## 4 Proposed Hybrid Modified Sine Cosine Algorithm Cuckoo Search Algorithm (HMSCACSA)
### Concept of Hybridization  
SCA uses the characteristics of sine and cosine trigonometric functions to update the solutions. However, similar to other population-based optimization algorithms, the SCA suffers from low diversity, stagnation in local optima and the skipping of true solutions [13]. Therefore, we attempted to eradicate these issues by proposing a hybrid version of the SCA. The proposed algorithm is named  
Figure 3: Convergence curves of SCA and modified Sine Cosine Algorithm (MSCA). the HMSCACSA. In this study, the CSA was integrated with MSCA to find the optimum convergence rate rapidly, thus making the method more practical for a wide range of applications without losing the attractive features of the original method. Iterative Hybridization of the Modified Sine Cosine Algorithm (MSCA) with the Cuckoo Search Algorithm (CSA)  
The initial motivation to develop a hybrid metaheuristic approach was that obtaining an efficient solution for an optimization problem is a very challenging task which depends on the correct selection of optimization techniques. The optimal solution will satisfy six main requirements: easy implementation; balance of exploration and exploitation values; every iteration must be found in a true global optimum; fast convergence; minimum parameter tuned; and minimum computational power complexity. With the HMSCACSA technique, we aimed to achieve the above six requirements. Similar to the SCA and the CSA, it is based on population algorithms and uses a population to pursue the global solution [50]. The initial solution is transferred for distribution through a solution space generated by the initial population. There is no change in the number of populations for the entire algorithm iteration. Towards reaching the optimum minima, the population is fully guided through the iteration process of reproduction (to obtain the best number of search agents with the lowest cost). The pseudocodes of the proposed HMSCACSA are presented in Algorithm 2. In this case, the new CSA is assumed from the LHS in the SCA, generated by the fitness minimum value in the current best solution that replaces the new population. ```
01:Input: The population \(X=\{X_{1},X_{2},\ldots,X_{D}\}\), the constant magnitude \(a\)
02:Output: \(X_{best}\) and the updated population \(X^{\prime}=\{X^{\prime}_{1},X^{\prime}_{2},\ldots,X^{\prime}_{N}\}\)
03:Generate a random set of population \(X\). 04:Evaluate the fitness of each and every population
05:whiledo
06:Set initial \(r_{1}\) using Equation (2)
07:Update the best solution
08:for (\(t<\) maximum number of generation)
09:Perform Latin Hypercube Sampling
10:Update \(r_{1},r_{2},r_{3}\) and \(r_{4}\)
11:Update the position of the search agents (population) using (1), (2) and (3)
12:Evaluate the fitness of each and every search agent
13:If fitness (current) \(<\) fitness (previous) then
14:Update the best solution
15:Else if fitness (current) = fitness (previous) and Number of features selected (current) \(<\) Number of features selected (previous) then
16:Update the best solution and its position, \(P^{t}_{i}=X_{best}\)
17:End
18:Get a cuckoo (4) by the minimum value of sampling LHS (5) position and fitness destination. 19:Keep the best solution (or nests with quality solutions), rank the solutions and find the current best
20:Replace the least fit search agents and update the new population
21:End
22:End
23:Return the updated solution, \(X\). and the best result (\(X_{best}\))
```  
**Algorithm 2** Pseudo-code of the HMSCACSA. The MSCA begins the search by applying the standard sine cosine functions for the number of iterations. The best-obtained solution is then passed to the CSA to accelerate the search and overcome the slow convergence of the standard SCA. To determine the success of this HMSCACSA algorithm, three elements of the parameter are gradually examined in an attempt to change the value of five samples to obtain the best iterative algorithm solution.