified Cross Domain Benchmarking Framework

Abstract

The rapid growth of high-dimensional and heterogeneous datasets has intensified the demand for scalable and robust optimization approaches in data analytics. Traditional deterministic algorithms frequently suffer from premature convergence, initialization sensitivity, and reduced performance in nonlinear or noisy environments. Nature-inspired algorithms (NIAs), including evolutionary, swarm-based, and physics-inspired approaches, provide stochastic population based mechanisms capable of navigating complex search spaces effectively. This study develops a unified cross-domain benchmarking framework for evaluating NIAs across feature selection, clustering, predictive modeling, and image segmentation tasks. A structured taxonomy is introduced, and performance is analyzed along five dimensions: predictive accuracy, convergence efficiency, computational complexity, robustness, and hybridization trade-offs. Mathematical formulations of representative algorithms (Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Simulated Annealing) are incorporated to strengthen analytical rigor. Comparative synthesis reveals that algorithm suitability is task-dependent: evolutionary algorithms excel in feature selection, swarm-based approaches converge efficiently in clustering, and hybrid models yield superior predictive performance at increased computational cost. The proposed framework establishes standardized evaluation criteria to enhance reproducibility and cross-domain comparability. The findings provide theoretical insight and practical guidance for selecting nature-inspired optimization strategies in complex data-driven environments.