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2021 | OriginalPaper | Buchkapitel

On the Performance of the ORTHOMADS Algorithm on Continuous and Mixed-Integer Optimization Problems

verfasst von : Marie-Ange Dahito, Laurent Genest, Alessandro Maddaloni, José Neto

Erschienen in: Optimization, Learning Algorithms and Applications

Verlag: Springer International Publishing

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Abstract

ORTHOMADS is an instantiation of the Mesh Adaptive Direct Search (MADS) algorithm used in derivative-free and blackbox optimization. We investigate the performance of the variants of ORTHOMADS on the bbob and bbob-mixint, respectively continuous and mixed-integer, testbeds of the COmparing Continuous Optimizers (COCO) platform and compare the considered best variants with heuristic and non-heuristic techniques. The results show a favourable performance of ORTHOMADS on the low-dimensional continuous problems used and advantages on the considered mixed-integer problems. Besides, a generally faster convergence is observed on all types of problems when the search phase of ORTHOMADS is enabled.

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Vorheriges Kapitel Towards a High-Performance Implementation of the MCSFilter Optimization Algorithm

Nächstes Kapitel A Look-Ahead Based Meta-heuristics for Optimizing Continuous Optimization Problems

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Titel: On the Performance of the ORTHOMADS Algorithm on Continuous and Mixed-Integer Optimization Problems
verfasst von: Marie-Ange Dahito
Laurent Genest
Alessandro Maddaloni
José Neto
Verlag: Springer International Publishing
Buch: Optimization, Learning Algorithms and Applications
Print ISBN: 978-3-030-91884-2

Electronic ISBN: 978-3-030-91885-9

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-91885-9_3

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