An Integrated Analytical Framework of Hybrid Metaheuristic Optimization Strategies in Multi-Domain Engineering Systems: From Power Grid Stability to Computational Intelligence

Abstract

The rapid evolution of complex engineering environments-ranging from interconnected power systems and cloud computing infrastructures to geotechnical modeling and digital image processing-has necessitated the development of increasingly sophisticated optimization methodologies. Traditional deterministic approaches often fail to account for the stochastic nature and high-dimensional constraints inherent in modern large-scale systems. This research article provides a comprehensive investigation into the synthesis and application of hybrid metaheuristic algorithms, with a primary focus on Particle Swarm Optimization (PSO) and its various derivatives, including Grey Wolf Optimization (GWO), Artificial Bee Colony (ABC), and Grasshopper Optimization (GOA). By analyzing the integration of these algorithms with gradient boosting machines and surrogate-assisted models, this study elucidates the mechanisms through which hybrid intelligence overcomes the limitations of premature convergence and computational expense. The article explores the application of these techniques in Load Frequency Control (LFC) for multi-area power systems, geotechnical liquefaction prediction, and resource allocation in dynamic cloud environments. Through an extensive theoretical discourse, the research demonstrates that the hybridization of exploration-oriented and exploitation-oriented metaheuristics provides a superior balance in navigating complex search spaces. The findings suggest that adaptive granularity and multi-swarm architectures are essential for addressing the "curse of dimensionality" in contemporary engineering problems, offering a robust foundation for future autonomous system design.