{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T03:27:21Z","timestamp":1772767641018,"version":"3.50.1"},"reference-count":48,"publisher":"Cambridge University Press (CUP)","issue":"4","license":[{"start":{"date-parts":[[2020,7,15]],"date-time":"2020-07-15T00:00:00Z","timestamp":1594771200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["AIEDAM"],"published-print":{"date-parts":[[2020,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Significant research has been undertaken focusing on the application of evolutionary algorithms for design exploration at conceptual design stages. However, standard evolutionary algorithms are typically not well-suited to supporting such optimization-based design exploration due to the lack of design diversity in the optimization result and the poor search efficiency in discovering high-performing design solutions. In order to address the two weaknesses, this paper proposes a hybrid evolutionary algorithm, called steady-stage island evolutionary algorithm (SSIEA). The implementation of SSIEA integrates an island model approach and a steady-state replacement strategy with an evolutionary algorithm. The combination aims to produce optimization results with rich design diversity while achieving significant fitness progress in a reasonable amount of time. Moreover, the use of the island model approach allows for an implicit clustering of the design population during the optimization process, which helps architects explore different alternative design directions. The performance of SSIEA is compared against other optimization algorithms using two case studies. The result shows that, in contrast to the other algorithms, SSIEA is capable of achieving a good compromise between design diversity and search efficiency. The case studies also demonstrate how SSIEA can support conceptual design exploration. For architects, the optimization results with diverse and high-performing solutions stimulate richer reflection and ideation, rendering SSIEA a helpful tool for conceptual design exploration.<\/jats:p>","DOI":"10.1017\/s0890060420000281","type":"journal-article","created":{"date-parts":[[2020,7,15]],"date-time":"2020-07-15T08:59:09Z","timestamp":1594803549000},"page":"458-476","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":23,"title":["SSIEA: a hybrid evolutionary algorithm for supporting conceptual architectural design"],"prefix":"10.1017","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4054-649X","authenticated-orcid":false,"given":"Likai","family":"Wang","sequence":"first","affiliation":[]},{"given":"Patrick","family":"Janssen","sequence":"additional","affiliation":[]},{"given":"Guohua","family":"Ji","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2020,7,15]]},"reference":[{"key":"S0890060420000281_ref2","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1099-0526(199903\/04)4:4<31::AID-CPLX5>3.0.CO;2-4"},{"key":"S0890060420000281_ref24","doi-asserted-by":"publisher","DOI":"10.1086\/651235"},{"key":"S0890060420000281_ref19","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1016\/j.apenergy.2013.08.061","article-title":"A review on simulation-based optimization methods applied to building performance analysis","volume":"113","author":"Nguyen","year":"2014","journal-title":"Applied Energy"},{"key":"S0890060420000281_ref21","unstructured":"Rechenraum GmbH (2019) Goat. Retrieved from: https:\/\/www.rechenraum.com\/en\/goat.html"},{"key":"S0890060420000281_ref8","first-page":"207","article-title":"Creative design exploration by parametric generative systems in architecture","volume":"29","author":"Dino","year":"2012","journal-title":"METU Journal of the Faculty of Architecture"},{"key":"S0890060420000281_ref41","doi-asserted-by":"publisher","DOI":"10.1109\/4235.585893"},{"key":"S0890060420000281_ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s10492-014-0069-z"},{"key":"S0890060420000281_ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cad.2013.01.003"},{"key":"S0890060420000281_ref11","unstructured":"Jakubiec, JA and Reinhart, CF (2011) DIVA 2.0: Integrating daylight and thermal simulations using Rhinoceros 3D, Daysim and EnergyPlus. In Proceedings of Building Simulation 2011. Sydney, Australia, International Building Performance Simulation Association, pp. 2202\u20132209."},{"key":"S0890060420000281_ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2018.04.006"},{"key":"S0890060420000281_ref47","unstructured":"Wortmann, T , Waibel, C , Nannicini, G , Evins, R , Schroepfer, T and Carmeliet, J (2017) Are genetic algorithms really the best choice for building energy optimization? In 2017 Proceedings of the Symposium on Simulation for Architecture and Urban Design. Toronto, Canada: Society for Computer Simulation International, pp. 51\u201358."