Evaluating memetic building spatial design optimisation using hypervolume indicator gradient ascent

K. van der Blom, S. Boonstra, H. Wang, H. Hofmeyer, Michael T.M. Emmerich

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

In traditional, single objective, optimisation local optima may be found by gradient search. With the recently introduced hypervolume indicator (HVI) gradient search, this is now also possible for multi-objective optimisation, by steering the whole Pareto front approximation (PFA) in the direction of maximal improvement. However, so far it has only been evaluated on simple test problems. In this work the HVI gradient is used for the real world problem of building spatial design, where the shape and layout of a building are optimised. This real world problem comes with a number of constraints that may hamper the effectiveness of the HVI gradient. Specifically, box constraints, and an equality constraint which is satisfied by rescaling. Moreover, like with regular gradient search, the HVI gradient may overstep an optimum. Therefore, step size control is also investigated. Since the building spatial designs are encoded in mixed-integer form, the use of gradient search alone is not sufficient. To navigate both discrete and continuous space, an evolutionary multi-objective algorithm (EMOA) and the HVI gradient are used in hybrid, forming a so-called memetic algorithm. Finally, the effectiveness of the memetic algorithm using the HVI gradient is evaluated empirically, by comparing it to an EMOA without a local search method. It is found that the HVI gradient method is effective in improving the PFA for this real world problem. However, due to the many discrete subspaces, the EMOA is able to find better solutions than the memetic approach, albeit only marginally.
LanguageEnglish
Title of host publicationNumerical and Evolutionary Optimization – NEO 2017
EditorsL. Trujillo, O. Schütze, Y. Maldonado, P. Valle
Place of PublicationCham
PublisherSpringer
Pages62-82
ISBN (Print)978-3-319-96103-3
DOIs
StatePublished - 2019

Publication series

NameStudies in Computational Intelligence
Volume785

Fingerprint

Gradient methods
Multiobjective optimization
Design optimization

Cite this

van der Blom, K., Boonstra, S., Wang, H., Hofmeyer, H., & Emmerich, M. T. M. (2019). Evaluating memetic building spatial design optimisation using hypervolume indicator gradient ascent. In L. Trujillo, O. Schütze, Y. Maldonado, & P. Valle (Eds.), Numerical and Evolutionary Optimization – NEO 2017 (pp. 62-82). (Studies in Computational Intelligence; Vol. 785). Cham: Springer. DOI: 10.1007/978-3-319-96104-0_3
van der Blom, K. ; Boonstra, S. ; Wang, H. ; Hofmeyer, H. ; Emmerich, Michael T.M./ Evaluating memetic building spatial design optimisation using hypervolume indicator gradient ascent. Numerical and Evolutionary Optimization – NEO 2017. editor / L. Trujillo ; O. Schütze ; Y. Maldonado ; P. Valle. Cham : Springer, 2019. pp. 62-82 (Studies in Computational Intelligence).
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van der Blom, K, Boonstra, S, Wang, H, Hofmeyer, H & Emmerich, MTM 2019, Evaluating memetic building spatial design optimisation using hypervolume indicator gradient ascent. in L Trujillo, O Schütze, Y Maldonado & P Valle (eds), Numerical and Evolutionary Optimization – NEO 2017. Studies in Computational Intelligence, vol. 785, Springer, Cham, pp. 62-82. DOI: 10.1007/978-3-319-96104-0_3

Evaluating memetic building spatial design optimisation using hypervolume indicator gradient ascent. / van der Blom, K.; Boonstra, S.; Wang, H.; Hofmeyer, H.; Emmerich, Michael T.M.

Numerical and Evolutionary Optimization – NEO 2017. ed. / L. Trujillo; O. Schütze; Y. Maldonado; P. Valle. Cham : Springer, 2019. p. 62-82 (Studies in Computational Intelligence; Vol. 785).

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

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M3 - Chapter

SN - 978-3-319-96103-3

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van der Blom K, Boonstra S, Wang H, Hofmeyer H, Emmerich MTM. Evaluating memetic building spatial design optimisation using hypervolume indicator gradient ascent. In Trujillo L, Schütze O, Maldonado Y, Valle P, editors, Numerical and Evolutionary Optimization – NEO 2017. Cham: Springer. 2019. p. 62-82. (Studies in Computational Intelligence). Available from, DOI: 10.1007/978-3-319-96104-0_3