## 5 Conclusions and Future Works  
In this study, an innovative evolutionary optimization technique inspired by the locomotor behavior of lemurs was developed. The proposed algorithm is called Lemur Optimizer (LO) which included one operator to simulate how lemurs escape from predator attacks and search for food. The proposed algorithm is investigated from different aspects which are: convergence behavior, exploitation, and exploration. The conclusion can be summarized as follows:  
* An exhaustive analysis was performed on 23 mathematical benchmark functions. In contrast to other state-of-the-art metaheuristic algorithms, LO is found to be sufficiently competitive. * Three structural engineering problems (i.e., Transmission network expansion planning (TNEP) problem, The bifunctional catalyst blend optimal control problem, and Parameter estimation for frequency-modulated (FM) sound waves) are studied and used to evaluate the performance of the proposed algorithm. * The results show that LO is very competitive when compared to other metaheuristic algorithms. As a future direction, we will develop a multi-objective version of the LO algorithm. Another future work can be introducing a binary version LO algorithm. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app121910057/sl](https://www.mdpi.com/article/10.3390/app121910057/sl)]([https://www.mdpi.com/article/10.3390/app121910057/sl](https://www.mdpi.com/article/10.3390/app121910057/sl)), Table S1: Comparative performance of the LO algorithm with swarm algorithms based on functions F1-F7; Table S2: Comparative performance of the LO algorithm with swarm algorithms based on functions F8-F23; Table S3: p_values of the Wilcoxon statistical test between the proposed LO algorithm and ABC, SCA, BA, FPA, JAYA, and LEO-MPA. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Algorithm** & **Best** & **Mean** & **Median** & **Worst** & **Stdv** \\ \hline LO & \(\mathbf{0.000000\times 10^{0}}\) & \(0.000000\times 10^{0}\) & \(0.000000\times 10^{0}\) & \(0.000000\times 10^{0}\) & \(0.000000\times 10^{0}\) \\ HMA & \(1.167400\times 10^{-11}\) & \(2.094900\times 10^{0}\) & \(6.084700\times 10^{-10}\) & \(1.137400\times 10^{1}\) & \(4.306400\times 10^{0}\) \\ DE & \(\mathbf{0.000000\times 10^{0}}\) & \(6.044800\times 10^{-13}\) & \(1.085400\times 10^{-27}\) & \(1.312700\times 10^{-11}\) & \(2.638800\times 10^{-12}\) \\ APS 9 & \(\mathbf{0.000000\times 10^{0}}\) & \(1.193500\times 10^{1}\) & \(1.481300\times 10^{1}\) & \(1.869800\times 10^{1}\) & \(6.516900\times 10^{0}\) \\ D00E-RHC & \(5.020000\times 10^{-20}\) & \(8.910000\times 10^{0}\) & N/A & \(1.560000\times 10^{1}\) & \(6.370000\times 10^{0}\) \\ CHIO & \(9.057300\times 10^{0}\) & \(1.831100\times 10^{1}\) & \(1.706000\times 10^{1}\) & \(4.023300\times 10^{1}\) & \(8.153800\times 10^{0}\) \\ HDE & \(7.209300\times 10^{-15}\) & \(8.769700\times 10^{-1}\) & \(1.236200\times 10^{-11}\) & \(1.175700\times 10^{1}\) & \(3.043900\times 10^{0}\) \\ ADE & \(\mathbf{0.000000\times 10^{0}}\) & \(3.852600\times 10^{0}\) & \(0.000000\times 10^{0}\) & \(1.702100\times 10^{1}\) & \(5.690000\times 10^{0}\) \\ IMO & \(\mathbf{0.000000\times 10^{0}}\) & \(8.989400\times 10^{-1}\) & \(0.000000\times 10^{0}\) & \(1.230600\times 10^{1}\) & \(3.126600\times 10^{0}\) \\ CDASA & \(3.278900\times 10^{-18}\) & \(0.01151500\times 10^{-3}\) & \(1.137600\times 10^{1}\) & \(2.117100\times 10^{1}\) & \(7.095500\times 10^{0}\) \\ GA-MPC & \(\mathbf{0.000000\times 10^{0}}\) & \(0.000000\times 10^{0}\) & \(0.000000\times 10^{0}\) & \(0.000000\times 10^{0}\) & \(0.000000\times 10^{0}\) \\ KHABC & \(1.231000\times 10^{1}\) & \(2.231000\times 10^{1}\) & N/A & \(2.779000\times 10^{1}\) & \(3.530000\times 10^{0}\) \\ AABC & \(3.669600\times 10^{-1}\) & N/A & N/A & N/A & N/A \\ ABC & \(2.772500\times 10^{0}\) & N/A & N/A & N/A & N/A \\ \hline \hline \end{tabular}
\end{table}