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/

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