## 6 Proposed Schemes
### Multi-Verge Optimizer (MVO)
Following are the steps for implementing this technique. \[x_{i}^{j}=\left\{ \begin{array}{cc}\left\{\begin{array}{cc}X_{j}+TDR\times \left(\left(ub_{j}-lb_{j}\right)\times r_{4}+lb_{j}\right)&\\ X_{j}-TDR\times\left(\left(ub_{j}-lb_{j}\right)\times r_{4}+lb_{j}\right)&\\ x_{i}^{j}&\end{array}\right.&\begin{array}{cc}r3<0.5\;\;,\;r2<\text{WEP}\\ r3\geq 0.5\;\;,\;r2\geq\text{WEP}\end{array}\right.\end{array} \right. \tag{20}\]  
\(X_{j}\) is the \(j_{th}\) term of the best universe produced up to now; TDR and WEP are the two coefficients; \(lb_{j}\) and \(ub_{j}\) indicate the lower and upper bounds respectively; r2, r3 and \(r_{4}\) appear randomly which may be 1 or 0. Traveling distance rate (TDR) and the wormholes-existing probability (WEP) are two prime coefficients. WEP must be increased linearly during optimization. TDR tells us about the object which is deported by the wormhole to the best universe. Mathematically, WEP and TDR are given as follows (Equation (21)):  
\[WEP=\text{min}+\;l\times\left(\frac{\text{max}-\text{min}}{L}\right) \tag{21}\]  
We have kept the value of min = 0.2 in this paper; max = 1, \(l\) is the recent iteration; and \(L\) indicates max iterations. \[TDR=1-\frac{l^{1/p}}{L^{1/p}} \tag{22}\]  
In this paper we have kept the value of p equal to 6 and it shows the accuracy of exploitation. Algorithm 1 shows the stepwise process, whereas Figure 4 depicts the step by step process of the proposed (MVO) algorithm via a flow chart. ```
1 Parameters initialization: WEP, TDR, maximum size of the pop., number of iterations etc. Input: DAP \((\rho(t))\), \(\lambda_{s}\), \(\lambda_{ns}\), \(T_{s}\), \(T_{f}\) Specify LOTs for all machines Specify power ratings of all appliances in a load unit **while**_Numbers of iterations < population size_**do
2 fori =1: Xdo
3 forj =1: Ydo
4 end for
5 Find used-energy price Evaluate the price of all LOTs for consumer smart appliances in a load unit Evaluate \(P_{best}\) Assign \(P_{best}\) to \(L_{best}\) New solution is evaluated Update LOTs of appliances Find \(P_{best}\) and \(L_{best}\) Assign \(L_{best}\) to \(G_{best}\)
6 end for
7
8 end for
9 Output:\(\varphi_{d_{s}}^{Total}\), PAR

### Sine-Cosine Algorithm (SCA)

We are working on a university campus solution. For that, we have divided the campus into sections--an academic block, a hostel, a staff hostel, a canteen and searchlights, to deal with each block separately. Each block is treated as a smart building (SB). A smart building is connected to the EMC and EMC is further connected to the smart meter (SM). SM establishes the connection with the SG. In this study, we propose that each section of the campus will be connected to separate EMC. EMC automatically schedules all shiftable appliances, while a smart meter establishes a two-way link between SG and SB. Thus, to utilize energy efficiently on the campus and to make the electricity cost per month minimal, and to reduce PAR, we used the population-based optimization algorithm called the sine cosine algorithm (SCA). We took electricity cost and PAR our main objectives keeping user discomfort within the threshold. SCA uses sine and cosine functions to move and fluctuate the random candidate solution towards the best solution. Several other parameters and variables can be integrated into this algorithm to make the search for the optimal solution easier, faster, more accurate and more precise. According to [30], SCA is able to explore the different regions of search space, avoid local optima and converge toward the global optimum. Initially, the SCA algorithm proposes a set of the random candidate solution. After that, through iterations, the algorithms try to improve those randomly selected solutions. As we know that the population-based algorithms move towards the optima stochastically, so there is no information about the iteration number in which optima will occur. However, by increasing the number of search agents and iterations, the probability of success increases.