pymoo

Overview

pymoo provides a unified API for multi-objective optimization via population-based evolutionary algorithms. Users define a problem by subclassing Problem or ElementwiseProblem, specifying objectives (n_obj), decision variables (n_var), and optional constraints (n_ieq_constr). Algorithms like NSGA-II and NSGA-III return a Result object containing the Pareto-optimal population, objective values, and decision variable values. pymoo separates problem definition, algorithm configuration, operator selection, and analysis — each component is independently replaceable.

When to Use

• Optimizing a design with two or more conflicting objectives (e.g., minimizing cost while maximizing performance)
• Running evolutionary algorithms (GA, DE, PSO) as black-box optimizers when gradients are unavailable
• Performing multi-objective hyperparameter search for ML models where accuracy and inference time trade off
• Computing Pareto fronts for portfolio optimization or multi-criteria decision analysis
• Customizing crossover/mutation operators for domain-specific solution encodings (binary, permutation, real-valued)
• Benchmarking optimization algorithms on standard test problems (ZDT, DTLZ, CTP)
• Use scipy.optimize instead for single-objective, gradient-available, smooth optimization

Prerequisites