## 6 Conclusions and Future Research  
This paper presented a novel optimization algorithm named hybrid symbiotic organisms search with local operators (HSOS-LO). It is proposed as an innovative search for solving the CSLP problem. As a hybrid of two algorithms, SOS and local operators, HSOS-LO combines the unique searching abilities of both. SOS focuses on global search, while local operators focus on neighbourhood local \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multicolumn{8}{c}{**Location**} \\ \cline{2-10}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline
1 & 0 & 139 & 156 & 33 & 39 & 49 & 139 & 170 & 174 & 150 \\
2 & 139 & 0 & 19 & 106 & 100 & 112 & 128 & 160 & 165 & 188 \\
3 & 156 & 19 & 0 & 125 & 119 & 131 & 112 & 144 & 148 & 207 \\
4 & 33 & 106 & 125 & 0 & 12 & 23 & 111 & 143 & 147 & 123 \\
5 & 39 & 100 & 119 & 12 & 0 & 12 & 99 & 131 & 135 & 111 \\
6 & 49 & 112 & 131 & 23 & 12 & 0 & 89 & 121 & 125 & 101 \\
7 & 138 & 128 & 112 & 111 & 99 & 89 & 0 & 32 & 36 & 104 \\
8 & 170 & 160 & 144 & 143 & 131 & 121 & 32 & 0 & 9 & 42 \\
9 & 174 & 165 & 148 & 147 & 135 & 125 & 36 & 9 & 0 & 102 \\
10 & 150 & 188 & 207 & 123 & 111 & 101 & 104 & 42 & 102 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Travel distance between locations. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metaheuristic** & \multicolumn{2}{c}{**Results**} & \multicolumn{2}{c}{**Best Layout**} & \multicolumn{2}{c}{**Parameters**} \\ \hline
**Algorithms** & **Best** & **Average** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **F1–F10** & _pop\_size_ & _maxIter_ \\ \hline PSO [36] & 39184 & 39327.07 & 303.011 & 2 6 3 4 5 1 10 7 9 8 & 30 & 30 \\ ABC [36] & 39184 & 41733.77 & 2013.849 & 2 6 3 4 5 1 10 7 9 8 & 30 & 30 \\ SOS [36] & 39184 & 39243.40 & 274.206 & 2 6 3 4 5 1 10 7 9 8 & 30 & 30 \\ HSOS-LO * & 39184 & 39184 & 0 & 2 6 3 4 5 1 10 7 9 8 & 30 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparative results of DE, PSO, SOS, and HSOS-LO for Case 3.  
search. The combination of these advantages creates a new model with better consistency. The HSOS-LO was tested with three real case studies of the construction site layout and the material storage layout problems. Experimental results showed that HSOS-LO has good consistency and reliability in obtaining solutions with good quality within a reasonable time for those layout problems. According to the results, the HSOS-LO surpasses the performance of SOS, PSO, GA, and other optimization algorithms. We, thus, conclude that the novel HSOS-LO is a robust optimization tool for solving the CSLP problem. Further research may consider the application of the proposed HSOS-LO to solve other problems in a similar setting or variants of the CSLP. The proposed HSOS-LO can be further developed to solve a problem with an extended characteristic of CSLP, such as the one that considers multi-objective optimization. Conceptualization, D.P., M.-Y.C., and Y.-W.W.; Methodology, D.P., M.-Y.C., and Y.-W.W., A.A.N.P.R. and V.F.Y. ; Software, D.P. ; Validation, D.P., M.-Y.C., Y.-W.W., A.A.N.P.R. ; Formal analysis, D.P., M.-Y.C., Y.-W.W., A.A.N.P.R. ; Investigation, D.P., M.-Y.C., Y.-W.W., A.A.N.P.R. ; Resources, D.P., M.-Y.C., Y.-W.W., A.A.N.P.R. ; Data curation, D.P. and A.A.N.P.R. ; Writing-original draft preparation, D.P., M.-Y.C., and Y.-W.W., A.A.N.P.R. and V.F.Y. ; Writing--review and editing, D.P., A.A.N.P.R., V.F.Y., S.F.P., and R.N. ; Visualization, D.P., A.A.N.P.R., V.F.Y., S.F.P., and R.N. Supervision, D.P., M.-Y.C., Y.-W.W. and V.F.Y. ; Project administration, D.P. ; Funding acquisition, D.P., A.A.N.P.R., S.F.P. and R.N. All authors have read and agreed to the published version of the manuscript. This research was funded by The Ministry of Research, grant number SP DIPA-042.06.1.401516/2018.