## 4 Results and Discussions
So, the performance of optimization algorithm is best in a \(5\times 5\) array, followed by the \(4\times 4\) array, the \(3\times 3\) array, and the \(2\times 2\) array, in that order. This is because the \(5\times 5\) array provides more degrees of freedom for tuning the radiation pattern in desired form. The \(5\times 5\) array computes appropriate amplitude and phase values for the radiating elements of the array, which restore the radiation pattern in terms of mainlobe reconstruction, null locations, sidelobe levels, up to the deformation of 20 cm in both spherical and cylindrical configurations as shown in Figure 6.  
Table 1 provides a comprehensive overview of techniques utilized in synthesizing radiation patterns for conformal phased arrays. These optimization techniques are purposeful for specific applications, each carrying its own set of strengths and limitations. Their strengths encompass enhanced computational efficiency and enhanced convergence properties, while their limitations may include increased computational complexity or sensitivity to system parameters. Figure 6: Results for \(5\times 5\) conformal array. \begin{table}
\begin{tabular}{c c c c c} \hline
**Ref. ** & **Algorithm Used** & **Design** & **Technique Followed** & **Performance Comparison** \\ \hline \multirow{4}{*}{[13]} & & & Conventional phase shifting techniques failed to adjust the main beam, leading to the introduction of a genetic algorithm for optimizing element phase excitations to achieve the desired radiation pattern. & \begin{tabular}{c} Suitable for basic pattern \\ shaping, addressing design of linear arrays with a fixed and uniform cylinder radius. The algorithm exhibits limited control over sidelobe levels. \\ \end{tabular}  \\  & & & & The synthesis is suitable for single-curved linear arrays with cylindrical deformations. \\ \multirow{4}{*}{[30]} & & & & The synthesis requires prior knowledge to generate the main beam and nulls. The employed to attain a particular saddlebe level while ensuring a specific mainlobe direction. & \begin{tabular}{c} The synthesis is suitable for single-curved linear arrays with cylindrical deformations. \\ The synthesis requires prior knowledge to generate the main beam and nulls. The effect of cylindrical deformation results in the deviation of sidelobe levels and HPBW is also increased as the curvature increases. \\ \hline \multirow{4}{*}{[31]} & & & The weighting factors give this technique greater flexibility to achieve the desired pattern by iterative minimization of the objective function while considering constraints in different directions. & \begin{tabular}{c} The weighting factors give this technique greater flexibility to control the desired lobes; \\ however, the paper focuses only on a static array with a fixed radius, without exploring the results for other radii of curvature. \\ \end{tabular}  \\  & & & & The method provides a solution based on Linear Constrained \\ \multirow{4}{*}{[32]} & & & & \\  & & An arbitrarily shaped & Minimum Variance (LCMV) by incorporating the radiating element responses into the & \
\begin{tabular}{c} The analysis assumes that noise on each antenna is independent of both the impinging signals and each other. Furthermore, the analysis does not consider the effect of antenna coupling, which, if taken into account, would degrade the original radiation pattern. \\ \hline \multirow{4}{*}{[33]} & & & The Modified PSO involves \\  & & \(1\times 20\) cylindrical & \begin{tabular}{c} initializing a population, updating individual and global best values, \\ optimizing velocity and position, \\ and iterating until a stopping criterion is satisfied under clonal \\ selection operation, and ultimately \\ selecting the best individual as the final solution. \\ \end{tabular}  \\  & & & & \\ \hline \multirow{4}{*}{[17]} & & & & To address beam degradation due to array deformation, intelligent beam stabilization algorithm using DNNs (referred to as \\  & & & & \\