## 6 Results and Discussions  
The presented model, explained in detail in Section 3, was applied to the transit services, including bus feeder services connecting the rail stations in the case study (_i.e._, PJ area). The locations of nodes (_i.e._, bus stops and rail stations) are given in Tables 3 and 4. Furthermore, the demand of each bus stop is listed in Table 6. The values for the model parameters (e.g., vehicle sizes, operating speed and costs) are specified in Table 7. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bus Stop No.** & **Demand (Passenger/h)** & **Bus Stop No.** & **Demand (Passenger/h)** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 6: Passenger demand at bus stops in the Petaling Jaya study area. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Location** & **Survey Date** & **Survey Time** \\ \hline Taman Jaya & 8–9 April & 6:30 to 9:30 a.m. \\ Asia Jaya & 10–11 April & 6:30 to 9:30 a.m. \\ Taman Paramount & 15–16 April & 6:30 to 9:30 a.m. \\ Taman Bahagia & 17–18 April & 6:30 to 9:30 a.m. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Selected LRT stations for public transit passenger questionnaires. The WCA and ICA techniques have demonstrated their viability as powerful optimization tools with great potential for solving optimization problems [31; 32; 33; 34]. The proposed model and the corresponding optimization methods were coded and run in MATLAB programming software. The optimization procedure for the transit service model involved 50 independent runs, which were performed for each of the considered optimizers. After performing sensitivity analyses for both optimizers with 50 independent runs, initial parameters for the WCA were a population size of 100, \(N_{sr}\) of eight and \(d_{max}\) of \(1\times 10^{-5}\). Accordingly, for the ICA, the initial parameters consisted of a country population of 100, a number of imperialist country of eight and a revolution rate of 0.4. The application of different optimization algorithms resulted in solutions with diverse precision values. In fact, the solutions state the accuracy of the applied methods and the method's ability to determine the optimum results. There is a close relationship between the number of function evaluations (NFEs) and the best solutions obtained. This means that the ideal situation contains the least number of NFEs and is a more accurate solution. With regard to the convergence trend of optimization algorithms and in order to draw a fair comparison between the optimizers, a maximum of 100,000 NFEs was considered the stopping condition for both optimizers. The number of generated routes is also considered a design variable and varies in a generated population. In fact, the total number of design variables can be changed in this problem in each iteration. Having numerous and changeable design variables can be considered a special feature that can categorize this model as a dynamic optimization model. The optimization results for the presented optimization algorithms are compared and discussed in this section. Table 8 shows the comparison of the best solutions attained for all cost terms using the applied optimization engines for the improved model. The obtained total cost (\(C_{T}\)) is highlighted in bold in Table 8 for the two reported algorithms.