## 3 Design of IDPGMFO
### Configuration of Simulation Platform
Table 2 is used to show the preference matching degree, network loss and dual optimization objective function values for the final ordered charging plan obtained using the proposed IDPGMFO and comparison optimization algorithms. The unit of network loss power and line loss power is kW, while the preference matching degree and dual optimization objective function values are pure numerical values without units. As shown in Table 2, compared with comparison optimization algorithms, the proposed IDPGMFO exhibits significantly better preference matching degree, network loss, and dual optimization objective function values. This confirms the high optimization quality of IDPGMFO. In summary, compared with the  
effective optimization algorithms, IDPGMFO has significantly more powerful optimization performance. Simulation results for an actual example of EV OCP optimization for Chinese economic and technological development zones reveal two key contributions of the proposed moth-flame algorithm:  
(I) The iterative convergence curve illustrates the strong optimization quality of our algorithm, obtaining improved optimization solutions. (II) The iterative convergence curve shows that the improved moth-flame algorithm proposed has a fast global convergence speed. The main innovative points of the paper are as follows:  
(I) Innovations of EV OCP optimization model design for Chinese economic and technological development zones: as for Chinese economic and technological development zones, we design an optimization model considering both time consumption, electric charging cost for the tourism operating and the quality of the power supply for EV traffic network. (II) Innovations of the MFO algorithm: this paper introduces an improved moth-flame optimization algorithm with three specific novel strategies: (I) integrating a genetic mechanism in the iterative process to enhance optimization quality; (II) incorporating adaptive nonlinear decreasing strategies for selection, crossover, mutation probability, and weight coefficient; and (III) introducing the OBL mechanism to further improve the optimization quality. In summary, the IDPGMFO proposed in this paper is effective. To validate its effectiveness, a simulation comparison of an EV OCP optimization example for Chinese economic and technological development zones is conducted, demonstrating the efficacy of the proposed algorithm. However, there are several shortcomings in this research:  
(I) There is a gap between the actual scenario and the designed EV OCP optimization model for Chinese economic and technological development zones. (II) The effectiveness of the adaptive nonlinear decreasing strategies of selection, crossover, mutation probability and weight coefficient are limited. (III) The effectiveness of the introduced genetic mechanism is finite. (IV) The simulation comparison of EV OCP optimization for Chinese economic and technological development zones still lacks an effective precise EVs charging forecast algorithm. The future areas for improvement in the context of EV OCP optimization for Chinese economic and technological development zones based on the improved moth-flame optimization are as follows:  
(I) Further studies should focus on specific optimization model design and adjusting several objective functions and constraint conditions to align with reality. (II) Further studies such as Gaussian mutation, cosine or sine decreasing modes, segmented adaptive nonlinear decreasing modes, etc., should be focused on. These strategies can be introduced to strengthen optimization abilities. (III) Combining the improved MFO with other algorithms, such as differential evolution (DE), memetic algorithms, etc., could enhance the potential for optimization performance improvement. (IV) To enhance the effectiveness of the specific simulation comparison, it is necessary to conduct further research on a precise EVs charging forecast algorithm.