Numerical studies on venting system with multi-chamber perforated mufflers by GA optimization

Research on new techniques of perforated silencers has been well addressed and developed; however, the research work in shape optimization for a volume-constrained silence requested upon the demands of operation and maintenance inside a constrained machine room is rare. Therefore, the main purpose of this paper is to not only analyze the sound transmission loss of a multi-chamber perforated muffler but also to optimize the best design shape under space-constrained condition.In this paper, both the generalized decoupling technique and plane wave theory are used. The four-pole system matrix in evaluating the acoustic performance of sound transmission loss (STL) is also deduced in conjunction with a genetic algorithm (GA). To demonstrate the precision of the tuning ability in a muffler, various targeted pure tones are proposed in numerical cases. Results reveal that the maximal acoustical performance precisely occurred in the desired frequency. Furthermore, a noise reduction with respect to full-band exhausted noise emitted from a diesel engine is also introduced and assessed. To achieve a better optimization in GA, several test parameter values were used. Before a GA operation can be carried out, the accuracy of the mathematical models have to be checked by experimental data.The optimal result in eliminating full-band noise reveals that the overall noise reduction of a multi-chamber muffler can achieve 68 dB under space-constraint conditions. Consequently, the approach used for the optimal design of the STL proposed in this study is indeed easy, economical and quite effective.     

Multiobjective muffler shape optimization with hybrid acoustics modeling

This paper considers the combined use of a hybrid numerical method for the modeling of acoustic mufflers and a genetic algorithm for multiobjective optimization. The hybrid numerical method provides accurate modeling of sound propagation in uniform waveguides with non-uniform obstructions. It is based on coupling a wave based modal solution in the uniform sections of the waveguide to a finite element solution in the non-uniform component. Finite element method provides flexible modeling of complicated geometries, varying material parameters, and boundary conditions, while the wave based solution leads to accurate treatment of non-reflecting boundaries and straightforward computation of the transmission loss (TL) of the muffler. The goal of optimization is to maximize TL at multiple frequency ranges simultaneously by adjusting chosen shape parameters of the muffler. This task is formulated as a multiobjective optimization problem with the objectives depending on the solution of the simulation model. NSGA-II genetic algorithm is used for solving the multiobjective optimization problem. Genetic algorithms can be easily combined with different simulation methods, and they are not sensitive to the smoothness properties of the objective functions. Numerical experiments demonstrate the accuracy and feasibility of the model-based optimization method in muffler design.     

Shape optimization of multi-chamber cross-flow mufflers by SA optimization

It is essential when searching for an efficient acoustical mechanism to have an optimally shaped muffler designed specially for the constrained space found in today's plants. Because the research work of optimally shaped straight silencers in conjunction with multi-chamber cross-flow perforated ducts is rarely addressed, this paper will not only analyze the sound transmission loss (STL) of three kinds of cross-flow perforated mufflers but also will analyze the optimal design shape within a limited space.In this paper, the four-pole system matrix used in evaluating acoustic performance is derived by using the decoupled numerical method. Moreover, a simulated annealing (SA) algorithm, a robust scheme in searching for the global optimum by imitating the softening process of metal, has been adopted during shape optimization. To reassure SA's correctness, the STL's maximization of three kinds of muffles with respect to one-tone and dual-tone noise is exemplified. Furthermore, the optimization of mufflers with respect to an octave-band fan noise by the simulated algorithm has been introduced and fully discussed. Before the SA operation can be carried out, an accuracy check of the mathematical model with respect to cross-flow perforated mufflers has to be performed by Munjal's analytical data and experimental data.The optimal result in eliminating broadband noise reveals that the cross-flow perforated muffler with more chambers is far superior at noise reduction than a muffler with fewer chambers. Consequently, the approach used for the optimal design of noise elimination proposed in this study is certainly easy and efficient.     

