# Multirobot Task Planning Method Based on the Energy Penalty Strategy
## 5 Simulation Experiment Analysis
### MRTA Optimization
The specific experimental parameters are shown in Table 2.  
The results show that when the number of tasks is m = 40, the total energy consumption for completing all tasks with the number of robots \(n=3\) and \(n=4\) is reduced by 4.2% and 2.5% compared with the basic GA (without penalty mechanism), and the energy standard deviation is reduced by 60.6% and 76.5%, respectively. These findings reveal that the energy penalty strategy can stably maintain the optimal energy consumption and energy balance in genetic iteration. According to the calculation results of Experiments 1 and 2, different initial parameters, such as the number of robots and tasks and the task environment, have little impact on the algorithm performance. Furthermore, the improvement in the  
Figure 12: Iterative curve of robot energy consumption in Experiment 2: (**a**) algorithm in this paper (\(m=40\), \(n=3\)); (**b**) basic genetic algorithm (\(m=40\), \(n=3\)); (**c**) algorithm in this paper (\(m=40\), \(n=4\)); (**d**) basic genetic algorithm (\(m=40\), \(n=4\)). algorithm's robustness is directly proportional to the increase in the number of tasks and robots. The comparison of time is similar to Experiment 1 and increases as the number of tasks increases. **Experiment 3:** To compare the algorithm performance difference between this paper and the penalty function and genetic algorithm in [34], we set the number of tasks \(m=30\) and the number of robots \(n=5\) and conducted simulation experiments under the same genetic parameter settings. The comparison of energy consumption iteration curves is shown in Figure 13.  
It can be intuitively seen in the figure that the total energy consumption of the algorithm in this paper is smaller in the stable iteration stage (after about 300 iterations) and that the energy consumption curve of each robot is more concentrated. The specific operation parameters are shown in Table 3.  
The experimental data reveal that, compared with the algorithm in [34], the total energy consumption of the algorithm in the current work is reduced by 0.3%, and the standard deviation is also reduced by 29.6%. This indicates that the performance of the proposed algorithm is better than that of the basic GA. Therefore, the energy penalty strategy is effective and feasible in ensuring optimal and balanced energy consumption. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{\(m=40\), \(n=3\)} \\ \hline
**Algorithm** & **\(\boldsymbol{E(R_{1})}\) (J)** & **\(\boldsymbol{E(R_{2})}\) (J)** & **\(\boldsymbol{E(R_{3})}\) (J)** & **Sum(E) (J)** & \(\boldsymbol{\sigma}\) & \(\boldsymbol{T(s)}\) \\ \hline This paper & 657.6 & 743.7 & 732.8 & 2134.1 & 46.9 & 19.2 \\ Basic GA & 762.3 & 614.9 & 850.4 & 2227.6 & 119.0 & 16.4 \\ \hline \hline \multicolumn{8}{c}{\(m=40\), \(n=4\)} \\ \hline
**Algorithm** & **\(\boldsymbol{E(R_{1})}\) (J)** & **\(\boldsymbol{E(R_{2})}\) (J)** & **\(\boldsymbol{E(R_{3})}\) (J)** & **\(\boldsymbol{E(R_{4})}\) (J)** & **Sum(E) (J)** & \(\boldsymbol{\sigma}\) & \(\boldsymbol{T(s)}\) \\ \hline This paper & 632.3 & 570.1 & 558.0 & 568.