## 11 Scheduled Beacon-Based Node Broadcasting
### Performance Evaluation of Metaheuristic Techniques  
Population-based metaheuristic algorithms are suitable for global searches because they can explore the entire search space and make use of local resources. However, traditional optimization algorithms such as dynamic programming, backtracking, and branch-and-bound may not be effective in solving complex and non-linear optimization problems. Exhaustive search is not feasible in these cases due to the exponential growth of the search space. The BFA is modeled after the behavior of bacteria defending their territory by following directional cues to reduce the search area to the target location with respect to their fitness function and error values. The fitness error value indicates how far a candidate solution is from the optimal solution in the search space. It is the difference between the computed fitness value and the best-known or ideal fitness value. In bio-inspired or metaheuristic algorithms, the fitness function serves as an objective criterion to measure the \"quality\" of a solution. The fitness error value represents the difference between the current solution's fitness value and the best or optimal solution's fitness value. From Figure 3a,b, the function evaluation and fitness error value are compared with the approximate of between 0 (min.) and 1 (max) coordinate. The comparison across the algorithms (GA, BFS, FA, ABC, CS, BPSO, BFA) in plot (a) reveals that BFS is the most efficient algorithm in terms of quickly reaching the lowest fitness error value. The other algorithms, FA and ABC, converge slower, indicating less  
Figure 4: (a) Comparative analysis of fitness function and fitness error value for 200 iterations; (b) results for 300 iterations. Figure 5: (a) Comparative analysis of fitness function and fitness error value for 300 iterations; (b) results for 400 iterations. optimal performance for this function. The different parameter configurations (e.g., N = 10, N = 30) influence the convergence rate of the fitness error values. Smaller configurations (e.g., N = 10) converge faster initially, but larger configurations like N = 150 or N = 200 take longer to improve the solution. Similarly, beacon nodes perform a similar function by broadcasting their signals in a specific direction to reduce the search area for non-beacon nodes. Both BFS and BNB by SRAL aim to improve the efficiency of the search process by reducing the search area and focusing on the target location. Additionally, the BFS algorithm and beacon broadcasting can be adjusted and modified based on the accumulated historical experience. Many algorithms modeled after natural processes, including BFS, FA, GSA, CS, ABC, DE, and PSO, have been proposed to address these limitations. This work aims to rigorously evaluate each algorithm, with a focus on the BFS and its ability to reach global optimum conditions. BFS is modeled after the behavior of bacteria defending their territory by following directional cues to reduce the search area to the target location. The underwater nodes also serve to increase local exploration capability which is important for maintaining a balance between exploration and exploitation, just as bacteria search abilities are improved depending on their historical performance. Then, as demonstrated in Figure 3a,b, BFS performed similarly to the employed underwater search abilities and outperformed FA, LFA, GA, BFA, and BPSO. Figure 4a shows simulations to demonstrate that the scheduling behavior of a node with a BFS (Bacterial Foraging Search) in which the BFA is working similarly when utilizing a foraging strategy with a 60-minute Simulation Time Interval (STI). Nodes with scheduled locations and equal energy status are favored to change positions more slowly. For BFA, GA, FA, LFA, and BPSO, the cost of every node shift or exchange is controllable, yet the fitness error values continue to rise.