How to Dynamically Change a Constraint Value for Each Generation using NSGA-II in Pymoo

What will you learn?

In this tutorial, you will master the art of dynamically adjusting constraint values for each generation while utilizing the NSGA-II algorithm in Pymoo. By customizing your implementation within Pymoo’s framework, you’ll be able to seamlessly manage changing constraints as your optimization process progresses.

Introduction to the Problem and Solution

Imagine the need to adapt constraint values during each generation when applying the NSGA-II algorithm in Pymoo. This requirement arises when dynamic constraints that evolve over time or generations are essential. Our solution involves implementing a mechanism that can update these constraint values as our optimization process unfolds.

To address this challenge, we will harness the flexibility and extensibility of Pymoo, a robust multi-objective optimization library for Python. By tailoring our implementation within Pymoo’s structure, we can effectively handle varying constraints for different generations with ease.


# Import necessary libraries from pymoo
from pymoo.model.problem import Problem

# Define your custom problem class inheriting from `Problem`
class MyDynamicConstraintProblem(Problem):
    def __init__(self):

    # Override method to handle dynamic constraints logic
    def _calc_constraint(self, X, out, *args, **kwargs):
        # Implement your dynamic constraint calculations here

        # Example: Update constraint based on generation number (gen)
        gen = kwargs.get("gen")
        updated_constraint_value = self.constraint_value + gen  # Adjusting constraint based on generation

        out["G"] = updated_constraint_value

# Usage example - Initialize your problem with dynamic constraints
problem = MyDynamicConstraintProblem()

# Copyright PHD


In this code snippet:

  • We create a custom problem class MyDynamicConstraintProblem inheriting from Problem.
  • The _calc_constraint method is overridden to incorporate logic for updating constraint values dynamically.
  • Within this method, any mechanism can be implemented to adjust constraints based on parameters like generation number (gen) or other evolving criteria.
  • The example demonstrates modifying a constraint value based on the current generation number.

By following this approach and leveraging Pymoo’s customizable components like Problem, managing dynamic constraints within an evolutionary algorithm setup becomes efficient and effective.

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    Effectively managing dynamic constraints within algorithms such as NSGA-II enhances their adaptability across various scenarios. By mastering how to tailor these aspects using tools like Pymoo and custom implementations specific to unique requirements ensures efficient optimization processes tailored according to evolving needs.

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