## 2 Literature Review  
The optimal scheduling of smart home appliances is a challenging problem that is focused on by many researchers. The main objective is to minimize the consumption cost of electricity and achieve a load balance among the supply and demand sides. In the last few decades, several methodologies and techniques have been proposed by researchers for minimizing users' electricity consumption cost, reducing the PAR, and maximizing the consumers' comfort. The optimum scheduling and operation of home appliances is a non-linear, discrete, and multi-dimensional problem with multiple constraints. Several traditional, evolutionary, and swarm intelligence techniques, such as mixed-integer programming (MILP), real-time rolling optimization (RTRO), but algorithm (BA), GWO, and particle swarm optimization (PSO), etc., are considered in the literature to solve the home appliance scheduling problem [15; 16]. [PERSON] et al. [17] proposed a scheduling strategy using MILP to optimally operate household appliances for achieving a reduction in the emission rate, energy import from the grid, and financial burden on the user. [PERSON] et al. [18] presented a scheduling technique to minimize the electricity consumption cost with a PAR reduction for residential consumers in peak hours using MILP. However, their proposed model did not consider consumers' comfort. [PERSON] et al. extended the work presented in [PERSON] et al. [18] using heuristic techniques for reducing residential energy consumption during peak hours and cost minimization while incorporating consumers' comfort as well [19]. [PERSON] and [PERSON] [20] proposed an optimal strategy for a HEMS with the objectives of cost minimization and peak load reduction while incorporating an energy storage system (ESS). A MILP technique was used to solve this multi-objective problem for residential customers using a time-of-use (ToU) pricing scheme that offers low prices during the off-peak and the mid-peak hours while offering high prices during the peak hours. The integration of RERs was not considered in their proposed scheme. [PERSON] [21] presented a score-based intelligent home energy management algorithm for attaining the optimum scheduling of home appliances by considering an electricity purchase tariff and the PAR. RERs and ESS were not considered in their proposed model. A GA-based model of a HEMS was proposed in [PERSON] and [PERSON] [22] with the purpose of reducing the consumption cost of electricity for residential consumers. Models based on evolutionary algorithms were proposed in References [23; 24] to solve an energy-based optimization problem in a residential area. In the proposed models, authors presented a strategy for the scheduling of household appliances in different time intervals to minimize electricity consumption expenses and the PAR. However, the integration of RERs and an ESS were not considered in these models. A PSO is a swarm intelligence algorithm that is frequently implemented for the optimal scheduling of the HEMS [25]. [PERSON] et al. developed a binary PSO (BPSO) algorithm in [PERSON] et al. [26]. Their proposed algorithm automatically controlled the appliance scheduling by shifting the consumers' load from a high-pricing time to a low-pricing time. Since a PSO is appropriate for solving real number optimization problems, it might get stuck in a local optimum value in discrete optimization problems. HEMS is a discrete problem; therefore, a PSO algorithm is not suitable for this type of problem [27]. [PERSON] et al. [28] proposed an electron drifting algorithm (EDA) to optimally operate household appliances for achieving a reduction in the emission rate, energy import from the grid, and financial burden on the user. The main strength of the proposed EDA compared to other algorithms was its lower probability of getting stuck in a local minimum. However, this algorithm lacks the availability of explicit rules for constraints handling, which results in a high uncertainty. [PERSON] et al.