Automatic Calibration of Numerical Models using Fast Optimisation by Fitness Approximation

Liu, Y.; Khu, Soon‐Thiam

doi:10.1109/ijcnn.2007.4371107

Cited by 13 publications

(5 citation statements)

References 3 publications

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“…Additionally, a surrogate modeling approach [59] is applied in the GA tool to reduce the number of fitness/function evaluations during model implementation, decreasing the computational cost [60]. The surrogate approach uses a k-nearest neighbors (KNN) classifier [61] to approximate the fitness scores of some individual chromosomes based on their distance to the remaining chromosomes whose fitness scores are exactly evaluated by executing the fitness function.…”

Section: Calibration Of the Ca Parameters Using A Genetic Algorithmmentioning

confidence: 99%

Delineation of Urban Growth Boundaries Using a Patch-Based Cellular Automata Model under Multiple Spatial and Socio-Economic Scenarios

et al. 2019

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The urban growth boundary (UGB) plays an important role in the regulation of urban sprawl and the conservation of natural ecosystems. The delineation of UGBs is a common strategy in urban planning, especially in metropolitan areas undergoing fast expansion. However, reliable tools for the delineation of informed UGBs are still not widely available for planners. In this study, a patch-based cellular automaton (CA) model was applied to build UGBs, in which urban expansions were represented as organic and spontaneous patch growing processes. The proposed CA model enables the modeler to build various spatial and socio-economic scenarios for UGB delineation. Parameters that control the patch size and shape, along with the spatial compactness of an urban growth pattern, were optimized using a genetic algorithm. A random forest model was employed to estimate the probability of urban development. Six scenarios in terms of the demand and the spatial pattern of urban land allocation were constructed to generate UGB alternatives based on the simulated urban land maps from the CA model. Application of the proposed model in Ezhou, China from 2004 to 2030 reveals that the model proposed in this study can help urban planners make informed decisions on the delineation of UGBs under different scenarios.

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Section: Calibration Of the Ca Parameters Using A Genetic Algorithmmentioning

confidence: 99%

Delineation of Urban Growth Boundaries Using a Patch-Based Cellular Automata Model under Multiple Spatial and Socio-Economic Scenarios

et al. 2019

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“…However, the evolutionary (genetic) algorithms are efficient enough to perform a robust solution search [15] in a complex parameter space with a lack of historical data for quality assessment [21]. The applicability of evolutionary algorithms for SWAN wave model calibration is demonstrated in [13].…”

Section: Related Workmentioning

confidence: 99%

Robust Ensemble-Based Evolutionary Calibration of the Numerical Wind Wave Model

Vychuzhanin¹,

Nikitin²,

Kalyuzhnaya³

2019

Lecture Notes in Computer Science

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The adaptation of numerical wind wave models to the local time-spatial conditions is a problem that can be solved by using various calibration techniques. However, the obtained sets of physical parameters become over-tuned to specific events if there is a lack of observations. In this paper, we propose a robust evolutionary calibration approach that allows to build the stochastic ensemble of perturbed models and use it to achieve the trade-off between quality and robustness of the target model. The implemented robust ensemble-based evolutionary calibration (REBEC) approach was compared to the baseline SPEA2 algorithm in a set of experiments with the SWAN wind wave model configuration for the Kara Sea domain. Provided metrics for the set of scenarios confirm the effectiveness of the REBEC approach for the majority of calibration scenarios.

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“…The particle swarm optimisation and differential evolution are two efficient stochastic optimisation methods minimizing an objective function that can model the problem's objectives while incorporating constraints, and have three main advantages: global search regardless of the initial parameter values, fast convergence and a few control parameters. Both techniques have shown great promise in several real-world applications (Deb, 2001;Liu & Khu, 2007;Liu, 2009;Liu & Sun, 2011;Liu and Pender, 2012;Liu and Pender, 2013). Facts have proved that population based optimisations like GA, PSO and DE are suitable to handle complicated constrained optimisation problems (Coello Coello, 2002;Wang and Cai, 2012).…”

Section: Introductionmentioning

confidence: 99%

A novel battery network modelling using constraint differential evolution algorithm optimisation

Liu¹,

Rowe

Holderbaum

et al. 2016

Knowledge-Based Systems

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The amount of battery storage into the power system network has been increasing in the recent years. The use of battery storage devices has been advocated as one of the main ways of improving the power quality and reliability of the power system, including minimization of energy imbalance and reduction of peak demand. Higher peaks in demand will increase the electricity price and could cause blackouts and infrastructure damage. Lowering peak demand to reduce the use of carbon-intensive fuels and the number of expensive peaking plant generators is thus of major importance. Self-adaptive control methods for individual batteries have been developed to reduce the peak demand. However, these self-adaptive control algorithms of are not very efficient without sharing the energy among different batteries. This paper proposes a novel battery network system with optimal management of energy between batteries. An optimal management strategy has been implemented using a population-based constraint differential evolution algorithm. Taking advantage of this strategy the battery network model can remove more peak areas of forecasted demand data compared to the self-adaptive control algorithm developed for the New York City study case.

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Automatic Calibration of Numerical Models using Fast Optimisation by Fitness Approximation

Cited by 13 publications

References 3 publications

Delineation of Urban Growth Boundaries Using a Patch-Based Cellular Automata Model under Multiple Spatial and Socio-Economic Scenarios

Delineation of Urban Growth Boundaries Using a Patch-Based Cellular Automata Model under Multiple Spatial and Socio-Economic Scenarios

Robust Ensemble-Based Evolutionary Calibration of the Numerical Wind Wave Model

A novel battery network modelling using constraint differential evolution algorithm optimisation

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