## 3 Proposed Methodology
### Farmland Fertility Feature Selection (F\({}^{3}\)S)
Consequently, local \(\mathrm{L}^{\mathrm{mem}}\) and global memory \(\mathrm{G}^{\mathrm{mem}}\) updation are performed, and the best solution obtained from each portion is maintained in the local memory as represented below:  
\[\mathrm{G}^{\mathrm{mem}}=\mathrm{round}(\mathrm{t}\times\mathrm{H});\ 0.1< \mathrm{t}<1 \tag{4}\]  
\[\mathrm{L}^{\mathrm{mem}}=\mathrm{round}(\mathrm{t}\times\eta);\ 0.1< \mathrm{t}<1 \tag{5}\]  
Moreover, the soil quality of each portion is changed and determined using the solutions of global memory in the farm's worst section as represented in the following models:  
\[\rho=\tau\times\mathrm{rand}(-1,1) \tag{6}\]  
\[\mathrm{F}_{\mathrm{s}}^{\mathrm{new}}=\rho\times\left[\mathrm{F}_{\mathrm{s} }^{\mathrm{ik}}-\mathrm{F}_{\mathrm{s}}^{\mathrm{G}}\right]+\mathrm{c} \tag{7}\]  
where \(\mathrm{F}_{\mathrm{s}}^{\mathrm{G}}\) randomly selects one of the global memory solutions, and \(\tau\) is a random number between 0 and 1 that is initiated at the beginning of the algorithm. Furthermore, the solutions based on both local and global memory are updated, providing the feasible solutions in each portion, but they are not integrated with the local memory. However, some of the solutions are integrated with the best solution for improving quality, as illustrated below:  
\[\delta_{\mathrm{f}}=\left\{ \begin{aligned} &\mathrm{F}_{\mathrm{s}}^{ \mathrm{new}}=\mathrm{F}_{\mathrm{s}}^{\mathrm{ik}}+\varphi_{1}\times\left[ \mathrm{F}_{\mathrm{s}}^{\mathrm{id}}-\mathrm{G}^{\mathrm{best}}\right] \mathrm{R}>\mathrm{rand}\\ &\mathrm{F}_{\mathrm{s}}^{\mathrm{new}}=\mathrm{F}_{\mathrm{s}}^{ \mathrm{ik}}+\mathrm{rand}(0,1)\times\left[\mathrm{F}_{\mathrm{s}}^{\mathrm{ id}}-\mathrm{G}^{\mathrm{best}}\right]\mathrm{else}\end{aligned} \right. \tag{8}\]  
where R indicates a random number ranging from o to 1 that represents the extent to which the solutions are combined with best global, and \(\varphi_{1}\) is an integer determined at the beginning of optimization. Finally, the optimized feature set \(\delta_{\mathrm{f}}\) is obtained as the output of this algorithm, which is further used by the classifier for intrusion detection and classification. The description of \(\mathrm{F}^{3}\mathrm{S}\) technique is presented in Algorithm 1. ```
Input : Feature set F, Output : Selected Features \(\delta_{\mathrm{f}}\) Step 1: - H for each section of land, as shown in Equation (1); Step 2: \(\rightarrow\) Initialize the populations, and determine the soil quality \(\mathrm{Q}_{\mathrm{src,tion}}\) of each portion of the farm using Equation (2); Step 3: \(\rightarrow\) Compute the fitness of quality solution in each portion \(\mathrm{fit}_{\mathrm{src,tion}}\) using Equation (3); Step 4: \(\rightarrow\) Perform local (\(\mathrm{L}^{\mathrm{mem}}\)) and global (\(\mathrm{G}^{\mathrm{mem}}\)) memory updation, where the best solutions in each portion are stored in the local memory using Equations (4) and (5); Step 5: \(\rightarrow\) Change the quality of soil in each portion of the farm, which is determined with global memory solutions in the farm’s worst section, as shown in Equations (6) and (7); Step 6: \(\rightarrow\) Update the solutions based on local memory and local memory \(\mathrm{L}^{\mathrm{post}}\), providing the feasible solutions in each section. Step 7: \(\rightarrow\) Improve the quality of solutions, as depicted in Equation (8), for obtaining the optimized set of features \(\delta_{\mathrm{f}}\). ```  
**Algorithm 1**FarmLand Fertility Feature Selection (\(\mathrm{F}^{3}\mathrm{S}\))