## 6 Conclusions  
In this study, the LHS method was applied to develop a MSCA. Moreover, to enhance the MSCA, a hybridization between the MSCA and traditional CSA was introduced. The proposed HMSCACSA--comprised of the sine function, cosine function, and Levy flight--was validated using six chosen IEEE CEC 2014 [28] and IEE CEC 2017 [27] benchmark test functions. It was found that the overall performance of the HMSCASCA outperformed other algorithms (hybrid PSOABC, PSOGWO, and PSOGSA) by up to 63.89% in terms of achieving better optimal solutions and ability to perform a global search. Additionally, the means and standard deviation values of fitness were low, ranging from \(10^{3}\) to \(10^{10}\) for the HMSCASCA. Moreover, the HMSCASCA also had a lower median fitness value compared to the other algorithms. The proposed HMSCACSA also demonstrated higher stability and reduced computational time when the population size was high and the number of dimensions was low, respectively. The proposed HMSCACSA consumed the minimum CPU time by up to 43.6% compared with the other benchmarked hybrid metaheuristics. However, a limitation occurred whenever the proposed HMSCACSA was compared with its counterparts in minimizing the F5 test function. In this case, the HMSCAS was outperformed by the MSCA in the F5 fitness minimization by 3.49%. In the future, a study could be done to apply the proposed HMSCACSA in solving Low Autocorrelation Binary Sequences (LABS) for radar communication systems [52; 53; 54], with the aim of obtaining optimized high Energy Levels (E), low peak Sidelobe Levels (SL), and a high merit factor (MF). The proposed algorithm can also be applied as a detection algorithm in Massive Multi-Input Multi-Output (MIMO) by searching for the optimum solution vector in the modulation alphabet with linear detection. Such an optimization will result in the minimization of the Bit Error Rate (BER) for large-scale antenna [55]. In addition, the proposed optimization algorithm could also be run in a more complex antenna array synthesis to optimize locations, excitation amplitudes, and the excitation phase of array elements, achieving a  
Figure 10: Diversity plot of the HMSCACSA for variants in the range of \(r\) exploration and exploitation. high antenna directivity, small half-power beamwidth, low average side lobe level suppression, and predefined nulls mitigation [56]. Conceptualization, S.J.R., H.A.R. and K.N.A.R. ; methodology, S.J.R., H.A.R. and K.N.A.R. ; software, S.J.R. ; validation, S.J.R., H.A.R., K.N.A.R. and R.N. ; formal analysis, S.J.R., H.A.R. and K.N.A.R. ; investigation, S.J.R., H.A.R., K.N.A.R. ; resources, H.A.R., K.N.A.R., R.B.A. ; data curation, S.J.R. ; writing--original draft preparation, S.J.R; writing--review and editing, H.A.R., K.N.A.R., R.N., R.B.A., N.Z.Y., M.A., M.J., M.N.M.Y., T.S. and A.M.A. ; visualization, S.J.R. ; supervision, H.A.R., K.N.A.R. and R.N. ; project administration, H.A.R. and K.N.A.R. ; funding acquisition, H.A.R., R.B.A. and N.Z.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by a Universiti Sains Malaysia RUI grant, grant number 1001/PJJAUH/8011058 and The APC was funded by Universiti Sains Malaysia RUI grant, grant number 1001/PJJAUH/8011058 and UniMAP. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: