pymoo: Multi-objective Optimization in Python

Фреймворк предлагает современные алгоритмы одно- и многоцелевой оптимизации, а также множество других функций, связанных с многоцелевой оптимизацией, таких как визуализация и принятие решений. pymoo доступен на PyPi. Список алгоритмов:
- GA: Genetic Algorithm
- BRKGA: Biased Random Key Genetic Algorithm
- DE: Differential Evolution
- PSO: Particle Swarm Optimization
- Nelder Mead
- Pattern Search
- CMA-ES
- ES: Evolutionary Strategy
- SRES: Stochastic Ranking Evolutionary Strategy
- ISRES: Improved Stochastic Ranking Evolutionary Strategy
- G3PCX: A Computationally Efficient Evolutionary Algorithm for Real-Parameter Optimization
- NSGA-II: Non-dominated Sorting Genetic Algorithm
- R-NSGA-II: Reference Point Based Multi-Objective Optimization Using Evolutionary Algorithms
- NSGA-III
- U-NSGA-III
- R-NSGA-III
- MOEA/D
- C-TAEA
- AGE-MOEA: Adaptive Geometry Estimation based MOEA
- AGE-MOEA2: Adaptive Geometry Estimation based MOEA
- RVEA: Reference Vector Guided Evolutionary Algorithm
- SMS-EMOA: Multiobjective selection based on dominated hypervolume
- D-NSGA-II: Dynamic Multi-Objective Optimization Using Modified NSGA-II
- KGB-DMOEA: Knowledge-Guided Bayesian Dynamic Multi-Objective Evolutionary Algorithm

https://github.com/anyoptimization/pymoo-data/raw/main/animation.gif

https://www.pymoo.org/