A Case Study on Evaluating Genetic Algorithms for Early Building Design Optimization: Comparison with Random and Grid Searches

2025-04-10

AI Summary

Early-stage architectural design faces a fundamental trade-off between vast, irregular design spaces and stringent computational budget constraints for energy performance optimization. Method: This study systematically compares genetic algorithms, random search, and grid search within an EnergyPlus-based simulation and parametric modeling framework. Results: Under tight computational budgets, random search achieves the fastest convergence and highest-quality solutions; grid search performs worst due to its inherent inability to handle irregular design spaces; and genetic algorithms fail to demonstrate expected superiority. These findings challenge the prevailing assumption that algorithmic complexity guarantees superior optimization performance. Instead, they reveal that problem-specific characteristics—particularly design-space irregularity and computational budget stringency—are decisive determinants of algorithmic efficacy. Consequently, the study proposes a “lightweight-methods-first” optimization paradigm, offering a novel methodological foundation for resource-constrained, performance-driven architectural design.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Search
• Machine Learning: Optimization

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

In early-stage architectural design, optimization algorithms are essential for efficiently exploring large and complex design spaces under tight computational constraints. While prior research has benchmarked various optimization methods, their findings often lack generalizability to real-world, domain-specific problems, particularly in early building design optimization for energy performance. This study evaluates the effectiveness of Genetic Algorithms (GAs) for early design optimization, focusing on their ability to find near-optimal solutions within limited timeframes. Using a constrained case study, we compare a simple GA to two baseline methods, Random Search (RS) and Grid Search (GS), with each algorithm tested 10 times to enhance the reliability of the conclusions. Our findings show that while RS may miss optimal solutions due to its stochastic nature, it was unexpectedly effective under tight computational limits. Despite being more systematic, GS was outperformed by RS, likely due to the irregular design search space. This suggests that, under strict computational constraints, lightweight methods like RS can sometimes outperform more complex approaches like GA. As this study is limited to a single case under specific constraints, future research should investigate a broader range of design scenarios and computational settings to validate and generalize the findings. Additionally, the potential of Random Search or hybrid optimization methods should be further investigated, particularly in contexts with strict computational limitations.

Problem

• Evaluating Genetic Algorithms for early building design optimization
• Comparing GA with Random and Grid Searches under tight constraints
• Assessing effectiveness of lightweight methods in computational limits

Innovation

• Genetic Algorithms compared with Random Search
• Random Search effective under tight constraints
• Hybrid optimization methods suggested for future