Genetic algorithms for inverse cathodic protection problems

Wrobel, L.C.; Miltiadou, Panayiotis

doi:10.1016/s0955-7997(03)00057-2

Cited by 20 publications

(10 citation statements)

References 13 publications

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“…It is recognized to be highly efficient in dealing with large, discrete, non-linear and poorly understood optimization problems [18,19]. This method does not guarantee the exact identification of the optimum solution of a problem.…”

Section: Genetic Algorithm Methodsmentioning

confidence: 99%

Soil parameter identification using a genetic algorithm

Levasseur

Malécot

Boulon

et al. 2007

Num Anal Meth Geomechanics

157

111

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SUMMARYThis paper is dedicated to the identification of constitutive parameters of the Mohr-Coulomb constitutive model from in situ geotechnical measurements. A pressuremeter curve and the horizontal displacements of a sheet pile wall retaining an excavation are successively used as measurements. Two kinds of optimization algorithms are used to minimize the error function, the first one based on a gradient method and the second one based on a genetic algorithm. The efficiency of each algorithm related to the error function topology is discussed. Finally, it is shown that the use of a genetic algorithm to identify the soil parameters seems particularly suitable when the topology of the error function is complex.

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Section: Genetic Algorithm Methodsmentioning

confidence: 99%

Soil parameter identification using a genetic algorithm

Levasseur

Malécot

Boulon

et al. 2007

Num Anal Meth Geomechanics

157

111

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“…4 To handle the constraints, the genetic algorithm is combined with the Genocop III technique (Genetic Algorithm for Numerical Optimization of Constrained Problems) [18]. This procedure maintains two 4 A good choice of the crossover probability is said to be in between 0.4 and 0.9 [14,54], 0.6 and 0.8 [27], 0.6 and 0.95 [20,22], and 0.6 and 0.8 [51]. A good choice of the mutation probability is said to be in between 0.001 and 0.1 [14,20,22], 0.005 and 0.05 [51], and 0.05 and 0.1 [54].…”

Section: Well Placement Using Gamentioning

confidence: 99%

Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models

2011

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The amount of hydrocarbon recovered can be considerably increased by finding optimal placement of non-conventional wells. For that purpose, the use of optimization algorithms, where the objective function is evaluated using a reservoir simulator, is needed. Furthermore, for complex reservoir geologies with high heterogeneities, the optimization problem requires algorithms able to cope with the non-regularity of the objective function. In this paper, we propose an optimization methodology for determining optimal well locations and trajectories based on the covariance matrix adaptation evolution strategy (CMA-ES) which is recognized as one of the most powerful derivativefree optimizers for continuous optimization. In addition, to improve the optimization procedure, two new techniques are proposed: (a) adaptive penalization with rejection in order to handle well placement constraints and (b) incorporation of a meta-model, based on locally weighted regression, into CMA-ES, using an approximate stochastic ranking procedure, in order to reduce the number of reservoir simulations required to evaluate the objective function. The approach is applied to the PUNQ-S3 case and compared with a genetic algorithm (GA) incorporating the Genocop III technique for handling constraints. To allow a fair comparison, both algorithms are used without parameter tuning on the problem, and standard settings are used for the GA and default settings for CMA-ES. It is shown that our new approach outperforms the genetic algorithm: It leads in general to both a higher net present value and a significant reduction in the number of reservoir simulations needed to reach a good well configuration. Moreover, coupling CMA-ES with a meta-model leads to further improvement, which was around 20% for the synthetic case in this study.

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“…3 They have been extensively used as optimization techniques 4 in a number of engineering processes such as steelmaking, polymer production, design of pharmaceutical formulations, hydrocyclone design and performance, preparation of concrete mixtures, and cathodic protection. [5][6][7][8][9][10] The genetic algorithm method used in this work takes into consideration the raw elemental analysis of the concentrates and computes the corresponding mineralogical composition of the feed. Such a composition is further used in the mass balance equations to predict the amounts and compositions of matte, slag, and the off-gas stream.…”

Section: Introductionmentioning

confidence: 99%

Mass balance calculations in copper flash smelting by means of genetic algorithms

Sánchez-Corrales

Valera-Gonzalez

Pérez-Tello

et al. 2004

JOM

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This paper presents mass balance calculations using genetic algorithms for copper smelting in an Outokumpu fl ash furnace. Based on the elemental composition of the copper concentrates being fed to the reactor, the mineralogical composition of the concentrate mixture is adjusted by means of genetic algorithms. The macroscopic mass balance equations for the species entering and leaving the furnace are solved and the compositions and fl ow rates of matte, slag, and the off-gas stream are computed. Good agreement between the predicted and plant data was obtained in terms of matte and slag fl ow rates, matte grade, and copper, iron, magnetite, and silica contents in the slag. Predictions are more suitable and faster to obtain with this method than a conventional method in which the mineralogical composition of the feed is not adjusted. Future applications of the formulation are discussed.

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Genetic algorithms for inverse cathodic protection problems

Cited by 20 publications

References 13 publications

Soil parameter identification using a genetic algorithm

Soil parameter identification using a genetic algorithm

Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models

Mass balance calculations in copper flash smelting by means of genetic algorithms

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