Depending on different design parameters and limitations, optimization of sound absorbers has always been a challenge in the field of acoustic engineering. Various methods of optimization have evolved in the past deca...
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Depending on different design parameters and limitations, optimization of sound absorbers has always been a challenge in the field of acoustic engineering. Various methods of optimization have evolved in the past decades with innovative method of evolutionstrategy gaining more attention in the recent years. Based on their simplicity and straightforward mathematical representations, single-layer absorbers have been widely used in both engineering and industrial applications and an optimized design for these absorbers has become vital. In the present study, the method of evolution strategy algorithm is used for optimization of a single-layer absorber at both a particular frequency and an arbitrary frequency band. Results of the optimization have been compared against different methods of genetic algorithm and penalty functions which are proved to be favorable in both effectiveness and accuracy. Finally, a single-layer absorber is optimized in a desired range of frequencies that is the main goal of an industrial and engineering optimization process.
Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally *** paper examines different evolutionary algorithms for tr...
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Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally *** paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network(SRBFNN)through the behavior’s integration of satisfiability *** by evolutionary algorithms,which can iteratively find the nearoptimal solution,different evolutionary algorithms(EAs)were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation(SRBFNN-2SAT).The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms,including Genetic algorithm(GA),evolution strategy algorithm(ES),Differential evolutionalgorithm(DE),and evolutionary Programming algorithm(EP).Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming *** the use of SRBFNN-2SAT,a training method based on these algorithms has been presented,then training has been compared among algorithms,which were applied in Microsoft Visual C++software using multiple metrics of performance,including Mean Absolute Relative Error(MARE),Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),Mean Bias Error(MBE),Systematic Error(SD),Schwarz Bayesian Criterion(SBC),and Central Process Unit time(CPU time).Based on the results,the EP algorithm achieved a higher training rate and simple structure compared with the rest of the *** has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight,accompanied by the slightest iteration error,which minimizes the objective function of SRBFNN-2SAT.
The purpose of this work is to evaluate the performance of an optimization algorithm from the field of evolutionary computation, namely an evolutionstrategy, in back analysis of geomechanical parameters in undergroun...
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The purpose of this work is to evaluate the performance of an optimization algorithm from the field of evolutionary computation, namely an evolutionstrategy, in back analysis of geomechanical parameters in underground structures. This analysis was carried out through a parametric study of a synthetic case of a tunnel construction. Different combinations of parameters and measurements were carried out to test the performance of the algorithm. In order to have a comparison base for its performance also three classical optimization algorithms based on the gradient of the error function and a Genetic algorithm were used. It was concluded that the evolution strategy algorithm presents interesting capabilities in terms of robustness and efficiency allowing the mitigation of some of the limitations of the classical algorithms. Moreover a back analysis study of geomechanical parameters using real monitoring data and a 3D numerical model of a hydraulic underground structure being built in the North of Portugal was performed using the evolution strategy algorithm, in order to reduce the uncertainties about the parameters evaluated by in situ and laboratory tests. It was verified that the low quantity of monitoring data available hinders the possibility to identify the parameters of interest. The existence of information of only one additional extensometer perpendicular to the existing one would allow this identification to succeed. (C) 2012 Elsevier Ltd. All rights reserved.
As an important part of the vessel stowage planning. the Master Bay Plan Problem (MBPP) can effectively reduce the container's reverse operation and ship stay time, improve the transportation efficiency of contain...
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ISBN:
(纸本)9781728101057
As an important part of the vessel stowage planning. the Master Bay Plan Problem (MBPP) can effectively reduce the container's reverse operation and ship stay time, improve the transportation efficiency of container ships and the safety of the entire route if it rationally formulated. The essay established a mathematical model of container loading sequence with the target of minimizing the number of ship containers. The evolution strategy algorithm (ES) is used to optimize the container MBPP, and the two-dimensional real number coding method is designed. The two-point crossover interchange recombination and mutation method is used to analyze ES through different parent and child scales and multiple scale examples. Finally, the ES is compared with the commonly used optimization algorithm PSO and the heuristic algorithm based on the actual shipping problem. The results show that ES can find the optimal solution more quickly, and the optimization efficiency is higher than PSO and the heuristic algorithm of the actual ship loading problem. This consequence verified the validity of the model and algorithm.
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