Particle pairing using genetic algorithms for PIV

This paper presents a new particle pairing algorithm using “Genetic Algorithms” for DPIV (Digital Particle Image Velocimetry), which are searching algorithms for obtaining an optimal solution based on the mechanism of evolution. The particle pairing between two tracer images with a constant time interval is needed to obtain a velocity vector field. Since the algorithm adopts a fitness function which totally evaluates the similarity between respective small particle patterns in the two tracer images over the field, it promises to give a more correct velocity vector distribution than the conventional PTV (Particle Tracking Velocimetry) which identifies each particle based on its local information. In addition, a particle pattern matching for the similarity is performed after correcting fluid rotation. It therefore is robust against a high particle density and an increase in the time interval. The algorithm is applied to the PIV standard images distributed through the Internet (http://www.vsj.or.jp/piv). It gives a correct velocity vector distribution as a result even if a pair of the successive images has a large time interval.     

Influence of rounding errors on the quality of heuristic optimization algorithms

Search space smoothing and related heuristic optimization algorithms provide an alternative approach to simulated annealing and its variants: while simulated annealing traverses barriers in the energy landscape at finite temperatures, search space smoothing intends to remove these barriers, so that a greedy algorithm is sufficient to find the global minimum. Several formulas for smoothing the energy landscape have already been applied, one of them making use of the finite numerical precision on a computer. In this paper, we thoroughly investigate the effect of finite numerical accuracy on the quality of results achieved with heuristic optimization algorithms. We present computational results for the traveling salesman problem.     

用遗传算法构造光正交码

提出了一种利用遗传算法构造任意码长、码重、自相关限、互相关限光正交码(GA-OOC)的方法,设计了新的码字矩阵;分析了基于GA-OOC的光CDMA系统的误码率性能。仿真结果表明:该算法具有构造算法简单、易于编程实现的特点;与基于PC,EPC,QC和BIBD-OOC的光码分多址系统相比,GA-OOC的光码分多址系统具有更好的误码率性能。GA-OOC有望用于构造二维光地址码。     

Fringes demodulation in time-averaged digital shearography using genetic algorithms

This paper presents a phase recovery method, based on genetic algorithms, in time-average shearography. It is proved that a single Bessel fringe pattern obtained under a subtraction operation could be enough to calculate the phase. A merit function is solved iteratively using genetic operator like selection, reproduction and mutation. Experimental results are presented in this paper using a simple shearing system based on a Fresnel biprism.     

Optimal brushless DC motor design using genetic algorithms

This paper presents a method for the optimal design of a slotless permanent magnet brushless DC (BLDC) motor with surface mounted magnets using a genetic algorithm. Characteristics of the motor are expressed as functions of motor geometries. The objective function is a combination of losses, volume and cost to be minimized simultaneously. Electrical and mechanical requirements (i.e. voltage, torque and speed) and other limitations (e.g. upper and lower limits of the motor geometries) are cast into constraints of the optimization problem. One sample case is used to illustrate the design and optimization technique.     

Three dimensional k-space trajectory design using genetic algorithms

Image quality and total scan time in MRI are determined in large part by the trajectory employed to sample the Fourier space. Each trajectory has different properties like coverage of k-space, scan time, sensitivity to off-resonance conditions, etc. These properties are often contradictory, therefore a universal optimal trajectory does not exist and ultimately, it will depend on the image characteristics sought. Most trajectories used today are designed based on intuition and k-space analysis more than with optimization methods. This work presents a 3D k-space trajectory design method based on Genetic Algorithm optimization. Genetic Algorithms have been chosen because they are particularly good for searching large solution spaces. They emulate the natural evolutionary process allowing better offsprings to survive. The objective function searches the maximum of the trajectory's k-space coverage subject to hardware constraints for a fixed scanning time using the trajectory's torsion as its optimization variable.The method proved to be effective for generating k-space trajectories. They are compared with well-established trajectories. The results of simulated experiments show that they can be appropriate for image acquisition under certain special conditions, like off-resonance and undersampling. This design method can be extended to include other objective functions for different behaviors.     

