Evaluating the Efficiency of Gray Wolf Optimization and Colonial Competition Algorithm in Load Balancing of Distributed Systems

The expansion of distributed systems has highlighted the critical importance of efficient distributed processing. Load balancing, a vital aspect of distributed processing, minimizes system response time and optimizes resource utilization, making it crucial for effective distributed resource management. Recent years have seen the development of numerous resource allocation algorithms aimed at reducing costs and energy consumption in distributed systems. However, the high time complexity of traditional resource allocation algorithms has driven the adoption of metaheuristic approaches, which offer improved efficiency and scalability. Among these, the Gray Wolf Optimization algorithm (GWO) has gained attention for its innovative approach and promising results. Comparative evaluations with algorithms like the Colonial Competitive Algorithm (CCA) provide a strong foundation for future research. Experimental results using standard datasets indicate the superior performance of GWO in resource allocation. The GWO algorithm is closer to the ideal state by more than 96% and has been able to consume the least resources. The CCA algorithm with 83% and the First-best Algorithm (FF) algorithm with 78.4% have shown a large divergence from the ideal state and have consumed more resources, which is not desirable. It achieves near-optimal allocations with fewer resources, even when dealing with large-scale datasets. GWO enhances load balancing by efficiently distributing workloads, highlighting its potential for optimizing resource allocation in distributed systems. By efficiently balancing loads and minimizing resource consumption, the GWO algorithm enhances distributed system performance, paving the way for further exploration of metaheuristic methods to ensure scalability and efficiency in complex systems.