add-optimizer

To integrate custom optimization algorithms into a model calibration loop, implement a subclass of the OptimizationAlgorithm, define the callback-driven optimize() method, and register it using the platform's command line interface.

The algorithm-to-worker contract is a required interface that dictates how your custom optimization algorithm communicates with the calibration worker, ensuring the callback-driven optimize() method correctly handles search strategies within the framework.

You register a new optimization algorithm for model calibration by executing the command 'symfluence optimization algorithm add MyOptAlgorithm' after successfully subclassing the OptimizationAlgorithm and implementing its required methods.

Yes, adding optimization algorithms requires you to implement a subclass of OptimizationAlgorithm and understand the callback-driven optimize() method to ensure the new search strategy integrates correctly with the calibration framework.

You should add a custom optimization algorithm when the default set lacks a specific optimization technique or search strategy required for your software development and model calibration needs.

Extending the calibration loop with custom optimization algorithms requires advanced implementation depth, meaning you must fully understand the callback-driven optimize() method and algorithm-to-worker contract to avoid integration failures.