id 
caadria2019_172 
authors 
G. BelÃ©m, Catarina and LeitÃ£o, AntÃ³nio 
year 
2019 
title 
Conflicting Goals in Architecture  A study on MultiObjective Optimisation 
source 
M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed  Proceedings of the 24th CAADRIA Conference  Volume 1, Victoria University of Wellington, Wellington, New Zealand, 1518 April 2019, pp. 453462 
summary 
Sustainability and economic factors are driving architectural practice towards more efficient designs. The application of optimization to the design process becomes essential to reduce the environmental footprint of buildings, as well as to reduce their costs. Building design requirements tend to be conflicting, involving the optimization of multiple goals simultaneously, which often translates to different compromises among the goals. Ideally, to make more informed and intelligent decisions, the architect should be given a set of design variations representing a heterogeneous sample of the optimal compromises one can achieve. In this paper, we discuss different approaches to find such compromises and we focus on multiobjective optimization algorithms that produce the required design variants, applying them in the context of an architectural case study. 
keywords 
MultiObjective Optimization; Pareto Optimization 
series 
CAADRIA 
email 
catarina.belem@tecnico.ulisboa.pt 
full text 
file.pdf (3,221,364 bytes) 
references 
Contenttype: text/plain

Attia, S., Hamdy, M., O, W. and Carlucci, S. (2013)
Computational optimisation for zero energy building design : Interviews results with twenty eight international experts
, 13th Conference of International Building Performance Simulation Association, p. 36983705




Belém, C and Leit?o, A (2018)
From Design to Optimized Design: An algorithmicbased approach
, Proceedings of the 36th eCAADe 2018, Lodz, p. 559568




Cichocka, JM, Browne, WN and Rodriguez, E (2017)
Optimization in Architectural Practice
, Proceedings of the 22nd International CAADRIA, Hong Kong, p. 387397




Corne, DW, Jerram, NR, Knowles, JD and Oates, MJ (2001)
PESAII: Regionbased Selection in Evolutionary Multiobjective Optimization
, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), California, pp. 283290




Deb, K, Agrawal, S, Pratap, A and Meyarivan, T (2002)
A fast elitist nondominated sorting genetic algorithm for multiobjective optimization: NSGAII
, IEEE Transactions on Evolutionary Computation, 6(2), pp. 182197




DíazManríquez, A, Toscano, G, BarronZambrano, JH and TelloLeal, E (2016)
A review of surrogate assisted multiobjective evolutionary algorithms
, Computational Intelligence and Neuroscience, 4, pp. 114




Evins, R. (2013)
A review of computational optimisation methods applied to sustainable building design
, Renewable and Sustainable Energy Reviews, 22, pp. 230245




Fang, Y (2017)
Optimization of Daylighting and Energy Performance Using Parametric Design, Simulation Modeling, and Genetic Algorithms
, Ph.D. Thesis, North Carolina State University




Giunta, A, Wojtkiewicz, W and Eldred, M (2003)
Overview of modern design of experiments methods for computational simulations
, 41st Aerospace Sciences Meeting and Exhibit, Nevada




Hamdy, M, Nguyen, A and Hensen, JLM (2016)
A performance comparison of multiobjective optimization algorithms for solving nearlyzeroenergybuilding design problems
, Energy and Buildings, 121, pp. 5771




Hasançabi, O, Çarbas, S, Dogan, E, Erdal, F and Saka, MP (2009)
Performance evaluation of metaheuristic search techniques in the optimum design of real size pin jointed structures
, Computers and Structures, 87, pp. 284302




Ilunga, G and Leit?o, A (2018)
Derivativefree Methods for Structural Optimization
, Proceedings of the 36th eCAADe 2018, Lodz, pp. 179186




Knowles, J and Corne, D (1999)
The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Pareto Multiobjective Optimisation
, Proceedings of the 1999 Congress on Evolutionary Computation, Washington DC, pp. 98105




Knowles, J and Corne, D (2002)
On Metrics for Comparing Nondominated Sets
, Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, Hawaii, p. 711716




Kämpf, J., Wetter, M. and Robinson, D. (2010)
A comparison of global optimization algorithms with standard benchmark functions and realworld applications using EnergyPlus
, Journal of Building Performance Simulation, 3(2), pp. 103120




Marler, R and Arora, J (2004)
Survey of multiobjective optimization methods for engineering
, Structural and Multidisciplinary Optimization, 26(6), pp. 369395




Nebro, AJ, Durillo, JJ, GarciaNieto, J, Coello, CAC, Luna, F and Alba, E (2009)
SMPSO: A new PSObased metaheuristic for multiobjective optimization
, IEEE Symposium on Computational Intelligence in MultiCriteria DecisionMaking(MCDM), Tennessee, pp. 6673




Nguyen, A., Reiter, S. and Rigo, P. (2014)
A review on simulationbased optimization methods applied to building performance analysis
, Applied Energy, 113, p. 10431058




Riquelme, N, Von Lucken, C and Baran, B (2015)
Performance metrics in multiobjective optimization
, Latin American Computing Conference (CLEI), Arequipa




Saltelli, A, Ratto, M, Andres, T, Campolongo, F, Cariboni, J, Gatelli, D and Tarantola, S (2007)
Global Sensitivity Analysis. The Primer.
, John Wiley & Sons Ltd, England




last changed 
2019/04/16 08:22 
