This study aims to enhance the efficiency and performance of cloud computing systems by addressing issues related to workload distribution and resource utilization in server consolidation. By ensuring an even distribution of computing workloads among available resources, workload balancing helps avoid resource limitations and enhances system performance. At the same time, the objective of server consolidation is to minimize energy consumption while reducing response time, ultimately improving the efficiency and sustainability of cloud infrastructures. To address these issues, this paper introduces a novel optimization algorithm for resource allocation. Our algorithm achieves a better trade-off between response time (RT) and energy consumption (EC) compared to widely-used algorithms, including Ant Bee Colony (ABC), Particle Swarm Optimization (PSO), and First Come First Serve (FCFS).