Metaheuristics are powerful optimization techniques that have gained significant attention for their ability to solve complex and large-scale problems where exact algorithms fall short. These methods, including evolutionary algorithms, swarm intelligence, physics-based, and Human-based, are inspired by natural processes and are particularly effective for problems with vast search spaces and multiple constraints. In engineering, metaheuristics are frequently applied to optimize resource allocation, scheduling, and design processes, where traditional methods are computationally intensive or impractical. In cloud computing, task scheduling remains a critical challenge as demand for scalable, high-performance, and cost-effective solutions grows. Metaheuristic optimization offers promising approaches to address the scale, heterogeneity, and dynamic nature of cloud environments.

The increasing reliance on cloud-based systems across industries has amplified the need for efficient task scheduling and resource management solutions. Traditional scheduling approaches often lack the flexibility and adaptability required to handle the dynamic workloads of cloud environments, leading to inefficiencies in resource utilization and task execution time. Motivated by these challenges, this research explores how metaheuristic optimization can enhance cloud task scheduling by improving performance, balancing loads, and minimizing costs. This thesis aims to develop innovative optimization techniques that address these pressing issues, contributing to more robust and adaptive scheduling frameworks for cloud systems.

This thesis is organized into two main parts. The first part provides a theoretical foundation, offering background on optimization methods, an overview of engineering problems, and a discussion of task scheduling challenges in cloud computing. The second part comprises three published studies that illustrate the practical application of the proposed methods. Paper I and II present the Enhanced Evaporation rate Water Cycle Algorithm (EErWCA) and modified Artificial Electric Field Algorithm (mAEFA) techniques for addressing global optimization and engineering problems. Paper III develops hybrid Artificial Gorilla Troops Optimizer with Honey Badger Algorithm (GTOHBA) for optimized cloud task scheduling. Together, these contributions address key research questions, positioning this work within the broader context of optimization-driven scheduling and cloud computing.