Nonparametric algorithms for vector optimization

Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Fizika, No. 9, pp. 5–10, September, 1995.     

Rules extraction in short memory time series using genetic algorithms

Data mining is performed using genetic algorithm on artificially generated time series data with short memory. The extraction of rules from a training set and the subsequent testing of these rules provide a basis for the predictions on the test set. The artificial time series are generated using the inverse whitening transformation, and the correlation function has an exponential form with given time constant indicative of short memory. A vector quantization technique is employed to classify the daily rate of return of this artificial time series into four categories. A simple genetic algorithm based on a fixed format of rules is introduced to do the forecasting. Comparing to the benchmark tests with random walk and random guess, genetic algorithms yield substantially better prediction rates, between 50% to 60%. This is an improvement compared with the 47% for random walk prediction and 25% for random guessing method. Received 29 August 2000     

Optimization of time-domain header processing filters using genetic algorithms

We present genetic algorithms as optimization tools for the design of header recognition filters based on time-delay lines. The fitness parameter in the selection process is the discrimination of the target against all other bit patterns. For phase-shift-keyed headers, optimized filters exhibit improved discrimination compared with the matched filter. Time-domain filtering with no ambiguities is also achieved for specific amplitude-shift-keyed headers. Delay-line-based time-domain filters can be implemented in a planar lightwave circuit platform by simple phase and amplitude adjustments on the time delay taps.     

遗传算法优化设计三角晶格光子晶体

将遗传算法应用于三角晶格光子晶体的禁带设计过程. 考虑到可制备性，算法以第五能级下最大绝对禁带为优化目标，采用粗粒度像素表示原胞结构. 通过引入傅里叶变换数据备份机制以提高禁带计算速度和算法效率，从而快速搜索到了具有较大绝对禁带的“介质空气柱”散射体形状的三角晶格光子晶体，其最大绝对禁带的相对宽度值(绝对禁带宽度和中心高度的比值)为23.8%. 关键词： 二维光子晶体 三角晶格 光子带隙 遗传算法     

Impact load identification of composite structure using genetic algorithms

For structural health monitoring of composite structure, it is important to quickly and accurately identify the impact load whenever an impact event occurs. This paper proposes a genetic algorithms (GA)-based approach for impact load identification, which can identify the impact location and reconstruct the impact force history simultaneously. In this study, impact load is represented by a set of parameters, thus the impact load identification problem in both space (impact location) and time (impact force history) domains is transformed to a parameter identification problem. A forward model characterizes the dynamic response of the structure subject to a known impact force is incorporated in the identification procedure. By minimizing the difference between the analytical responses given by the forward model and the measured ones, GA adaptively identify the impact location and force history with its global search capability. This new impact identification approach is applied to a stiffened composite panel. The stiffened composite panel is modeled as an equivalent laminate with varying properties and the forward response is obtained by using an assumed modes approach. To improve the computational efficiency, micro-GA (μGA) is employed to perform the identification task. Numerical simulation studies are conducted to demonstrate the effectiveness and applicability of the proposed method.     

Design and optimization of phase plates for shaping partially coherent beams by adaptive genetic algorithms

The adaptive genetic algorithms (AGAs) are applied to the design and optimization of phase plates for shaping partially coherent beams. The simulation procedure based on the AGAs is described. Numerical examples for shaping partially coherent Cosh-Gaussian (ChG) and Hermite-Gaussian (HG) beams demonstrate the advantage of the AGAs in applications to the partially coherent beam transformation and shaping.     

Optimization of magnet sorting in a storage ring using genetic algorithms

In this paper, the genetic algorithms are applied to the optimization problem of magnet sorting in an electron storage ring, according to which the objectives are set so that the closed orbit distortion and beta beating can be minimized and the dynamic aperture maximized. The sorting of dipole, quadrupole and sextupole magnets is optimized while the optimization results show the power of the application of genetic algorithms in magnet sorting.     

