## 4 Results
### Experiment Three: Test the Performance of the Proposed Model in Terms of System Utilization  
Objective: To measure the optimum solution for the resources available within the grid network, this set of experiments was conducted to illustrate the effect of three different optimization techniques to minimize the number of resources with the grid. The aim is to decrease the number of resources with respect to grid size to enhance grid utilization. Table 2 illustrates the parameters for factors and levels of setup of different optimization techniques. The error estimation parameter value \(\varepsilon\) represents the fitness functions. Observation: The results shown in Figures 10-12 reveal that as the number of iterations increases, the \(\xi\) value increases for GA, ACO, and PSO, respectively. The increasing of the curve tends to become stable approximately after the 8 th iteration for both PSO and ACO, while GA reaches stability at the final iteration to obtain an optimal value. It can also be observed that the PSO optimization technique yields the best \(\xi\) value as it reaches a stable value faster than ACO. Figure 13 shows the system utilization resources as a function of optimal size (number of iterations for each optimization technique). As the optimization size increases, the system utilization decreases. When comparing the three optimization algorithms with respect to system utilization, the results confirm that they almost give the same results (about 75%) but it could be shown that the PSO algorithm gives the best performance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Factors** & **Minimum Level** & **Maximum Level** \\ \hline Genetic Algorithm (GA) & & \\ Number of chromosomes & 32 & 64 \\ Number of iterations & 500 & 1000 \\ Cross-mutation rate & 0.4 & 0.8 \\ Mutation rate & 0.01 & 0.02 \\ \hline Ant Colony Optimization (ACO) & & \\ Number of ants & 32 & 64 \\ Number of iterations & 500 & 1000 \\ Weight of pheromone (\(\alpha\)) & 1 & 2 \\ Evaporation rate (\(\lambda\)) & 0.4 & 0.8 \\ \hline Particle Swarm Optimization & & \\ (PSO) & & \\ Number of particles & 32 & 64 \\ Number of iterations & 500 & 1000 \\ Acceleration constant (\(C_{1}=C_{2}\)) & 2 & 4 \\ Inertia weight (\(W\)) & 0 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optimization techniques setup. Figure 11: Fitness function of ACO. Figure 12: Fitness function of PSO. Figure 10: Fitness function of GA. Discussion: Several conclusions were derived from the results: (i) in terms of effectiveness, the three algorithms performed equally well to search for the optimal solution. (ii) In terms of efficiency, PSO is the fastest among the three algorithms to find the optimum result, followed by ACO and then GA. (iii) In terms of consistency, all methods are proved to be consistent in solving this construction site layout problem. This study contributes to the decision in determining an appropriate solution algorithm for the construction site layout problem. The population size in the parameter design gives a significant effect on the objective solution. All methods performed equally well in terms of effectiveness. However, PSO appears to achieve the minimum mean of cost as compared to GA and ACO. This is due to the implementation of the craziness concept in the PSO mechanism; i.e., advantages of randomly reinitializing particles, a process referred to as craziness. PSO is shown to be the fastest algorithm that converges on the minimum cost. For consistency, all methods are proved to be consistent in finding solutions for both cases. This is due to the diversification components that prevent the algorithm from becoming trapped in local optima and are able to explore the solution in search space until they finally converge on the best objectives. The experimental results that were found through a simulator confirmed that the suggested model can be improved by up to 98% for load balance from the initial condition; furthermore, it outperforms the related work by an average of 26% for job response time and 33% for fault tolerance.