# Artificial-Intelligence-Based Charger Deployment in Wireless Reshargeable Sensor Networks
## 5 Simulation Results
### Comparison of Time Complexity  
The time complexity of SABC depends on several factors, such as the amount of input data, the cooling schedule, and the convergence criterion. The main steps of SABC include generating initial solutions, calculating their objective functions' values, generating neighboring solutions by applying random perturbations and accepting or rejecting them based on a probability determined by the current temperature. The number of iterations required for SABC to converge to a near-optimal solution depends on the cooling schedule and the convergence criterion. The cooling schedule determines the rate at which the temperature is decreased during the search. A slower cooling schedule can result in a longer runtime but can also increase the probability of finding a near-optimal solution. On the other hand, a faster cooling schedule can result in a shorter runtime but can also decrease the probability of finding a near-optimal solution. The convergence criterion determines when the search is terminated. SABC is terminated when the temperature falls below a certain threshold or when a maximum number of iterations is reached. Overall, the time complexity of SABC can be approximated as O(\(kn\)), where \(n\) is the size of the input dataset and \(k\) is the number of iterations required to converge to a near-optimal solution. The time complexity of TSBC depends on several factors, such as the size of the input dataset, the neighborhood structure and the length of the tabu list. The main steps of TSBC include generating initial solutions, calculating their objective functions' values, generating neighboring solutions by applying local moves and selecting the best solution based on a tabu search strategy. The time complexity of each iteration of TSBC is O(\(n^{2}\)), where \(n\) is size of the input dataset, which corresponds to the time required to evaluate the objective function for the current solution and its neighbors. The neighborhood structure determines the set of moves that can be applied to the current solution to generate neighboring solutions. The time complexity of generating neighboring solutions can vary depending on the complexity of the neighborhood structure. The tabu list is used to avoid revisiting previously visited solutions and to prevent cycling in the search. The length of the tabu list determines the number of solutions that are prohibited from being revisited. The time  
Figure 5: Comparison of energy efficiency. complexity of maintaining the tabu list can vary depending on the implementation. The number of iterations required for TSBC to converge to a near-optimal solution depends on the convergence criterion and the quality of the initial solution. Overall, the time complexity of TSBC can be approximated as O(\(kn^{2}\)), where \(n\) is the size of the input data and \(k\) is the number of iterations required to converge to a near-optimal solution. The time complexity of GABC depends on several factors, such as the size of the input dataset, the population size, the crossover and mutation operators and the convergence criterion. The main steps of GABC include generating an initial population, evaluating the fitness of each individual, selecting parents for reproduction, applying crossover and mutation operators to create new offspring and selecting the best individuals for the next generation. The time complexity of each iteration of GABC is O(\(nm\)), where \(n\) is the size of the input data and \(m\) is the time required to evaluate the fitness of an individual. The fitness evaluation involves computing the objective function's value for each individual in the population. The time complexity of the objective function evaluation can vary depending on the complexity of the problem. The crossover and mutation operators are used to generate new offspring from the selected parents. The time complexity of the crossover and mutation operators can vary depending on the implementation and the complexity of the problem.