},{"key":"S0890060420000281_ref34","doi-asserted-by":"crossref","unstructured":"Vierlinger, R (2019) OCTOPUS. Retrieved from https:\/\/www.food4rhino.com\/app\/octopus","DOI":"10.51291\/2377-7478.1495"},{"key":"S0890060420000281_ref42","first-page":"63","article-title":"Whither design space?","author":"Woodbury","year":"2006","journal-title":"AIE EDAM: Artificial Intelligence for Engineering Design, Analysis, and Manufacturing"},{"key":"S0890060420000281_ref15","first-page":"1","article-title":"Evolutionary algorithms for generating urban morphology: Variations and multiple objectives","author":"Makki","year":"2018","journal-title":"International Journal of Architectural Computing"},{"key":"S0890060420000281_ref7","doi-asserted-by":"crossref","unstructured":"Cichocka, J , Browne, WN and Ramirez, ER (2017) Optimization in the architectural practice: An international survey. In Proceedings of the 22nd International Conference for Computer-Aided Architectural Design Research in Asia (CAADRIA 2017), pp. 387\u2013397, Suzhou, China, 5-8 April 2017.","DOI":"10.52842\/conf.caadria.2017.387"},{"key":"S0890060420000281_ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-65338-9_14"},{"key":"S0890060420000281_ref12","unstructured":"Janssen, P (2005) A Design Method and Computational Architecture for Generating and Evolving Building Designs. The Hong Kong Polytechnic University, Hong Kong, China."},{"key":"S0890060420000281_ref38","doi-asserted-by":"crossref","unstructured":"Wang, L , Janssen, P and Ji, G (2019 a) Diversity and efficiency: A hybrid evolutionary algorithm combining the Island model with a steady-state replacement strategy. In Proceedings of the 23rd International Conference for Computer-Aided Architectural Design Research in Asia (CAADRIA 2019) Vol. 2, Wellington, New Zealand, 15-18 April 2019, pp. 593\u2013602.","DOI":"10.52842\/conf.caadria.2019.2.593"},{"key":"S0890060420000281_ref43","article-title":"Model-based optimization for architectural design: Optimizing daylight and glare in Grasshopper","volume":"1448","author":"Wortmann","year":"2017","journal-title":"Technology | Architecture + Design"},{"key":"S0890060420000281_ref36","unstructured":"Wang, L , Janssen, P and Ji, G (2018 a) Efficiency versus Effectiveness: A Study on Constraint Handling for Architectural Evolutionary Design. In Proceedings of the 23rd International Conference for Computer-Aided Architectural Design Research in Asia (CAADRIA 2018) Vol. 1 Beijing, China, 17-19 May 2018, pp. 163\u2013172."},{"key":"S0890060420000281_ref5","doi-asserted-by":"publisher","DOI":"10.3929\/ethz-a-010613319"},{"key":"S0890060420000281_ref30","doi-asserted-by":"crossref","DOI":"10.3390\/en10050637","article-title":"Performance simulation integrated in parametric 3D modeling as a method for early stage design optimization \u2013 A review","volume":"10","author":"Touloupaki","year":"2017","journal-title":"Energies"},{"key":"S0890060420000281_ref6","unstructured":"Chipperfield, AJ and Fleming, PJ (1994) Parallel Genetic Algorithms: A Survey. Research Report. ACSE Research Report 518. Department of Automatic Control and Systems Engineering. Sheffield, UK."},{"key":"S0890060420000281_ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2011.07.009"},{"key":"S0890060420000281_ref13","unstructured":"Kyropoulou, M , Ferrer, P and Subramaniam, S (2018) Optimization of intensive daylight simulations: A cloud-based methodology using HPC (high performance computing). In PLEA 2018 HONG KONG Smart and Healthy Within the 2-degree Limit (PLEA 2018). Hong Kong, China, pp. 150\u2013155."},{"key":"S0890060420000281_ref33","unstructured":"Vierlinger, R (2013) Multi Objective Design Interface. https:\/\/doi.org\/10.13140\/RG.2.1.3401.0324."},{"key":"S0890060420000281_ref10","unstructured":"Horii, H , Miki, M , Koizumi, T and Tsujiuchi, N (2002) Asynchronous migration of island parallel GA for multi-objective optimization problem. In Asia-Pacific Conference on Simulated Evolution and Learning (SEAL 2002). pp. 86\u201390, Singapore, 18-22 Nov, 2002."},{"key":"S0890060420000281_ref46","unstructured":"Wortmann, T and Schroepfer, T (2019) From optimization to performance-informed design. In 2019 Proceedings of the Symposium on Simulation for Architecture and Urban Design (SimAUD 2019). Vol. 51. Atlanta, GA, USA, pp. 261\u2013268."