Energy efficient internet of things appliance scheduling in smart grids using a crossover enhanced white shark optimizer

The increasing demand for electricity, driven by the proliferation of modern appliances, has placed significant strain on traditional power grids. Smart Grids have emerged as a viable solution to this challenge, particularly through intelligent demand-side energy management strategies such as the Appliance Energy Scheduling Problem (AESP). This study formulates AESP as a complex multi-objective optimization problem that seeks to minimize electricity bills, waiting time rate (WTR), capacity energy rate (CER), and the peak-to-average ratio, while maximizing user comfort. To effectively solve this problem, a novel hybrid optimization algorithm is proposed, the White Shark Optimizer with Crossover (WSC), which combines the exploration–exploitation capabilities of the White Shark Optimizer (WSO) with the diversity-enhancing benefits of a crossover evolutionary operator. A non-Pareto scalarization-based approach is employed to manage the multiple conflicting objectives simultaneously. The proposed approach is implemented within a residential Internet of Things framework, enabling intelligent and efficient management of home appliances by facilitating real-time monitoring, control, and optimization of energy usage. The performance of WSC is benchmarked against four other hybrid metaheuristics across seven consumption scenarios and 149 appliances. Experimental results demonstrate that WSC outperforms all baseline and hybrid counterparts in terms of convergence speed, solution quality, and overall fitness. It achieved the lowest average fitness function (FF) value of 0.2014, with a corresponding reduction of 18.6% in WTR and 15.9% in CER compared to the next-best hybrid method. Additionally, WSC maintained competitive electricity bills and peak-to-average ratio values. Overall, the proposed WSC provides a stable and efficient solution for optimizing multi-objective energy scheduling in Smart Grid environments.