Viscometric characterization of cobalt nanoparticle-based magnetorheological fluids using genetic algorithms

The rheological flow curves (shear stress vs. shear rate) of a nanoparticle cobalt-based magnetorheological fluid can be modeled using Bingham-plastic and Herschel–Bulkley constitutive models. Steady-state rheological flow curves were measured using a parallel disk rheometer for constant shear rates as a function of applied magnetic field. Genetic algorithms were used to identify constitutive model parameters from the flow curve data.     

采用遗传算法对压力脉动过程中气泡模型参数的辨识

流动液体中的压力变化会引起气泡和气穴的产生及破灭,而气泡和气穴又会对液体的流动产生影响及压力变化.为了合理预测流控系统瞬态压力脉动过程中气泡和气穴的体积变化及其对脉动传播过程的影响,基于气泡溶解和析出的物理过程,建立了压力脉动过程中气泡和气穴产生及破灭的数学模型,并提出采用遗传算法对气泡模型中初始气泡体积、气体溶解和析出时间常数进行参数辨识.以一段液压油管路为研究对象,对管路中伴随气泡和气穴的瞬态压力脉动过程进行仿真及实验研究.利用仿真及实验结果,验证了采用遗传算法对气泡模型进行参数辨识的可行性. 关键词： 气泡 气穴 压力脉动 参数辨识     

Phase shifting algorithms for non-sinusoidal waveforms with phase shift errors

In phase shifting interferometry, phase errors due to harmonic components of a fringe signal can be minimized by applying synchronous phase shifting algorithms with more than four samples. However, when the phase shift calibration is inaccurate, these algorithms cannot eliminate the effects of a non-sinusoidal waveform. It is shown that by taking a number of samples beyond one period of the fringe pattern, phase errors due to the harmonic components of the fringe signal can be eliminated, even when there exists a constant error in the phase shift interval. A general procedure for constructing phase shifting algorithms that eliminate these errors is derived. A seven-sample phase shifting algorithm is derived as an example, in which the effect of the second harmonic component can be eliminated in the presence of a constant error in the phase shift interval.     

Gas-solid phase calculations of binary mixtures using optimization of evolutionary algorithms

A comparison between different modern populations based optimization methods applied to the gas-solid phase calculations is presented. Simulations are carried out in twelve binary mixtures containing supercritical carbon dioxide. Particle swarm optimization (PSO) and genetic algorithm (GA) are used to calculate interaction parameters, solubility, and sublimation pressure on these mixtures using the Peng-Robinson equation of state with the Wong-Sandler mixing rules. Comparing PSO with GA shows that the performance of PSO is better than that of GA and that it is a preferable method to optimize parameters of the gas-solid equilibrium.     

Solving a class of continuous global optimization problems using quantum algorithms

We investigate the entwined roles that additional information and quantum algorithms play in reducing the complexity of a class of global optimization problems (GOP). We show that: (i) a modest amount of additional information is sufficient to map the continuous GOP into the (discrete) Grover problem; (ii) while this additional information is actually available in some GOPs, it cannot be taken advantage of within classical optimization algorithms; and (iii) quantum algorithms offer a natural framework for the efficient use of this information resulting in a speed-up of the solution of the GOP.     

Improving the performance of DCT-based fragile watermarking using intelligent optimization algorithms

The performance of a fragile watermarking method based on discrete cosine transform (DCT) has been improved in this paper by using intelligent optimization algorithms (IOA), namely genetic algorithm, differential evolution algorithm, clonal selection algorithm and particle swarm optimization algorithm. In DCT based fragile watermarking techniques, watermark embedding can usually be achieved by modifying the least significant bits of the transformation coefficients. After the embedding process is completed, transforming the modified coefficients from the frequency domain to the spatial domain produces some rounding errors due to the conversion of real numbers to integers. The rounding errors caused by this transformation process were corrected by the use of intelligent optimization algorithms mentioned above. This paper gives experimental results which show the feasibility of using these optimization algorithms for the fragile watermarking and demonstrate the accuracy of these methods. The performance comparison of the algorithms was also realized.