## 7 Performance Evaluation
### Simulation Results  
For each hill climbing experiment, the simulation was executed for 100 iterations, each iteration comprising two phases: BACA for global exploration and hill climbing for local refinement. Throughout these iterations, the coverage of the monitored area by the deployed sensors was systematically evaluated. This coverage metric, expressed as a percentage, reflected the proportion of the total area effectively covered by the strategically placed sensors. The outcomes for each iteration were recorded in both string and array formats, offering a detailed depiction of the optimization process.  
Iteration-wise coverage values were dynamically appended to the log string. This concise yet informative representation captured the evolving success of the optimization process. The coverage percentages for each iteration were systematically stored in an array. This structured data format provided a quantitative record of the simulation outcomes and facilitated statistical analysis.  
For simulated annealing, the algorithm was employed to optimize the sensor placement for coverage maximization in a predefined area. The study conducted 100 iterations of the algorithm, each iteration involving the exploration of the solution space and adjustments to the sensor positions. The coverage results for each iteration were logged, providing a comprehensive understanding of the algorithm's convergence behavior. The coverage was expressed as a percentage of the total area covered by the sensors.  
During the 100 iterations of each, instances where the coverage reached 100 percent were recorded. The frequency of achieving complete coverage provided insights into the algorithm's ability to find optimal solutions.  
The convergence plots, generated through the visualization process, vividly illustrate the progressive enhancement in coverage over iterations. These plots serve as visual proof of the algorithm's efficacy as it ascends toward optimal solutions.  
In summary, the iterative optimization process, combining the BACA for global search and localized hill climbing or simulated annealing for refinement, yields promising results.  
The results from the three experiments demonstrate that the BACA-HC approach outperformed the BACA-SA in achieving better field coverage.  
In Experiment 1, the BACA-HC achieved an average coverage of 97.19%, while the BACA-SA had an average coverage of 86.04%. Figure 7 visually depicts the superior coverage of BACA-HC, and it highlights that the results produced by BACA-HC were closer in value to those by BACA-SA.  
Moving on to Experiment 2, BACA-HC achieved an average coverage of 93.95%, surpassing BACA-SA's average coverage of 85.36%. Figure 8 provides a clear comparison of the percent coverage for both algorithms in Experiment 2.  
In Experiment 3, the trend continued, with BACA-HC maintaining its superiority, achieving an average coverage of 94.17%, while BACA-SA managed an average coverage of 87.93%. Figure 9 illustrates the percent coverage for both algorithms in Experiment 3.  
The amalgamation of ant colony optimization and hill climbing strikes a balance between exploration and exploitation, offering a reliable and effective solution for the intricate sensor placement problem. This dual-strategy approach showcases the adaptability of the algorithms in navigating the optimization landscape, consistently converging toward high-quality solutions.  
The consistent outperformance of BACA-HC across all three experiments suggests its robustness and effectiveness in ensuring better field coverage compared to BACA-SA.  
Figure 8: Experiment 2—percent coverage between BACA-SA and BACA-HC.  
Figure 7: Experiment 1—percent coverage between BACA-SA and BACA-HC.