Scientific Computing In The Cloud: Impact Of Ant Colony Optimization, Gradient Descent, And Bayesian Decision Models

Abstract

Efficient optimization approaches are required for scientific computing in cloud environments to manage large-scale calculations, dynamic workloads, and probabilistic decision making. This study investigates the role of Ant Colony Optimization (ACO), Gradient Descent (GD), and Bayesian Decision Models (BDM) in improving computational efficiency. A hybrid optimization framework is proposed, which includes ACO for resource allocation, GD for iterative learning, and BDM for probabilistic inference. The performance metrics examined were execution time, accuracy, resource utilization, and scalability. The results show that the hybrid model greatly improves computational performance by improving efficiency, adaptability, and optimization capabilities, making it a viable option for cloud-based scientific computing applications.