Food4Rhino

MOET — Multiobjective Evolutionary Tool

MOET is the computational engine for design exploration, optimization, and AI. It orchestrates multiobjective search, simulation loops, generation of the surrogate models and learning-based strategies to turn parametric ideas into high-performance, buildable solutions, big data. The architecture supports:

• Multiobjective optimization using advanced evolutionary and machine learning algorithms.
• Finite Element Method (FEM) analysis and performance evaluation of complex structures.
• Big data generation for training AI and surrogate models in engineering contexts.
• Material and production databases integrated with life-cycle assessment (LCA) and cost estimation workflows.
• Automated pipeline execution across Grasshopper, Python, FEM and other environments.
• Cross-institution collaboration, ensuring reproducibility and interoperability of research data.

In short, MOET unifies parametric design, simulation, and AI-driven optimization within a single modular architecture — enabling researchers and engineers to design, analyze, and evolve structures intelligently.