MOO4Modelica

An optimization framework and workflow for Modelica which supports both single- and multi-objective optimization.

GitHub Page: https://wangzizhe.github.io/MOO4Modelica

Framework

Highlights:

1. Easy to configure: All configurations can be defined in config.json.
2. SoTA algorithms: Dynamic import of algorithms from pymoo.
3. Enable use of parallel computing: For accelerated process.
4. Support transformation into feature models: To better analyze and understand large-scale models.

Structure:

./src/ 
(Feature Model Transformation)
	|-- feature_model
		|-- modelica.g4
		|-- parse_modelica.py
		|-- feature_model.py
(Optimization Operation)
	|-- config.json
	|-- config.py
	|-- optimize_main.py 
	|-- parallel_computing.py
	|-- adaptive_instance_selection.py 
	|-- optimization_libraries.py 
	|-- evaluate.py

• (Feature Model Transformation)
  • modelica.g4: an ANTLR4 grammar for Modelica files
  • parse_modelica.py: parse a Modelica model to extract it components and their parameters
  • feature_model.py: create a feature model and add the extracted components
• (Optimization Operation)
  • config.json & config.py: global settings and configurations
  • optimize_main.py: main optimization script
  • parallel_computing.py: parallel computing
  • optimization_libraries.py: dynamic import of algorithms from pymoo

Usage

https://wangzizhe.github.io/MOO4Modelica/docs/Usage.html

Example

https://wangzizhe.github.io/MOO4Modelica/docs/Example.html

Dynamic Adaptation and Orchestration of Systems with Modelica and MOO4Modelica

https://wangzizhe.github.io/MOO4Modelica/docs/OrchestrationWorkflow.html