pymoo

Solve multi-objective optimization problems in Python using a consistent minimize interface across algorithms. Define your problem once and apply state-of-the-art algorithms to obtain diverse Pareto fronts for engineering, design, and research tasks.

Many-objective optimization is supported through algorithms like NSGA-II, NSGA-III, and MOEA/D. These algorithms handle 2-objective to many-objective tasks efficiently, allowing you to tackle complex engineering design problems with multiple competing objectives.

Run NSGA-II on benchmark ZDT1 by executing the provided sample scripts. This generates a Pareto front using the consistent minimize interface, demonstrating the workflow from problem definition to result visualization on standard test problems.

Real-world constraints can be handled using built-in strategies and flexible problem definitions. The framework provides a uniform API for constraint handling, enabling you to apply benchmarks like ZDT and DTLZ to constrained engineering and design scenarios.

Pareto front visualization tools are included to analyze optimization results. Combined with access to common test problems like ZDT and DTLZ, these visualization capabilities help you compare trade-offs among cost, performance, and reliability in your design.

Multi-objective optimization requires numpy for numerical operations and matplotlib for visualization. These dependencies support the core framework, enabling problem definitions, algorithm execution, and graphical analysis of the resulting Pareto fronts.