Open AccessOptimizing solar access and density in Tel Aviv: Benchmarking multi-objective optimization algorithms
Author(s) -
Thomas Wortmann,
Jonathan Natanian
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2042/1/012066
Subject(s) - benchmarking , context (archaeology) , metric (unit) , benchmark (surveying) , computer science , tel aviv , algorithm , environmental science , mathematical optimization , mathematics , business , engineering , geography , operations management , archaeology , library science , geodesy , marketing
This paper explores the trade-off between redeveloping an urban site with higher density and maintaining solar access for the surrounding context in the hot and dry climate of Tel Aviv. Such trade-offs are important for future urban development in the Middle East, where densification is a demographic and environmental need. We explore this trade-off with multi-objective optimization (MOO). Specifically, we benchmark seven MOO algorithms on two test problems with different, parametric typologies: courtyard and high-rise. For both problems, we aim to maximize Floor Area Ratio and the simulation-based Context Exposure Index, a novel metric based on the Israeli green building code. The high-rise emerges as the better performing typology, and HypE, SPEA2, and RBFMOpt as the most efficient and robust MOO algorithms.