Cloudlet Based Computing Optimization Using Variable-Length Whale Optimization and Differential Evolution

Cloudlet-based optimization involves deploying a set of cloudlets in an environment and assigning user tasks to optimize various metrics, including energy consumption, quality service (QoS), cost. Typically, approaches deal with them separately, which might cause sub-optimality. Furthermore, assuming the fixed location will limit dynamic adaptability problem. Enabling more optimality nature cloudlet-based computing, we propose novel Variable-Length multi-objective Whale Integrated Differential Evolution designated as VL-WIDE. Unlike existing algorithm, VL-WIDE features capability searching different lengths solutions cover variable number for deployment. it enables non-dominated evaluation based on four objectives using crowding distance selection. It provides application-oriented repair operator repairing non-valid assuring that all are generated feasible region. The proposed algorithm moving among pre-defined locations increase according change density caused by mobility. Comparing this developed other algorithms shows its superiority (MOO) metrics. has provided best delta metrics was competitive