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Solving Multi-Objective Constrained Optimisation Problems using Pymoo

north hall
45 minutes


Pymoo is an open source python framework with state-of-the-art optimisation and post performance analysis capabilities. It provides an object oriented interface to solve constrained Single/Multi-Objective optimisation problems with a catalog of algorithms, customisations and post-optimisation evaluation functionalities. With additional features like Visualisation of optimal pareto-fronts, decision making, parallelization and customised sampling, Pymoo promises to be highly valuable for scalable optimisation solutions.

TalkTBD - Multiple Tracks


I will share a github repository with jupyter notebooks and datasets with the attendees, before the talk and have multiple hands on segments during the talk. The talk will take the users step-by-step through the sections mentioned below -

  1. What is an optimisation problem ?
  2. Core components of an optimisation problem ?
  3. Finding the right type of solution ?
  4. Identifying constraints, bounds and objectives
  5. Pymoo API - Object Oriented Interface, Parallelization, Visulization
  6. Hands on Example : a ML Model as an objective function, to find optimal input variables for a desired output variable
  7. Customisations using Pymoo : Termination, Crossover, Mutation etc
  8. A one-stop solution for Optimisation Problems

The speaker

Pranjal Biyani

Pranjal is a Senior AI Scientist at Polymerize, Singapore building the world's first unified Material Informatics platform to accelerate R&D using AI. He works on developing predictive models, optimisation techniques curated for small experimental datasets, multi-stage processes in continuous development systems. His interests lie in making AI interpretable, logical and reasonable.

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