Genetic evolution vs. function approximation: Benchmarking algorithms for architectural design optimization. Issue 3 (20th September 2018)

• Record Type: Journal Article
• Authors: Wortmann, Thomas
• Is Part Of: Journal of computational design and engineering. Volume 6:Issue 3(2019)
• Page Start: 414
• Page End: 428
• Publication Date: 2018-09-20
• Subjects: Architectural design optimization -- Black-box optimization -- Benchmarking -- Genetic algorithms -- Model-based methods; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.1016/j.jcde.2018.09.001
• Languages: English
• ISSNs: 2288-4300

Abstract

Graphical abstract: Abstract: This article presents benchmark results from seven simulation-based problems from structural, building energy, and daylight optimization. Growing applications of parametric design and performance simulations in architecture, engineering, and construction allow the harnessing of simulation-based, or black-box, optimization in the search for less resource- and/or energy consuming designs. In architectural design optimization (ADO) practice and research, the most commonly applied black-box algorithms are genetic algorithms or other metaheuristics, to the neglect of more current, global direct search or model-based, methods. Model-based methods construct a surrogate model (i.e., an approximation of a fitness landscape) that they refine during the optimization process. This benchmark compares metaheuristic, direct search, and model-based methods, and concludes that, for the given evaluation budget and problems, the model-based method (RBFOpt) is the most efficient and robust, while the tested genetic algorithms perform poorly. As such, this article challenges the popularity of genetic algorithms in ADO, as well as the practice of using them for one-to-one comparisons to justify algorithmic innovations.