},{"key":"S0890060420000281_ref25","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.ress.2015.12.002","article-title":"The generalization of Latin hypercube sampling","volume":"148","author":"Shields","year":"2016","journal-title":"Reliability Engineering and System Safety"},{"key":"S0890060420000281_ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2019.01.048"},{"key":"S0890060420000281_ref27","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1017\/S089006041500044X","article-title":"A fast genetic algorithm for solving architectural design optimization problems","volume":"29","author":"Su","year":"2015","journal-title":"Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AI EDAM"},{"key":"S0890060420000281_ref48","doi-asserted-by":"crossref","unstructured":"Yousif, S and Yan, W (2018) Clustering forms for enhancing architectural design optimization. In Proceedings of the 23rd International Conference for Computer-Aided Architectural Design Research in Asia (CAADRIA 2018) Vol. 2 , pp. 431\u2013440, Beijing, China, 17-19 May 2018.","DOI":"10.52842\/conf.caadria.2018.2.431"},{"key":"S0890060420000281_ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compenvurbsys.2011.08.001"},{"key":"S0890060420000281_ref39","doi-asserted-by":"publisher","DOI":"10.3390\/su11246965"},{"key":"S0890060420000281_ref37","doi-asserted-by":"crossref","unstructured":"Wang, L , Janssen, P and Ji, G (2018 b) Utility of evolutionary design in architectural form finding: An investigation into constraint handling strategies. In International Conference on Design Computing and Cognition. Cham: Springer, pp. 177\u2013194. https:\/\/doi.org\/10.1007\/978-3-030-05363-5_10.","DOI":"10.1007\/978-3-030-05363-5_10"},{"key":"S0890060420000281_ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2018.03.033"},{"key":"S0890060420000281_ref44","volume-title":"Efficient, Visual, and Interactive Architectural Design Optimization with Model-based Methods","author":"Wortmann","year":"2018"},{"key":"S0890060420000281_ref20","doi-asserted-by":"crossref","first-page":"2167","DOI":"10.1007\/s12541-015-0279-7","article-title":"A guideline for parameter setting of an evolutionary algorithm using optimal latin hypercube design and statistical analysis","volume":"16","author":"Park","year":"2015","journal-title":"International Journal of Precision Engineering and Manufacturing"},{"key":"S0890060420000281_ref14","doi-asserted-by":"crossref","DOI":"10.1007\/s10898-006-9056-6","article-title":"On initial populations of a genetic algorithm for continuous optimization problems","volume":"37","author":"Maaranen","year":"2007","journal-title":"Journal of Global Optimization"},{"key":"S0890060420000281_ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2002.1007058"},{"key":"S0890060420000281_ref26","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1016\/j.renene.2018.09.057","article-title":"Ineffectiveness of optimization algorithms in building energy optimization and possible causes","volume":"134","author":"Si","year":"2019","journal-title":"Renewable Energy"},{"key":"S0890060420000281_ref16","doi-asserted-by":"crossref","unstructured":"Montgomery, J and Chen, S (2012) A simple strategy for maintaining diversity and reducing crowding in differential evolution. In 2012 IEEE Congress on Evolutionary Computation, CEC 2012, (June 2014). https:\/\/doi.org\/10.1109\/CEC.2012.6252891.","DOI":"10.1109\/CEC.2012.6252891"},{"key":"S0890060420000281_ref3","unstructured":"Bradner, E , Iorio, F and Davis, M (2014) Parameters tell the design story: Ideation and abstraction in design optimization. In 2014 Proceedings of the Symposium on Simulation for Architecture and Urban Design, Tampa, FL, USA: Society for Computer Simulation International, pp. 172\u2013197."},{"key":"S0890060420000281_ref40","first-page":"33","article-title":"The island model genetic algorithm: On separability, population size and convergence","volume":"7","author":"Whitley","year":"1999","journal-title":"Journal of Computing and Information Technology"},{"key":"S0890060420000281_ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2018.10.023"},{"key":"S0890060420000281_ref28","doi-asserted-by":"publisher","DOI":"10.1002\/9780470496916"},{"key":"S0890060420000281_ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.113637"},{"key":"S0890060420000281_ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2019.109243"}],"container-title":["Artificial Intelligence for Engineering Design, Analysis and Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0890060420000281","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T20:46:19Z","timestamp":1667421979000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0890060420000281\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,15]]},"references-count":48,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,11]]}},"alternative-id":["S0890060420000281"],"URL":"https:\/\/doi.org\/10.1017\/s0890060420000281","relation":{},"ISSN":["0890-0604","1469-1760"],"issn-type":[{"value":"0890-0604","type":"print"},{"value":"1469-1760","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,15]]},"assertion":[{"value":"Copyright \u00a9 The Author(s), 2020. Published by Cambridge University Press","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}}]}}