Hybrid Computational Techniques for Large-Scale Data-Intensive Scientific Applications

Abstract

In the realm of large-scale data-intensive scientific applications, hybrid computational techniques have emerged as a pivotal solution to address the complexities associated with processing and analyzing vast datasets. These hybrid approaches integrate both exact methods and heuristic algorithms, effectively leveraging their respective strengths to enhance computational efficiency and solution quality. Traditional exact methods, while ensuring optimal solutions, often suffer from prohibitive computational costs when applied to large-scale problems. Conversely, heuristic algorithms provide quicker, feasible solutions but may lack the rigor needed for high-quality outcomes. Recent advancements in hybrid algorithms have demonstrated significant improvements in solving combinatorial optimization problems across various domains, including information technology, transportation, and healthcare. This paper reviews the current landscape of hybrid computational techniques, focusing on their application in large-scale scenarios. It highlights the characteristics of existing algorithms and proposes future research directions aimed at refining these methodologies. By synthesizing insights from recent studies, this work aims to guide researchers in developing more effective hybrid algorithms tailored for complex scientific applications