Analysing optimisation data for multicriteria building spatial design

Koen van der Blom, Sjonnie Boonstra, Herm Hofmeyer, Michael T.M. Emmerich

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Domain experts can benefit from optimisation simply by getting better solutions, or by obtaining knowledge about possible trade-offs from a Pareto front. However, just providing a better solution based on objective function values is often not sufficient. It is desirable for domain experts to understand design principles that lead to a better solution concerning different objectives. Such insights will help the domain expert to gain confidence in a solution provided by the optimiser. In this paper, the aim is to learn heuristic rules on building spatial design by data-mining multi-objective optimisation results. From the optimisation data a domain expert can gain new insights that can help engineers in the future; this is termed innovization. Originally used for applications in mechanical engineering, innovization is here applied for the first time for optimisation of building spatial designs with respect to thermal and structural performance.
LanguageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization, 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings
EditorsKalyanmoy Deb, Erik Goodman, Carlos A. Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, Patrick Reed
PublisherSpringer
Pages671-682
Number of pages12
ISBN (Print)978-3-030-12597-4
DOIs
StatePublished - 2019
EventEvolutionary Multi-Criterion Optimization, 10th International Conference, (EMO2019) - East Lansing, United States
Duration: 10 Mar 201913 Mar 2019
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Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11411
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEvolutionary Multi-Criterion Optimization, 10th International Conference, (EMO2019)
Abbreviated titleEMO2019
CountryUnited States
CityEast Lansing
Period10/03/1913/03/19
Internet address

Fingerprint

Mechanical engineering
Multiobjective optimization
Data mining
Engineers
Hot Temperature

Cite this

van der Blom, K., Boonstra, S., Hofmeyer, H., & Emmerich, M. T. M. (2019). Analysing optimisation data for multicriteria building spatial design. In K. Deb, E. Goodman, C. A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, & P. Reed (Eds.), Evolutionary Multi-Criterion Optimization, 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings (pp. 671-682). (Lecture Notes in Computer Science ; Vol. 11411). Springer. DOI: 10.1007/978-3-030-12598-1_53
van der Blom, Koen ; Boonstra, Sjonnie ; Hofmeyer, Herm ; Emmerich, Michael T.M./ Analysing optimisation data for multicriteria building spatial design. Evolutionary Multi-Criterion Optimization, 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings. editor / Kalyanmoy Deb ; Erik Goodman ; Carlos A. Coello ; Kathrin Klamroth ; Kaisa Miettinen ; Sanaz Mostaghim ; Patrick Reed. Springer, 2019. pp. 671-682 (Lecture Notes in Computer Science ).
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van der Blom, K, Boonstra, S, Hofmeyer, H & Emmerich, MTM 2019, Analysing optimisation data for multicriteria building spatial design. in K Deb, E Goodman, CA Coello, K Klamroth, K Miettinen, S Mostaghim & P Reed (eds), Evolutionary Multi-Criterion Optimization, 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings. Lecture Notes in Computer Science , vol. 11411, Springer, pp. 671-682, Evolutionary Multi-Criterion Optimization, 10th International Conference, (EMO2019), East Lansing, United States, 10/03/19. DOI: 10.1007/978-3-030-12598-1_53

Analysing optimisation data for multicriteria building spatial design. / van der Blom, Koen; Boonstra, Sjonnie; Hofmeyer, Herm; Emmerich, Michael T.M.

Evolutionary Multi-Criterion Optimization, 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings. ed. / Kalyanmoy Deb; Erik Goodman; Carlos A. Coello; Kathrin Klamroth; Kaisa Miettinen; Sanaz Mostaghim; Patrick Reed. Springer, 2019. p. 671-682 (Lecture Notes in Computer Science ; Vol. 11411).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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AB - Domain experts can benefit from optimisation simply by getting better solutions, or by obtaining knowledge about possible trade-offs from a Pareto front. However, just providing a better solution based on objective function values is often not sufficient. It is desirable for domain experts to understand design principles that lead to a better solution concerning different objectives. Such insights will help the domain expert to gain confidence in a solution provided by the optimiser. In this paper, the aim is to learn heuristic rules on building spatial design by data-mining multi-objective optimisation results. From the optimisation data a domain expert can gain new insights that can help engineers in the future; this is termed innovization. Originally used for applications in mechanical engineering, innovization is here applied for the first time for optimisation of building spatial designs with respect to thermal and structural performance.

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van der Blom K, Boonstra S, Hofmeyer H, Emmerich MTM. Analysing optimisation data for multicriteria building spatial design. In Deb K, Goodman E, Coello CA, Klamroth K, Miettinen K, Mostaghim S, Reed P, editors, Evolutionary Multi-Criterion Optimization, 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings. Springer. 2019. p. 671-682. (Lecture Notes in Computer Science ). Available from, DOI: 10.1007/978-3-030-12598-1_53