A novel hybrid grey wolf optimizer with min-conflict algorithm for power scheduling problem in a smart home

In this paper, the min-conflict local search algorithm (MCA) is hybridized with the grey wolf optimizer (GWO) for the power scheduling problem in smart home (PSPSH), and the proposed method is called GWO-MCA. MCA is utilized as a new operator of GWO to improve its exploitation capability in addressing constraint satisfaction problems, particularly scheduling problems. GWO is one of the most efficient metaheuristic algorithms which mimics the hunting behavior of grey wolves. PSPSH is a problem of scheduling smart home appliances in accordance with a dynamic pricing scheme(s) to flatten users' power consumption. PSPSH's objectives are to reduce electricity bills, improve user comfort, and maintain power systems' performance. PSPSH is formulated as a multi-objective optimization problem to achieve all objectives simultaneously. GWO-MCA is tested by using up to 36 appliance operations for 7 days. To show the effect of MCA on the convergence behavior of optimization problems, MCA is utilized as a new operator on other five popular optimization methods: genetic algorithm, particle swarm optimization, wind-driven optimization, antlion optimizer, and enhanced differential evolution. Interestingly, MCA shows a high impact on the performance of such algorithms. In addition, GWO-MCA achieves a better schedule than all compared MCA-Based methods in tackling PSPSH. In addition, the results of GWO-MCA are compared with those of three state-of-the-art hybrid methods to verify the GWO-MCA performance. The GWO-MCA excels the other comparative methods in almost all datasets used. Besides, the GWO-MCA method is compared with 20 state-of-the-art methods using their datasets. Again, GWO-MCA is able to outperform them for almost all datasets used.

Automated summary

In this paper, the hybridization of MCA with GWO for the power scheduling problem in smart home is proposed, and the method is called GWO-MCA. GWO-MCA outperforms other MCA-based methods in tackling PSPSH, and its performance is verified through comparisons with other hybrid methods.