A novel fast optimisation algorithm using differential evolution algorithm optimisation and meta-modelling approach

Liu, Yang, Kwan, Alan Shu Ken ORCID: https://orcid.org/0000-0003-4206-4435, Rezgui, Yacine ORCID: https://orcid.org/0000-0002-5711-8400 and Li, Haijiang ORCID: https://orcid.org/0000-0001-6326-8133 2016. A novel fast optimisation algorithm using differential evolution algorithm optimisation and meta-modelling approach. Yang, Xin-She, ed. Nature-Inspired Computation in Engineering, Vol. 637. Studies in Computational Intelligence, Cham: Springer, pp. 177-193. (10.1007/978-3-319-30235-5_9)

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Abstract

Genetic algorithms (GAs), Particle Swarm Optimisation (PSO) and Differential Evolution (DE) have proven to be successful in engineering model calibration problems. In real-world model calibration problems, each model evaluation usually requires a large amount of computation time. The optimisation process usually needs to run the numerical model and evaluate the objective function thousands of times before converging to global optima. In this study, a computational framework, known as DE-RF, is presented for solving computationally expensive calibration problems. We have proposed a dynamic meta-modelling approach, in which Random Forest (RF) regression model was embedded into a differential evolution optimisation framework to replace time consuming functions or models. We describe the performance of DE and DE-RF when applied to a hard optimisation function and a rainfall-runoff model calibration problem. The simulation results suggest that the proposed optimisation framework is able to achieve good solutions as well as provide considerable savings of the function calls with a very small number of actual evaluations, compared to these traditional optimisation algorithms.

Item Type:	Book Section
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Publisher:	Springer
ISBN:	978-3-319-30233-1
ISSN:	1860-949X
Last Modified:	01 Nov 2022 09:45
URI:	https://orca.cardiff.ac.uk/id/eprint/89136

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