## 2 Literature Review
In [16], the authors have used the grasshopper optimization algorithm (GOA) and cuckoo search algorithm (CSA) for optimum scheduling of an industry machines, to reduce cost and PAR. They divided the total industry into different load units according to their running operations. The simulation results show that cost and PAR are reduced. In [17], the authors used the cuckoo search (CS) algorithm to do energy optimization. In this paper, all the parameters that are considered are based on home energy consumption pattern. In this paper, the concept of energy management controller (EMC) is used and it is assumed that each home's appliance can communicate with the EMC. The main focus here is the same as in the previous case. Simulation results showed very beneficial output. The results of CS are compared with GA. Results showed that electricity cost is minimized in both the CS and GA. GA decreases electricity by 12.64% and CS decreases costs by 13.96%. Similarly, PAR reduction in case of GA is 17.41% and in CS12.94%. We know that technology is progressing very rapidly. In the field of energy, many types of equipment, pieces of machinery, peripherals and measuring devices have been designed. Similarly, in the generation of power, many useful types of machinery have been designed and introduced to the industry that can be used to obtain energy from renewable energy sources. Solar panel and windmills are the leading sources that can generate energy from renewable energy sources. In [18], the authors have used GOA and the bacterial foraging algorithm (BFA) for an efficient energy management system in an office. They scheduled different office appliances to reduce the cost and PAR. In [19], renewable energy sources (RESs) are integrated into the SG system to make the utility more stable, reduce electricity cost and reduce waiting time. SG provides the consumer the opportunity to integrate RESs to play significant role DSM. The authors have used an energy management controller unit (EMCU) to schedule home appliances efficiently and integrate RESs. When results were compared regarding costs, the algorithm that brought about higher costs was the GA, and genetic wind driven optimization (GWDO) showed a lesser value for the cost than all the others. Likewise, in [20; 21; 22; 23], the authors proposed DSM models for multiple-home areas. They evaluated the requirements of the project using three algorithm: GA, binary particle swarm optimization (BPSO) and ant colony optimization (ACO). They used the combined form of two pricing schemes: time of use (TOU) and inclined block rate (IBR). EMC was also introduced in this system. Their simulation exposed that in terms of cost and PAR, the genetic algorithm (GA) worked well compared to other two algorithms; i.e., BPSO and ACO. In [24], the authors used a dominance-based evolutionary algorithm for a set of Pareto-optimal solutions of the given problem. They considered economical, environment friendly and user comfortable solutions of the given problem. In [25; 26], the authors proposed chance-constrained programming (CCP) for reducing the renewable energy consumption cost. The problem is formulated using mixed-integer linear programming (MILP) by considering the uncertainties in the renewable energy generation. In all these studies, the researchers tried to reduce the energy costs and maximize user comfort.