Multi-objective optimization workflow based on Grasshopper genetic algorithm
Parametric tools have evolved from form generation to performance optimization engines. This workflow reduces cooling energy by 22%, validated on multiple projects.
Rhino+Grasshopper → Ladybug/Honeybee → Octopus genetic algorithm. 23 optimization variables. Objective: maximize daylight compliance + minimize annual energy.
After 500 generations: summer cooling -22%, winter heating -15%, annual energy -19%, maintaining >75% daylight compliance at no additional construction cost.
Standardized toolkit with 9 Grasshopper clusters and documentation. 80% reuse rate in new projects.
Plans for 2025: open-source release, structural-energy co-optimization, lifecycle carbon assessment, AI surrogate model for real-time optimization. Joint project with Tongji University.
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