Optimizing task scheduling in cloud environments: a hybrid golden search whale optimization algorithm approach

Managing fluctuating workloads and optimizing resource utilization in cloud environments pose significant challenges, particularly in fields requiring real-time data processing, such as healthcare. This paper introduces a novel hybrid metaheuristic technique, the Golden Search Whale Optimization Algorithm (GSWOA), specifically designed for scheduling independent dynamic biomedical data. GSWOA merges the strengths of Golden Search Optimization (GSO) and Whale Optimization Algorithm (WOA), optimizing numerical function optimization and achieving a balance between exploration and exploitation. The algorithm’s effectiveness was assessed using MATLAB by applying standard benchmark functions and further evaluated on a real-world biomedical dataset within the CloudSim environment. The performance evaluations demonstrate that GSWOA significantly outperforms existing metaheuristic and traditional scheduling techniques, achieving a 42.71% increase in resource utilization and a 14.17% reduction in makespan compared to conventional methods. These results highlight GSWOA’s potential to enhance scheduling efficiency substantially in cloud computing infrastructures, suggesting it is a powerful tool for complex task allocations. Future research will explore the scalability of GSWOA and its applicability across other data-intensive sectors. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.