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.
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Acknowledgments
The research reported in this paper was conducted as part of the “Developing a Real Time Abstraction & Discharge Permitting Process for Catchment Regulation and Optimised Water Management” project funded by EPSRC (Engineering Physical Sciences Research Council) and TSB (Technology Strategy Board) in the UK as part of the Water Security managed programme (TSB/EPSRC grant reference number: TS/K002805/1). This financial support is gratefully acknowledged.
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Liu, Y., Kwan, A., Rezgui, Y., Li, H. (2016). A Novel Fast Optimisation Algorithm Using Differential Evolution Algorithm Optimisation and Meta-Modelling Approach. In: Yang, XS. (eds) Nature-Inspired Computation in Engineering. Studies in Computational Intelligence, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-319-30235-5_9
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DOI: https://doi.org/10.1007/978-3-319-30235-5_9
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