@inproceedings{anis2023comparison,
    doi = {https://doi.org/10.26868/25222708.2023.1218},
  	url = {https://publications.ibpsa.org/conference/paper/?id=bs2023_1218},
  	year = {2023},
  	month = {September},
  	publisher = {IBPSA},
  	author = {Manal  Anis  and   Sumedh  Pendurkar  and   Yun Kyu  Yi  and   Guni  Sharon},
  	title  = {Comparison between popular Genetic Algorithm ({GA})-based tool and Covariance Matrix Adaptation -- Evolutionary Strategy ({CMA-ES}) for optimizing indoor daylight},
  	booktitle = {Proceedings of Building Simulation 2023: 18th Conference of IBPSA},
  	volume  = {18},
  	isbn = {},
  	address  = {Shanghai, China},
  	series  = {Building Simulation},
  	pages = {743--749},
  	abstract = {To maximize indoor daylight, design projects commonly use commercial optimization tools to find optimum window configurations. However, experiments show that such tools either fail to find the optimal solution or are very slow to compute in certain conditions.This paper presents a comparative analysis between a gradient-free optimization technique, Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and the widely used Genetic Algorithm (GA)-based tool, Galapagos, to optimize window parameters to improve indoor daylight in six locations across different latitudes. A novel combination of daylight metrics, sDA, and ASE, is proposed for single-objective optimization comparison. Results indicate that GA in Galapagos takes progressively more time to converge, from 11 minutes in southernmost to 11 hours in northernmost latitudes, while runtime for CMA-ES is consistently around 2 hours. On average, CMA-ES is 1.5 times faster than Galapagos, while consistently producing optimal solutions. This paper can help researchers in selecting appropriate optimization algorithms for daylight simulation based on latitudes, runtime, and solution quality.},
  	issn = {2522-2708},
  	Organisation = {IBPSA},
  	Editors = {}
  }