The paper introduces pymoo, a Python-based multi-objective optimization framework designed to address the growing need for comprehensive tools in data science, machine learning, and deep learning. The framework supports various aspects of multi-objective optimization, including single, multi, and many-objective test problems, automatic differentiation for gradient calculations, parallelization techniques, and visualization methods. Key features include: 1. **Installation and Getting Started**: The paper provides a step-by-step guide on how to install pymoo and define and solve a constrained bi-objective optimization problem. 2. **Framework Architecture**: The architecture of pymoo is described, consisting of three core modules: Problems, Optimization, and Analytics. Each module is detailed with examples. 3. **Test Problems**: A variety of single, multi, and many-objective test problems are implemented, allowing for benchmarking and algorithm evaluation. 4. **Optimization Module**: This module includes evolutionary operators such as sampling, mating selection, crossover, and mutation, as well as termination criteria and decomposition techniques. 5. **Analytics**: The framework supports performance indicators like GD/IGD, Hypervolume, and visualization techniques such as scatter plots, parallel coordinate plots, and heatmaps. 6. **Multi-Criteria Decision Making**: Methods for selecting solutions from a Pareto front, including compromise programming, pseudo-weights, and high trade-off solutions, are provided. The paper also discusses related optimization frameworks and highlights the unique features of pymoo, emphasizing its extendability and community contributions. Future plans include expanding the number of algorithms and test problems, enhancing performance metrics, and improving visualization capabilities.The paper introduces pymoo, a Python-based multi-objective optimization framework designed to address the growing need for comprehensive tools in data science, machine learning, and deep learning. The framework supports various aspects of multi-objective optimization, including single, multi, and many-objective test problems, automatic differentiation for gradient calculations, parallelization techniques, and visualization methods. Key features include: 1. **Installation and Getting Started**: The paper provides a step-by-step guide on how to install pymoo and define and solve a constrained bi-objective optimization problem. 2. **Framework Architecture**: The architecture of pymoo is described, consisting of three core modules: Problems, Optimization, and Analytics. Each module is detailed with examples. 3. **Test Problems**: A variety of single, multi, and many-objective test problems are implemented, allowing for benchmarking and algorithm evaluation. 4. **Optimization Module**: This module includes evolutionary operators such as sampling, mating selection, crossover, and mutation, as well as termination criteria and decomposition techniques. 5. **Analytics**: The framework supports performance indicators like GD/IGD, Hypervolume, and visualization techniques such as scatter plots, parallel coordinate plots, and heatmaps. 6. **Multi-Criteria Decision Making**: Methods for selecting solutions from a Pareto front, including compromise programming, pseudo-weights, and high trade-off solutions, are provided. The paper also discusses related optimization frameworks and highlights the unique features of pymoo, emphasizing its extendability and community contributions. Future plans include expanding the number of algorithms and test problems, enhancing performance metrics, and improving visualization capabilities.