Multi-stage differential evolution algorithm for constrained D-optimal design

In practice, objective condition may impose constraints on design region, which make it difficult to find the exact D-optimal design. In this paper, we propose a Multi-stage Differential Evolution (MDE) algorithm to find the global approximated D-optimal design in an experimental region with linear...

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Bibliographic Details
Published inAIMS mathematics Vol. 6; no. 3; pp. 2956 - 2969
Main Authors Zhang, Xinfeng, Zhu, Zhibin, Zhang, Chongqi
Format Journal Article
LanguageEnglish
Published AIMS Press 01.01.2021
Subjects
constrained experimental region
d-optimal design
experimental design
multi-stage differential evolution algorithm
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ISSN2473-6988
2473-6988
DOI10.3934/math.2021179

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Summary:In practice, objective condition may impose constraints on design region, which make it difficult to find the exact D-optimal design. In this paper, we propose a Multi-stage Differential Evolution (MDE) algorithm to find the global approximated D-optimal design in an experimental region with linear or nonlinear constraints. MDE algorithm is approved from Differential Evolution (DE) algorithm. It has low requirements for both feasible regions and initial values. In iteration, MDE algorithm pursues evolutionary equilibrium rather than convergence speed, so it can stably converge to the global D-optimal design instead of the local ones. The advantages of MDE algorithm in finding D-optimal design will be illustrated by examples.
ISSN:2473-6988
2473-6988
DOI:10.3934/math.2021179