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pymoo: Multi-objective Optimization in Python

arXiv 2002.04504 ↗First posted 22 Jan 2020cs.NE cs.LG cs.MScited by 196

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Abstract (arXiv)

Python has become the programming language of choice for research and industry projects related to data science, machine learning, and deep learning. Since optimization is an inherent part of these research fields, more optimization related frameworks have arisen in the past few years. Only a few of them support optimization of multiple conflicting objectives at a time, but do not provide comprehensive tools for a complete multi-objective optimization task. To address this issue, we have developed pymoo, a multi-objective optimization framework in Python. We provide a guide to getting started with our framework by demonstrating the implementation of an exemplary constrained multi-objective optimization scenario. Moreover, we give a high-level overview of the architecture of pymoo to show its capabilities followed by an explanation of each module and its corresponding sub-modules. The implementations in our framework are customizable and algorithms can be modified/extended by supplying custom operators. Moreover, a variety of single, multi and many-objective test problems are provided and gradients can be retrieved by automatic differentiation out of the box. Also, pymoo addresses practical needs, such as the parallelization of function evaluations, methods to visualize low and high-dimensional spaces, and tools for multi-criteria decision making. For more information about pymoo, readers are encouraged to visit: https://pymoo.org

Key facts. Cited by 196 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jan 2020.

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Citing papers per year: 2021: 20; 2022: 30; 2023: 38; 2024: 24; 2025: 51; 2026: 33.

196 papers.

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  1. 2022Multi-Objective GFlowNetscited by 100
  2. 2022Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoderscited by 99
  3. 2023Robust Scheduling with GFlowNetscited by 67
  4. 2022Pareto Set Learning for Expensive Multi-Objective Optimizationcited by 51
  5. 2023Sample-efficient Multi-objective Molecular Optimization with GFlowNetscited by 48
  6. 2023Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritizationcited by 46
  7. 2022Joint Entropy Search for Multi-objective Bayesian Optimizationcited by 45
  8. 2022Robust Multi-Objective Bayesian Optimization Under Input Noisecited by 36
  9. 2023Large Language Model for Multi-objective Evolutionary Optimizationcited by 29
  10. 2023Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Frameworkcited by 28
  11. 2024Large Language Model-Based Evolutionary Optimizer: Reasoning with elitismcited by 27
  12. 2023Denoising Diffusion Step-aware Modelscited by 24
38 more
  1. 2024Large Language Model Agent as a Mechanical Designercited by 20
  2. 2021Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehiclescited by 19
  3. 2021BOiLS: Bayesian Optimisation for Logic Synthesiscited by 18
  4. 2021Consequence-aware Sequential Counterfactual Generationcited by 17
  5. 2023B2Opt: Learning to Optimize Black-box Optimization with Little Budgetcited by 15
  6. 2021Inverse design optimization framework via a two-step deep learning approach: application to a wind turbine airfoilcited by 14
  7. 2022Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spacescited by 14
  8. 2024OptLLM: Optimal Assignment of Queries to Large Language Modelscited by 14
  9. 2024Offline Multi-Objective Optimizationcited by 14
  10. 2025Offline Model-Based Optimization: Comprehensive Reviewcited by 14
  11. 2025OptunaHub: A Platform for Black-Box Optimizationcited by 14
  12. 2023Efficient Generator of Mathematical Expressions for Symbolic Regressioncited by 13
  13. 2021Multi-Objective Constrained Optimization for Energy Applications via Tree Ensemblescited by 12
  14. 2023DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networkscited by 11
  15. 2023qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson samplingcited by 11
  16. 2024LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorchcited by 10
  17. 2025MERGE3: Efficient Evolutionary Merging on Consumer-grade GPUscited by 9
  18. 2023EvoTorch: Scalable Evolutionary Computation in Pythoncited by 8
  19. 2023DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problemscited by 8
  20. 2023Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learningcited by 8
  21. 2023High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraftcited by 8
  22. 2024Explainable Bayesian Optimizationcited by 8
  23. 2022A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequencescited by 7
  24. 2024Inverse Design of Copolymers Including Stoichiometry and Chain Architecturecited by 7
  25. 2021Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processescited by 6
  26. 2022Design of Detectors at the Electron Ion Collider with Artificial Intelligencecited by 6
  27. 2022A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalitiescited by 6
  28. 2023RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learningcited by 6
  29. 2023Optimizing fairness tradeoffs in machine learning with multiobjective meta-modelscited by 6
  30. 2025ExLLM: Experience-Enhanced LLM Optimization for Molecular Design and Beyondcited by 6
  31. 2025Goal Conditioned Reinforcement Learning for Photo Finishing Tuningcited by 6
  32. 2025Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft designcited by 6
  33. 2025Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddingscited by 6
  34. 2025EngiBench: A Framework for Data-Driven Engineering Design Researchcited by 6
  35. 2021Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernelcited by 5
  36. 2021A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimizationcited by 5
  37. 2021Learning Interpretable Models Through Multi-Objective Neural Architecture Searchcited by 5
  38. 2022MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimationcited by 5

50 most cited of 196 All 196 (JSON)