### 1. A Multi-objective Optimization Algorithm and Process for Modelica Model
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@@ -97,66 +98,3 @@ Bender, Daniel. "DESA: Optimization of variable structure modelica models using
**Zizhe's thoughts:**
* This only works in Dymola...
### II. Frameworks
#### 0. pymoo: Multi-objective Optimization in Python
https://pymoo.org
The framework offers state-of-the-art single- and multi-objective optimization algorithms and many more features related to multi-objective optimization such as visualization and decision-making.
#### 1. **DEAP (Distributed Evolutionary Algorithms in Python)**
DEAP is a flexible framework for evolutionary algorithms. It provides tools for single and multi-objective optimization, making it suitable for a wide range of optimization problems.
Platypus is a framework for evolutionary computing with a focus on multi-objective optimization. It supports a variety of algorithms and is designed to be user-friendly.
#### 3. **PyGMO (Python Global Multiobjective Optimizer)**
PyGMO is a scientific library for massively parallel optimization. It provides a wide range of optimization algorithms, including those for multi-objective optimization.
-**Documentation**: https://esa.github.io/pygmo2/
-**GitHub**: https://github.com/esa/pygmo2
#### 4. **SciPy**
SciPy is a fundamental library for scientific computing in Python, which includes several optimization routines. While it focuses more on classical optimization algorithms, it is still quite powerful for certain types of optimization problems.
Nevergrad is a gradient-free optimization platform by Facebook AI Research. It provides a variety of algorithms suitable for optimization tasks where gradients are not available.
Optuna is an automatic hyperparameter optimization software framework, particularly for machine learning, but it can be used for general optimization tasks. It supports both single-objective and multi-objective optimization.
CMA-ES is a robust optimization algorithm suitable for difficult optimization problems. Libraries such as `pycma` provide implementations of this algorithm.