### 1. A Multi-objective Optimization Algorithm and Process for Modelica Model
Zhang, Congcong, et al. "A Multi-objective Optimization Algorithm and Process for Modelica Model." *2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)*. IEEE, 2022.
**Problem:** In the current commercial software based on Modelica model, there are few functions for multi-objective optimization, and the current multi-objective optimization algorithm has the problems of insufficient approximation of the optimal solution set and uneven distribution of the solution set.
**Objective:** To solve this problem, Combined with the characteristics of Modelica model and NSGA-II algorithm, this paper proposes a multi-objective optimization algorithm and process for Modelica model.
**Solution:** This paper provides a multi-objective optimization design process for the current modeling and simulation platform. **The process analyzes the model variables according to ANTLR4 and transforms the tree structure**.
**Future Work:** MOO takes huge computing resources, so it is slow, the future work would be to implement parallel computing to solve this problem.
* This paper only provides the idea of the overall process, I can't find it anywhere or reproduce it. Also, the ANTLR method seems complicated.
* The future work part in this paper is interesting for me and the authors are not doing it. So I could think about implementing parallel computing.
#### 2. DESA - Optimization of variable structure Modelica models using custom annotations
Bender, Daniel. "DESA: Optimization of variable structure modelica models using custom annotations." *Proceedings of the 7th International Workshop on Equation-Based Object-Oriented Modeling Languages and Tools*. 2016.
**Contribution:** The library DESA uses custom annotations to implement the optimization task to the model. Further, the model is exported including this meta-information. The DESA optimization tool then allows to set of the optimization task in a Matlab environment and operates the optimization run. In this way, the optimization of variable structure models is achieved.