Biomimetic Generative Design for Sustainable Buildings: A Systematic Review

This review synthesizes research on 'Integration of Generative Design and Parametric Multi-Objective Optimization with Biomimicry principles to enhance overall energy consumption efficiency in residential buildings, focusing on their impact on sustainability and environmental performance' to address gaps in interdisciplinary design methodologies for sustainable architecture. The review aimed to evaluate current knowledge on biomimicry integration with generative and parametric optimization, benchmark design methodologies, identify biomimetic features influencing sustainability, compare optimization algorithms, and analyze adaptive façade roles. A systematic analysis of diverse studies employing parametric modeling, evolutionary algorithms, and biomimetic strategies across climatic contexts was conducted. Findings reveal that biomimicry combined with multi-objective optimization significantly reduces operational energy consumption—often exceeding ۴۰%—while enhancing thermal comfort and environmental outcomes. Genetic algorithms, particularly NSGA variants integrated with AI, demonstrate robust optimization performance, though empirical validation remains limited. Adaptive façades inspired by natural mechanisms effectively modulate solar gain and daylighting, contributing to occupant comfort. However, practical implementation challenges, economic feasibility, and comprehensive life cycle assessments are underexplored. These findings underscore the potential of biomimetic generative design frameworks to advance energy-efficient residential architecture. Future research should prioritize real-world validation, cost analysis, and scalable adaptive solutions to bridge the gap between computational design and sustainable building practice.

کلیدواژه ها:

Biomimicry ، Generative Design ، Multi-Objective Optimization ، Energy Efficiency ، Sustainable Architecture