## 1 Introduction
Specifically, the ISCA outperformed some other compatible functions at minimization, which included the functions of fixed-dimension multimodal benchmark, fixed-dimension multimodal benchmark, and unimodal benchmark. However, the performance of ISCA was not higher than the selected functions due to its unstable optimization operations. Modification tasks were performed using several techniques, including the modification of a set of candidate solutions in SCA for Opposition Based Learning (OBL) [20]. The concept of this modification was the development of more efficient approaches in different fields such as intelligent and expertsystems. Although the modified method was able to enhance the exploration of the search space, the range value of the population led to a drawback in the number of function optimization processes. [PERSON] et al. [21] proposed a Modified SCA (MSCA) using the elitism strategy and upgraded the new solution by selecting the ideal features to improve SCA accuracy. The aim of these modifications was to optimize the algorithm's quality with higher precision and reduce the number of features using wrapper-based feature selection. Based on the ten tested benchmark datasets in this experiment, it was proven that MSCA achieved higher efficiency with a lower number of features. This method was also compared with other metaheuristic algorithms, such as GA, PSO, and basic SCA. [PERSON] et al. [22] presented a new technique using SCA by modifying the position-updating equation and the conversion parameter research. This improved the SCA in terms of the position-updating weight to stabilize investigation and utilization. The modification of SCA was made on the Gaussian function to reduce the non-linear effect. Following that, the enhanced SCA was tested using 24 benchmark functions and five-dimensional values. The mean values and CPU frequency of the ISCA were small, and its improved performance was exhibited through faster convergence compared to the basic SCA. However, the ISCA showed the lowest minimization in the F6 function in comparison to the Whale Optimization Algorithm (WOA) and Teaching-Learning-Based Optimization (TLBO) algorithm. The improvement in SCA with PSO [23] led to advantages over the original SCA and other metaheuristics in terms of its precision and duration of calculation. This work improved the convergence and reduced the possibility of being confined to a local optimum solution or optimization issues, which contributed to the maximum length of continuous substrings. Not all of the test functions provided a minimum standard deviation, while exploitation in finding the substrings was poor. [PERSON] et al. [24] improved the SCA by decreasing the conversion parameter and inertia weight, and including the use of random optimal individuals with greedy Levy mutation techniques. [PERSON] et al. [25] developed a hybrid PSOSCA and Levy flight, which allowed the optimizer to identify optimal solutions. Notably, PSOCALF was more effective than the basic PSO and other algorithms in preventing the confinement of the local minimum. However, not all optimization stages in the convergence behavior were improved, besides the early convergence curves trapped in the local minimum. [PERSON] et al. [26] proposed a combination of two algorithms--SCA and PSO. The proposed algorithm was evaluated using 23 notable benchmark issues within IEEE CEC 2017 standards [27] and IEEE CEC 2014 [28]. The proposed method had a better performance in utilization and investigation and required less time to find local minima and maxima. However, a limitation remained in this method due to insufficient internal memory, not guaranteeing an accurate convergence detection. [PERSON] et al. [29] hybridized GWO and SCA, in which the grey wolves' natural movements were used to update the location of SCA. The proposed GWOSCA performance was compared with the SCA, GWO, Mean GWO (MGWO), Hybrid Approach GWO (HAGWO), WOA, PSO, and Ant Lion Optimizer (ALO).