抛物型方程的演化参数识别方法

给出了一种利用演化计算方法求解微分方程中的参数识别类型反问题的方法。该方法把参数识别问题转化为泛函的优化问题用演化算法来求解,指定待定参数的函数类形式,用遗传算法(Genetic Algorithms)来演化待求参数的最优估计值,并将该方法运用于线性扩散方程和拟线性对流扩散反方程反问题的数值模拟中。     

High-efficiency design of combustion kinetic experiments based on genetic algorithm

Experimental data is essential for the improvement of combustion kinetic models. Experimental design based on model analysis results can screen optimal experimental conditions with maximum information content. However, the computational cost of designing experiments by enumeration becomes unaffordable when an enormity of conditions with different temperatures/pressures/mixtures are to be investigated. An approach to facilitate the efficient discovery of optimal experimental conditions based on the genetic algorithm (GA) is proposed in this work. This approach regards the task of experimental design as an optimization problem to minimize an objective function that measures the information content provided by an experiment. The sensitivity entropy and surrogate model similarity are combined to form the objective function of optimization. Three designs of dimethyl ether experiments are provided to demonstrate the approach. The first case utilizes a benchmark for optimal experiments to validate the effectiveness of GA. The results show that GA can achieve better design results than the traditional enumeration strategy with less than 10% computational cost. The second case illustrates how GA is applied in the design of multiple experiments. The last one is an application in designing multiple experiments of various types, including ignition, species measurements in a jet-stirred reactor (JSR) and a plug flow reactor (PFR). The model parameters are calibrated with the designed experimental data using a Bayesian-based optimization approach. The uncertainties of model parameters are significantly reduced after the optimization.     

用遗传算法确定材料物态方程参数

 根据实测应力历史曲线建立非线性优化模型，以确定材料物态方程参数。将全局优化性能良好的遗传算法应用于优化过程。针对2号纯铁的一维平面碰撞实验记录，用这种方法计算了铁的物态方程参数C₀和s，并将结果同用BFGS算法得到的结果进行了比较。     

Identification of noise sources in factory’s sound field by using genetic algorithm

Noise control is important and essential in factory, where the noise level is restricted by the Occupational Safety and Health Act. Before noise abatement being performed, the identification work in searching for the location and sound power level (SWL) of noisy sound sources is absolutely prerequisite. Several researches on new techniques of single noise control have been well addressed and developed; however, the research work on sound identification for the existing multi-noise plant is hardly found sufficient. Under the circumstance of unrecognized noises, the noise control work will expectedly be extravagant and fruitless. Therefore, the numerical approach in distinguishing noises from a multi-noise plant becomes crucial and obligatory.In this paper, the novel technique of genetic algorithm (GA) in conjunction with the method of minimized variation square will be adopted and used in the following numerical optimization. In addition, various sound monitoring systems in detecting the noise condition within the plant area will also be introduced. Before noises identification, the accuracy of mathematical model has then been proved to be in good agreements comparing to the simulated data of SoundPlan, a commercialized simulation package in sound field. Moreover, three kinds of multi-noise plants have been fully discussed and acknowledged by GA optimization. The results reveal that the relevant locations and sound power levels (SWLs) of noises can be precisely recognized. This paper surely provides a rapid methodology in the noise identification work for a multi-noise plant.     

Support Vector Driven Genetic Algorithm for the Design of Circular Polarized Microstrip Antenna

In this paper, a hybrid soft computing method for designing specific microstrip antenna is presented. Evolutionary algorithm such as genetic algorithm (GA) is one of the promising ways of finding global optimum solution from a multivariate nonlinear feature space. Being a stochastic iterative algorithm, it requires much computation power when the function to be optimized is complex and time consuming. Various meta-modelling techniques such as neural network, response surface methods, kriging, etc. can be used to model the process under optimization in order to reduce the computational expenses. In this paper, we investigate one such technique – support vector regression (SVR) – to model the complex analytical process. The model, thus obtained, is used for optimization using genetic algorithms. This approach is demonstrated for the design of circular polarized microstrip antenna at 2.6 GHz band. The results of SVR model are compared with other meta-models generated with neural network and response surface methodology.     

Designing a genetic watermarking approach for 3D scenes

A new watermarking algorithm based on genetic algorithm (GA) in the transform domain is proposed. Unlike the existing computer-generated integral imaging based watermarking methods, the proposed method utilizes GA searching to the optimized transform domain to serve as a trade-off for watermark embedding. In this paper, 3D scene to be captured by using a virtual pinhole array and be computationally recorded as an elemental image array (EIA), watermarking with GA optimization and computer-generated holography is implemented. In the proposed GA optimization process, we utilize the fitness function to improve the visual quality of watermarked images and the robustness. Simulation results show that the proposed algorithm yields a holographic watermark that is imperceptibility to human eyes and robust to standard watermarking attacks. A comparison of the proposed watermarking method to the existing similar watermarking methods demonstrated that the proposed method generally outperforms completing methods in terms of imperceptibility and robustness.     

基于多算法优化SVM的粗糙面参数反演

支持向量机（SVM）是粗糙面参数反演中常用的一种反演算法,SVM反演中的惩罚参数C和核函数参数G对反演结果精度的影响较大,若参数取值不当,会使模型产生"过学习"或者"欠学习"的现象,从而降低预测精度.给出几种SVM参数C和参数G的优化算法,如K折交叉验证（K-CV）、遗传算法（GA）和粒子群算法（PSO）,并在此基础上提出一种基于K-CV和GA改进的PSO算法（GA-CV-PSO）.利用矩量法（MoM）获得的粗糙面后向散射系数构造训练集和测试集,通过不同参数反演的仿真结果对比不同优化算法的反演精度和计算时间,表明GA-CV-PSO算法克服了单一优化算法的缺陷,具有更精确的反演精度和更强的泛化能力.     

Intelligent reversible watermarking technique in medical images using GA and PSO

The advances in recording, editing, and broadcasting multimedia contents in digital form motivate to protect these digital contents from illegal use, such as duplication, manipulation, and redistribution. However, watermarking algorithms are designed to satisfy requirements of applications, as different applications have different concerns. We intend to design a watermarking algorithm for applications which require high embedding capacity and imperceptibility, to maintain the integrity of the host signal as well as embedded information. Reversible watermarking is a promising technique which satisfies our requirements. In this paper, we concentrate on improving the watermark capacity and reducing the perceptual degradation of an image. We investigated the Luo's [1] additive interpolation-error expansion algorithm and enhanced it by incorporating with two intelligent techniques: genetic algorithm (GA), and particle swarm optimization (PSO). Genetic algorithm is applied to exploit the correlation of image pixel values to obtain better estimation of neighboring pixel values, which results in optimal balance between information storage capacity and imperceptibility. Particle swarm optimization (intelligent technique) is also applied for the same purpose. Experimental results show that PSO and GA nearly give the same results, but GA outperforms the PSO. Experimental results also reveal that the proposed strategy outperforms the state of art works in terms of perceptual quality and watermarking payload.     

群体智能优化中的虚拟碰撞：雨林算法

启发式优化算法中寻优代理过早收敛易陷入局部最优. 本文对此进行机理分析并发现, 虚拟碰撞作为一种隐性过早收敛现象将直接影响群体智能优化算法的准确性与快速性, 而采样过程的无约束性和样本分布信息的缺失是导致虚拟碰撞的根本原因. 为解决上述问题, 本文提出雨林优化算法. 该算法仿照植物生长模式, 利用规模可变种群代替规模限定种群进行分区分级寻优采样, 并结合均匀与非均匀采样原则来权衡优化算法的探索与挖掘, 可以有效减少虚拟碰撞的发生, 在提高寻优效率的同时, 获取精准性和稳定性较高的全局最优解. 与遗传算法、粒子群算法对标称函数的寻优对比实验表明, 雨林算法在快速性、准确性以及泛化能力等方面均具有优势. 关键词： 优化算法 群体智能 进化计算 计算智能     

Single-parent evolution algorithm and the optimization of Si clusters

We describe a novel method for the structural optimization of molecular systems. Similar to genetic algorithms (GA), our approach involves an evolving population in which new members are formed by cutting and pasting operations on existing members. Unlike previous GA's, however, the population in each generation has a single parent only. This scheme has been used to optimize Si clusters with 13-23 atoms. We have found a number of new isomers that are lower in energy than any previously reported and have properties in much better agreement with experimental data.     

