pymoo
pymoo is a Python framework for single- and multi-objective optimization using evolutionary algorithms. Define problems as vectorized objective functions and constraints, then solve with NSGA-II, NSGA-III, MOEA/D, genetic algorithms, or differential evolution. Analyze Pareto fronts, visualize trade-off surfaces, and customize operators and callbacks. Ideal for engineering design, hyperparameter search, process optimization, and any problem with multiple conflicting objectives. Alternatives: scipy.optimize (single-objective, gradient-based), platypus (fewer algorithms), jMetalPy (Java-based, more algorithms).
Installation and usage
pymoo is a Python framework for single- and multi-objective optimization using evolutionary algorithms. Define problems as vectorized objective functions and constraints, then solve with NSGA-II, NSGA-III, MOEA/D, genetic algorithms, or differential evolution. Analyze Pareto fronts, visualize trade-off surfaces, and customize operators and callbacks. Ideal for engineering design, hyperparameter search, process optimization, and any problem with multiple conflicting objectives. Alternatives: scipy.optimize (single-objective, gradient-based), platypus (fewer algorithms), jMetalPy (Java-based, more algorithms).
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skills use pymoo