Welcome to ALPypeOpt documentation!

ALPypeOpt or AnyLogic Python Pipe for Optimization is an open source library for connecting AnyLogic simulation models with state-of-the-art black box optimization frameworks such as scikit-optimize, optuna , hyperopt and bayesian optmization.

General:

• ALPypeOpt
  • Features
  • Environments
  • Installation
  • Requirements
  • General architecture
• The AnyLogic Connector
  • Add ALPypeOptConnector library to your AnyLogic Palette
  • Drag and drop an instance of ALPypeOptConnector
  • Implement ALPypeOptClientController
• How to optimize your simulation model. The GPP example.
  • Create an AnyLogicModel connection instance
  • Create variable ranges and wrap your simulation environment within a function
  • Important note on AnyLogic console error

Advanced:

• Running your model directly from AnyLogic
• Running alpypeopt in a docker container
• Scaling your optimization process
• Running your simulation model as an AnyLogic CustomExperiment
• Most common issues and how to troubleshoot them
• API

Indices and tables

• Index

• Module Index

• Search Page