Analysing optimisation data for multicriteria building spatial design

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

doi:10.1007/978-3-030-12598-1_53

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 proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

6 Downloads (Pure)

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.

Original language	English
Title of host publication	Evolutionary Multi-Criterion Optimization - 10th International Conference, EMO 2019, Proceedings
Editors	Carlos A. Coello Coello, Patrick Reed, Kalyanmoy Deb, Erik Goodman, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim
Publisher	Springer
Pages	671-682
Number of pages	12
ISBN (Print)	978-3-030-12597-4
DOIs	https://doi.org/10.1007/978-3-030-12598-1_53
Publication status	Published - 2019
Event	Evolutionary Multi-Criterion Optimization, 10th International Conference, (EMO2019) - East Lansing, United States Duration: 10 Mar 2019 → 13 Mar 2019 https://commerce.cashnet.com/cashnetg/selfserve/EditItem.aspx?PC=3824-REG2&ItemCount=1

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	11411
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Evolutionary Multi-Criterion Optimization, 10th International Conference, (EMO2019)
Abbreviated title	EMO2019
Country/Territory	United States
City	East Lansing
Period	10/03/19 → 13/03/19
Internet address	https://commerce.cashnet.com/cashnetg/selfserve/EditItem.aspx?PC=3824-REG2&ItemCount=1

Keywords

Building spatial design
Data analysis
Evolutionary algorithms
Mixed integer optimisation
Multicriteria optimisation

Access to Document

10.1007/978-3-030-12598-1_53

Cite this

van der Blom, K., Boonstra, S., Hofmeyer, H., & Emmerich, M. T. M. (2019). Analysing optimisation data for multicriteria building spatial design. In C. A. Coello Coello, P. Reed, K. Deb, E. Goodman, K. Klamroth, K. Miettinen, & S. Mostaghim (Eds.), Evolutionary Multi-Criterion Optimization - 10th International Conference, EMO 2019, Proceedings (pp. 671-682). (Lecture Notes in Computer Science ; Vol. 11411). Springer. https://doi.org/10.1007/978-3-030-12598-1_53

van der Blom, Koen ; Boonstra, Sjonnie ; Hofmeyer, Herm et al. / Analysing optimisation data for multicriteria building spatial design. Evolutionary Multi-Criterion Optimization - 10th International Conference, EMO 2019, Proceedings. editor / Carlos A. Coello Coello ; Patrick Reed ; Kalyanmoy Deb ; Erik Goodman ; Kathrin Klamroth ; Kaisa Miettinen ; Sanaz Mostaghim. Springer, 2019. pp. 671-682 (Lecture Notes in Computer Science ).

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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.",

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author = "{van der Blom}, Koen and Sjonnie Boonstra and Herm Hofmeyer and Emmerich, {Michael T.M.}",

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note = "Evolutionary Multi-Criterion Optimization, 10th International Conference, (EMO2019), EMO2019 ; Conference date: 10-03-2019 Through 13-03-2019",

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van der Blom, K, Boonstra, S, Hofmeyer, H & Emmerich, MTM 2019, Analysing optimisation data for multicriteria building spatial design. in CA Coello Coello, P Reed, K Deb, E Goodman, K Klamroth, K Miettinen & S Mostaghim (eds), Evolutionary Multi-Criterion Optimization - 10th International Conference, EMO 2019, Proceedings. Lecture Notes in Computer Science , vol. 11411, Springer, pp. 671-682, Evolutionary Multi-Criterion Optimization, 10th International Conference, (EMO2019), East Lansing, Michigan, United States, 10/03/19. https://doi.org/10.1007/978-3-030-12598-1_53

Analysing optimisation data for multicriteria building spatial design. / van der Blom, Koen; Boonstra, Sjonnie; Hofmeyer, Herm et al.
Evolutionary Multi-Criterion Optimization - 10th International Conference, EMO 2019, Proceedings. ed. / Carlos A. Coello Coello; Patrick Reed; Kalyanmoy Deb; Erik Goodman; Kathrin Klamroth; Kaisa Miettinen; Sanaz Mostaghim. Springer, 2019. p. 671-682 (Lecture Notes in Computer Science ; Vol. 11411).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - van der Blom, Koen

AU - Boonstra, Sjonnie

AU - Hofmeyer, Herm

AU - Emmerich, Michael T.M.

PY - 2019

Y1 - 2019

N2 - 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.

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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SP - 671

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PB - Springer

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ER -

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

Analysing optimisation data for multicriteria building spatial design

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