Heuristic Initialization in Butterfly Optimization for Cloud Task Scheduling

In cloud computing environment, task scheduling is a major concern since it affects resource utilization and cost-effectiveness. The issue of task scheduling is categorized as nondeterministic polynomial time (NP)-hard due to its complexity, which makes it challenging to identify optimal solutions using traditional approaches. Since random initialization of solutions is vital for optimization techniques, the effectiveness of metaheuristic algorithms can be greatly enhanced by providing efficient initial point. This work presents an improved initialization strategy for the butterfly optimization algorithm (BOA) utilizing heuristic techniques. The BOA is initialized with respect to the minimum completion time (MCT) and longest job to fastest processor (LJFP). The degree of imbalance, total execution time, total energy consumption, and makespan are among the parameters that the MCT-BOA and LJFP-BOA algorithm’s efficiency is determined by.