## 2 Literature Review  
In the past few decades, particularly in the last two decades, a lot of work has been carried out on energy optimization. Different algorithms have been developed and deployed successfully in the realm of energy optimization. In all this work, the goals that we want to achieve are the reduction of electrical cost, reduction in PAR and maximized user comfort. All of the algorithms that were used to achieve the objectives showed beneficial results. Numerous researchers everywhere in the world are exploring various techniques to fulfill the energy demands of consumers by using smart meters (SM). For implementing different optimization techniques and making efficient use of natural resources, various algorithms have been proposed. In [11] with the help of the mixed integer linear programming approach, the authors have suggested a way for balancing the load and minimizing the cost by adopting scheduling methods for residential and populated areas. The prescribed algorithm has efficiently reduced the peak to average ratio and cost. However, they have not given due consideration to user comfort. In [12], mix integer non linear programming is used for reducing both PAR and cost. With this technique they were able to reduce the cost, but PAR was not given any importance. In [13], the authors used meta-heuristic techniques to achieve the objectives mentioned above. They used three meta-heuristic techniques; i.e., the harmony search algorithm (HSA), enhanced differential evolution (EDE) and the bacterial foraging algorithm (BFA) to efficiently utilize the available energy resources. In the end, their results are analyzed. HSA surpasses BFA and EDE in terms of cost. HSA reduced the cost of electricity well in comparison with the other two algorithms and BFA achieved the highest cost. However, the cost of the each individual algorithm is less than the unscheduled cost. The algorithm schedules appliances according to the day ahead pricing (DAP) signal provided by the utility. In every price signal, there are some peaks and dips, designed according to the energy consumption pattern of overall users. Algorithms try to move the user's appliances to off-peak hours where they can enjoy electricity for less per unit compared to occupied hours. It is shown that the maximum energy consumption in the unscheduled case is 12.0750 kWh. After algorithms executed their actions, the new values were 9.0152, 9.4750 and 9.7750 kWh in cases of HAS, EDE and BFA respectively. PAR comparison of algorithms reveals that EDE performed well. BFA did well in terms of user comfort which was assessed using waiting time as an estimating factor. Some of the researchers also used hybrid techniques by combining the steps of different algorithms to optimize the problem. In [14], the authors used a hybrid version of the genetic algorithm and moth-flame optimization algorithm and proposed the time-constrained genetic moth-flame optimization (TG-MFO) algorithm for achieving better results in terms of cost and PAR reduction. Likewise, in [15], BFA and the genetic algorithm (GA) are used to make a hybrid algorithm for energy optimization. The hybrid algorithm is a merged form of two or more algorithms. Thus, throughout the optimization process, different algorithms will be working inside a coordinated environment. The main focuses of this algorithms are cost minimization, PAR minimization and load balancing on the DSM side. The graph of the cost per day explicated that the price in case of hybrid is less compared to GA and BFA. Using the hybrid approach, 10% of the unscheduled cost is saved. Similarly, the graph of PAR showed that the PAR for BFA is low but the cost is high which shows the trade-off between cost and PAR in BFA and GA. The PAR achieved by hybrid approach is less than that achieved by GA. Thus, regarding PAR, the hybrid approach performed well. Similarly, waiting time response is better in the case of the hybrid approach.