## 2 Related Work
used the TOU pricing model along with the integration of RESs and BSUs to minimize the end-user's electricity bill and achieve energy consumption balancing. The authors proposed a model to sale extra generated energy back to the utility, as per their prior agreement. For minimization of the end-user electricity bill, [PERSON] et al. suggested a model in [19], which works in industrial, commercial and residential areas. For optimization, the authors used GA. They also compared the different EA with GA and found that it gave a maximum decrease of 21.9% in the consumption of energy. In [20], the authors proposed objective function generalization using the DR program to minimize the residential consumer electricity bill. The authors showed that by shifting the load, unexpected peaks were observed in off-peak hours. They evaluated this later peak formation with multi-CPP and multi-TOU pricing schemes combined with DAP concepts. In [21], in order to lessen the end-user electricity cost and minimize the end-user discomfort, [PERSON] et al. developed a GA-based optimization technique for scheduling of appliances. In [22], authors presented a DSM strategy, by shifting the load from on-peak hours to off-peak hours, using DAP signal and Evolutionary Algorithm (EA). However, consumer comfort is not considered. In [23], the authors proposed a Quality of Experience (QoE)-based home energy management system. They gave the priority to the end-user's frustration. Two algorithms that run the HEM system are: \"QoE-aware Cost Saving Appliance Scheduling (Q-CSAS)\" for scheduling of controlled load and \"QoE-aware Renewable Source Power Allocation (Q-RSPA)\" for management of appliances for renewable energy sources' surplus energy. They reduced energy cost to 30-33% without RES and 43-46% with RESs for the end-user annoyance rates of 1.67-3.36 and 1.70-3.43, respectively. In [24], the authors introduced three heuristic-based algorithms: GA, ACO and BPSO, to maximize user comfort, minimize PAR and minimize electricity cost, as well. In [25], the authors proposed a hybrid GA-PSO, which is a combination of the GA and PSO algorithm, for energy management and obtaining maximum end-user comfort in smart homes. [PERSON] et al. [26] presented multi-objective EA for the minimization of electricity bill and appliances waiting time. As soon as the running appliances' load increases from a threshold, they are switched off. A multi-residential energy scheduling issue with multi-class appliances in a smart grid was discussed in [27]. The authors proposed a PL-generalized Benders algorithm (Property (P) and L-Dual-Adequacy) for bill minimization and bounded user comfort. In [28], the authors proposed a distributed algorithm for shifting the load from on-peak hours to off-peak hours. They used the game theory approach for scheduling the residential load. The Nash equilibrium convergence rate was also accelerated by the Newton technique. PAR and end-user discomfort were minimized. The aforementioned research works achieved the cost minimization and reduction of PAR at the cost of end-user's waiting time. Therefore, in this paper, using TG-MFO, we achieved a nearly-zero waiting time along with cost and PAR minimization. Table 1 depicts the achievements and limitations of the aforementioned research work.