**6. Conclusions**

This paper presents a simulation-based intelligent multi-objective optimization method of a pump-driven electro-hydrostatic actuator with AMESim and a python script. The model of an EHA driving a flight control surface is carried out by AMESim. The python script generates design parameters by using an intelligent search method and transfers them to the AMESim model. Then, the script can run a simulation of the AMESim model with a pre-defined motion and load scenario of a control surface. The python script also can obtain the results when the simulation is finished, which can then be used to evaluate the performance as the objective of optimization. The multi-objective particle swarm optimization (MOPSO) method is applied to obtain the Pareto front of solutions. In the present study, the design parameters of level length and pump displacement of the pump are optimized. An application case of optimizing an EHA driving a flight control surface is studied to validate the proposed method. Both the static objectives of weight and dynamic performances of energy consumption, rise time, and dynamic stiffness are considered. These four performances are very important for an EHA and should be optimized simultaneously. The Pareto front of these four objectives is obtained with the relevant design parameters. The results present the mapping between the design parameters to the performance and the relations between these objectives. This work indicated the proposed MOO method and this platform can be used in the design phase to help engineers to determine the design parameters according to the required performance. It is also envisaged that the proposed method can be used in similar design problems.

**Author Contributions:** Investigation, L.X.; Simulation and Analysis, L.X. and S.W.; Methodology, S.W.; Software, S.W.; Writing and Editing, L.X.; Validation, Y.X.; Project Administration D.M.

**Funding:** This research was funded by National Aviation Science Foundation (Grant No. 20160751003) and National Science Fundation of China (Grant No. 51890885, 51775014).

**Conflicts of Interest:** The authors declare that there is no conflict of interest regarding the publication of this paper.

## **Abbreviations**

The following abbreviations are used in this manuscript:


#### **References**


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