## 1 Introduction  
The advent of modern technology has paved the way for efficient and effective utilization of energy resources through smart appliances and applications. It is necessary to utilize the energy resources in a better way using smart appliances in residential, commercial and industrial sectors. Unlike conventional power grids, the smart grid (SG) ensures optimum utilization of energy in a better and affordable way. A SG is actually an intricate structure composed of many sections. An opportunity is offered by the SG's demand side management (DSM) in which the appliances are scheduled according to time-based electricity rates. This is financial motivation which compels the users to shift appliances from peak time slots to off-peak time slots. DSM provides two functionalities: energy management and demand response. Energy management focuses on the intelligent utilization of energy. Demand Response (DR) can be achieved through two programs: incentive based [1] and price based [2]. In incentive-based programs, the utility can wirelessly switch the state of the user's appliance from the on to the off-state by sending a short message to the load unit (LU) or smart building's (SB) energy management controller (EMC) whenever it senses the peak. Thus, in such a way the utility can automatically decrease the peak to average ratio (PAR). In the price-based program, the user is motivated to use their appliances during off-peak hours. If the user shifts appliances to off-peak hours, then as a result, he will be charged less for the single unit and will bring a dramatic decrease in his electricity bill. Actually, there is some kind of trade-off between electricity cost and the user's waiting time. Thus, scheduling can disturb the user comfort if the scheduling is not intelligent. The consumer can decrease the electricity cost, PAR and electricity consumption via interactive relationship between the smart meter (SM) and the utility [3]. According to [4] electricity consumption can be decreased 10%-30% by scheduling of appliances intelligently. It means that scheduling can perform well toward the objective of using energy efficiently. SG also integrates renewable energy resources (RES) and sensors to make the procedure easier and more transparent [5]. RES are used in emergencies and catastrophic situations. A smart meter is basically a platform for the users and utilities to interact via two way communication [6]. The smart Meter (SM) functions as a bridge between the SB or SH and the utility provider. SM sends information to the utility server after some interval of time informing the utility about the running load, previous hour information, etc. DSM was developed in the early 1980s, to balance the varying requirements of the end users and the generation capacities of power systems. After that genetic algorithm (GA) was used for the scheduling of appliances to reduce electricity cost and peak to average ratio (PAR) [7]. But objectives were obtained at the cost of user discomfort. For fulfilling the daily electricity requirement, the smart grid is becoming more and more familiar after every passing day. At the same time, a devoted and worthy electrical power system is very important to satisfy the user's energy demand. There is an utmost need for natural resources and assets. Globally, the main problem is the mismatch between consumer need and utility supply. The smart grid has a very important role in reducing the user's electricity cost; however, it increases consumer frustration in terms of waiting time to switch on their appliances. Optimization problems can be solved through a natural phenomenon. Mostly, these problems are probabilistic and occur randomly. Some random solution is taken initially for solving a problem. After creating a random solution, it is then collected and passed through some steps. It is approximately the whole design of optimization problems. One algorithm changes from another only how to collect, shift and elaborate the possible solution.