## 5 Simulation Results
### Performance of Algorithm  
The simulations in this section verify the efficiency of the proposed algorithm in solving the residual energy maximization problem in this paper. We use the PyOpt optimization tool package to solve the primary nonconvex problem. To this end, the following two benchmark schemes are used for comparing the proposed method. Non Sorting Genetic Algorithm II (NSGA2): This algorithm can solve nonsmooth and nonconvex single and multiobjective optimization problems based on nondominating sorting genetic algorithm. When solving more complex combinatorial optimization problems, compared with some conventional optimization algorithms, this algorithm can usually obtain better optimization results faster. We adopt the NSAG2 method to solve the problem PI directly. Feasible Direction Method (CONMIN): The feasible direction method is a representative direct exploration method for solving constrained nonlinear optimization problems through gradients, and it is also one of the main methods for solving large-scale constrained optimization design problems. We adopt the CONMIN method to solve problem P3. In the simulation, we test the performance of RSB, NSGA2, and CONMIN on solving the residual energy maximization problem in this paper. In order to test the performance of the algorithm from various aspects, we consider the following influencing factors in the experiment: the AP transmission power, the task size, and the time block length. In Figure 6, the performance comparison of the residual energy maximization problem achieved by three methods in the AP transmit power varies from 200 to 290 W has been evaluated. From Figure 6a, we can see that as the AP transmit power increases, the residual energy of the three methods increases. This is because as the power of the AP increases, the energy harvesting by the mobile device increases, and the energy consumption remains unchanged. The results show that the three strategies have successfully solved the objective  
Figure 5: Residual Energy vs. Time Block. function, and the three algorithms are all feasible in solving the objective problem. The energy efficiency of the proposed RSB algorithm outperforms NSGA2 and CONMIN, and the residual energy is increased by 8% and 40%, respectively. This is because we get an approximate optimal solution by adopting NSGA2 to solve the primary nonconvex problem P1, while the proposed algorithm linearizes the problem P1 by RLT technology to obtain the convex problem P3 and adopts convex optimization theory to simplify the primary problem which is closer to the optimal solution of the primary problem. From the figure, the maximum residual energy difference between RSB and CONMIN is large. This is because our algorithm reduces variables, reduces computation complexity, and improves computation efficiency. In Figure 7, the performance comparison of the residual energy maximization problem achieved by three methods in the task input bits varies from 80 to 170 KB. The simulation result shows the residual energy of the three algorithms decrease as the number of task-input bits increases. This is because the more the number of local and offloading tasks are executed, the higher the energy consumption will be. Therefore, the residual energy of the system shows a downward trend as the number of task-input bits increases. Figure 7a shows that when the number of task input bits is smaller, the maximum residual energy difference among RSB, NSGA, and CONMIN to execute the computation task is smaller. As the number of task-input bits increases, the performance of our algorithm declines slowly, indicating that it is more suitable for larger computation tasks. It can also be observed that the residual energy curve of the NSGA2 algorithm also declines slowly. This is because the NSGA2 algorithm performs global optimization and it is easier to find a suboptimal solution close to the optimal solution.