Advancements in Modeling Techniques for Big Data Analytics: A Comprehensive Review of Evolutionary Optimization Approaches

Abstract

This paper presents a comprehensive review of the advancements in modeling techniques for Big Data analytics, with a particular focus on the integration of evolutionary optimization approaches. The study systematically analyzes recent developments in statistical models, machine learning models, and other computational frameworks that have been enhanced through evolutionary algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE). By synthesizing findings from peer-reviewed literature and experimental studies, the paper highlights the significant improvements in model accuracy, scalability, and computational efficiency achieved through these techniques. The review also identifies key challenges and opportunities for future research in the field, providing a roadmap for further exploration and innovation in Big Data analytics.