A Hybrid White Shark Equilibrium Optimizer for Power Scheduling Problem Based IoT

The power schedule problem (PSP) is the problem of managing, controlling, and scheduling power consumption of electrical appliances/devices to operate at the best periods according to several constraints and objectives. The PSP is a complex and high-constraint scheduling problem, making its search space extensive and rugged. The PSP components can be controlled and managed by utilizing a communication approach that interconnects the appliances and enhances exchanging data. Several communication approaches were used for the PSP, where the Internet of Things (IoT) is the best for data exchange. The PSP has been extensively handled using various optimization approaches, particularly metaheuristics, due to their capabilities to optimize different search space scales. Nevertheless, in some cases, these optimization algorithms suffer from low execution abilities, especially with huge search spaces like the PSP. In this study, a recent metaheuristic, called white shark optimizer (WSO), is adapted and enhanced to address the PSP efficiently. The proposed enhanced method is introduced to improve the WSO optimization performance and find better schedules for the PSP by hybridizing the WSO components with a well-known optimization algorithm called equilibrium optimizer. The proposed method is called the white shark equilibrium optimizer (WSEO). The proposed method is operated through a residential IoT system to manage home appliances efficiently. Moreover. the PSP is mathematically formulated as multi-objective PSP considering three main objectives, including electricity bills, power consumption balance, and users’ comfortabilities. In the evaluation stage, a new case study in the United Arab Emirates (UAE) is proposed that contains most of the available appliances in the UAE. The evaluation is presented in three main phases, including original, original with a hybrid approach, and hybrid approach evaluations. The proposed WSEO outperformed all compared methods in optimizing the PSP.