Optimization of semiconductor quantum devices by evolutionary search

A novel simulation strategy is proposed for searching for semiconductor quantum devices that are optimized with respect to required performances. Based on evolutionary programming, a technique that implements the paradigm of genetic algorithms in more-complex data structures than strings of bits, the proposed algorithm is able to deal with quantum devices with preset nontrivial constraints (e.g., transition energies, geometric requirements). Therefore our approach allows for automatic design, thus avoiding costly by-hand optimizations. We demonstrate the advantages of the proposed algorithm through a relevant and nontrivial application, the optimization of a second-harmonic-generation device working in resonance conditions.     

The improved genetic algorithms for digital image correlation method

We present a global optimization method, called the genetic algorithms (GAs), for digital image/speckle correlation (DISC). The new algorithms do not involve reasonable initial guess of displacement and deformation gradient and the calculation of second-order spatial derivatives of the digital images, which are important challenges in practical implementation of DISC. The performance of a GA depends largely on the selection of the genetic operators. We test various operators and propose optimal operators. The algorithms are then verified using simulated images and experimental speckle images.     

Genetic algorithms for the QoS based multicast routing and wavelength allocation problem in WDM network

To tackle the QoS based multicast routing and wavelength allocation problem (MRWA), three multi-objective genetic algorithms are proposed, which are based on the ideas of Non-dominated Sorting, Strength Pareto and Decomposition, respectively. The chromosome coding scheme, crossover and mutation operators are redefined. To ensure the generated offspring being a connected light-tree, a light-path repair process and a loop eliminating process are designed. The proposed algorithms were evaluated on a set of different scale test problems and compared with the recently proposed GA based multi-objective optimization algorithm for this problem. The experimental results reveal very encouraging results in terms of the solution quality.     

弱光探测器中复合抛物面聚光镜

基于非成像光学的原理,设计并研制了一种用于弱光探测器的复合抛物面聚光镜.提出基于遗传算法的优化设计聚光镜电子光学系统的新方法,将探测器阳极对光电子的接收率作为遗传进化的目标函数,把复合抛物面聚光镜系统的几何结构参量和电参量作为优化设计的搜索变量,通过控制遗传算法的遗传进化方向,进行全局优化调整.仿真实验结果表明,优化后的聚光镜在会聚光线的同时可以对光电子实现有效的会聚.当入射角为30°时,光透过率高于75%,电子收集率为100%,可以满足具有反射式光电阴极和大探测面积的弱光探测器要求.     

多传感器目标识别系统的特征优化方法

来自多传感器的目标特征往往是高维数的,并且包含了更多的冗余信息和噪声。为了减小数据获取的代价,提高目标识别器的性能和效率,提出了基于遗传算法(GA)的多传感器目标识别系统特征优化方法。将遗传算法与神经网络目标分类器结合,通过识别结果的反馈信息,控制GA的遗传进化方向,从而实现特征优化。为了克服遗传算法的未成熟收敛问题,提出了相关选择与自适应遗传算子相结合的改进遗传算法。仿真实验结果验证了方法的有效性。     

基因算法在光阴极注入器优化设计中的应用

基于正在建设的大连自由电子激光装置,研究了基因算法在注入器优化设计中的系统应用。与传统的注入器优化方法相比较,基因算法在多变量、多目标注入器优化问题中具有优化效率高、优化结果精确的优势;通过采用权重因子的方法将多目标问题转化为单目标优化问题,大大简化了基因算法优化流程;光阴极注入器对束流稳定性要求严格,通过基因算法优化使束流的到达时间抖动控制到150fs,满足束流稳定性的要求。     

改进遗传算法在红外增透膜系设计中的应用

在3～5μm红外增透膜系的遗传算法优化设计中，当群体接近成熟时，个体间的竞争能力和算法的区别选优能力会下降，进化将停止不前，产生无意义的重复计算和长时间等待。为了监控进化过程中染色体的变化趋势和改进情况，并提高算法的搜索能力，提出了一种采用离线比较法和辅助优选因子的薄膜改进遗传算法。计算实验表明，离线比较监控法能有效地掌握计算的进展情况，准确地监察到群体的成熟收敛、进化停止，并可据此决定改进算法的时机。采取辅助优选因子，能保持先前的计算成果，选出当前群体中的优良个体，跟引入的新鲜个体一同参与交叉，使进化不断向前，从而使改进后的算法搜到合格解的几率大大提高。