OPTIMIZATION OF MATERIAL COMPOSITION OF FUNCTIONALLY GRADED PLATE FOR THERMAL STRESS RELAXATION USING A GENETIC ALGORITHM

In this article, a genetic algorithm is applied to an optimization problem of material composition for a plate of step-formed functionally graded materials. The step-formed functionally graded plate is analyzed as a laminated composite plate made of numerous layers with homogeneous and different isotropic material properties. First, the onedimensional transient temperature distribution for a laminated plate is analyzed theoret ically. In addition, the thermal stress components for such an infinitely long plate are formulated under the mechanical condition of being traction-free. As a numerical example, a plate composed of zirconium oxide and titanium alloy is considered. In addition, for the optimization problem of minimizing the thermal stress distribution, the numerical calculations are made using a genetic algorithm without supposing a distribution function of material composition and the optimal material composition of each layer is determined taking into account the effect of the temperature dependency of material properties. Furthermore, the results obtained when a distribution function isn't specified and the results found when a distribution function is specified are compared.     

Genetic Algorithm Optimization for Tensioning in a Rotating Circular Saw Under a Thermal Load

The optimization problem for tensioning conditions in a rotating circular saw under a thermal load is solved by a genetic algorithm. The governing equations for the in-plane behavior due to rotation, thermal load by friction, and plastic strain by tensioning and those for the resulting out-of-plane behavior are presented; the solution for in-plane forces is obtained; and a modal analysis is performed. Numerical calculations are performed to investigate the effect of tensioning conditions on the natural frequencies. The optimization problem to maximize the natural frequency of the most critical mode is solved, and the optimal tensioning conditions are determined.     

Accurate estimation of cost function parameters for thermal power plants using a novel optimization approach

Accurate parameter estimation of the input-output characteristics in thermal power plants is an important issue in power system because these characteristics directly affect the economic dispatch calculations. Parameter estimation is an optimization problem in which the optimal values of the unknown parameters should be estimated by an optimization technique. By considering the valve-point effect, the parameter estimation will be more difficult since the fitness function of this optimization problem turns into a non-smooth and non-convex function in which finding the global optimal is a challenging task. In this paper, a recently proposed metaheuristic approach, crow search algorithm (CSA), is proposed for accurate estimation of the input-output characteristics of thermal power plants with and without valve-point effect. Simulation results show that CSA finds more promising results than least squares method (LSM), particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC).     

Synchronous machine parameters estimation using a new genetic‐based algorithm

A new genetic‐based algorithm (GA) for estimating synchronous machine parameters from frequency tests is presented in this paper. GAs are general search techniques based on biological concepts and are very suitable for solving optimization problems. The proposed method uses a set of digital measurements for the direct axis impedance magnitude and phase as functions of frequency for estimating both the d‐ and q‐axis parameters, such as direct reactance and time constants. The problem is formulated as an optimization problem and solved using the proposed method. Two different models along with different fitness functions are suggested to be used with the genetic algorithm. A practical example from the literature is used to test the proposed algorithm. The results obtained are compared with those given in the literature using other methods. The results and comparison show that the new algorithm is very applicable and highly accurate. Copyright © 1999 John Wiley and Sons, Ltd.     

改进加速遗传算法及其在非稳定渗流场反分析中的应用

在总结和分析基本遗传算法及其加速算法的基础上，提出了改进加速遗传算法，并针对非稳定渗流场反分析问题编制了计算程序，最后反分析计算了嶂山闸闸基非稳定渗流场关键计算参数，从而成功计算了闸基渗流场，并指出了闸基存在的主要问题。     

随机进化搜索算法及其收敛性

针对工程中具有约束条件的非线性函数的全局优化问题,提出了一种基于生物进化思想的随机进化搜索优化算法,在多方面弥补了遗传算法的不足,既具有遗传算法的全局优化能力,又显著地减小了计算复杂度.通过理论推求,结果证明了随机进化搜索算法的收敛性,同时指出单变量与多变量随机进化搜索算法无本质上差异,仅是选取的概率密度函数不同,该算法行之有效.     

基于改进遗传算法优化的小波神经网络短期负荷预测模型

针对传统小波神经网络在电力系统短期负荷预测中存在预测结果的精确度依赖初始网络参数的问题,提出了一种基于改进遗传算法优化的小波神经网络短期负荷预测模型。为了保证神经网络在训练过程中,各个层的权值和阈值按最优方向变化,将遗传算法引入小波神经网络,利用遗传算法寻优能力指导权值和阈值进行优化。将概率分布策略用于遗传算法的种群交叉和变异过程,解决遗传算法在中后期搜索精度差,收敛速度慢等问题。应用结果表明,与基本的小波神经网络的预测模型相比,在只考虑短期负荷历史数据的情况下,通过均方根误差计算比较,基于改进遗传算法优化的小波神经网络短期负荷预测模型具有更高的预测精度。     

基于遗传算法的水电站厂内经济运行模型研究

针对水电站厂内经济运行中需要考虑的关键问题，建立了相关的数学模型。根据该模型的特点，提出了一种基于遗传算法求解的新思路。该算法采用浮点数编码技术和以发电运行总成本倒数为适应度函数，并设计了一些启发式技术和遗传操作算予有效地处理模型中的各种约束条件，使得算法在遗传操作迭代过程中的所有个体都是可行解。实例研究表明，模型合理，算法可行、有效。     

基于相空间重构和GA-BP网络的径流预测

将相空间重构理论与神经网络以及遗传算法相结合提出了径流时间序列预测模型,通过相空间重构将一维径流时间序列拓展为多维序列,挖掘了更为丰富的信息,反映出系统的非线性特征,有利于神经网络建模和训练。研究表明,基于相空间重构理论的遗传算法和BP神经网络组合模型可较好地解决径流预测。以深圳宝安铁岗水库月径流为例,采用小波消噪对数据预处理,利用遗传算法训练BP神经网络,计算结果表明模型具有较高的预测精度。     

Inverse identification of temperature-dependent thermal conductivity via genetic algorithm with cost function-based rearrangement of genes

A new application of the cost function-based rearrangement of genes (proposed by Liu (2008) [1]) is presented in this paper through the genetic algorithm-based solution of the inverse heat conduction problem of identifying the temperature dependent thermal conductivity of a solid material using transient temperature histories. The inverse problem was defined according to the evaluation of the BICOND thermophysical property measurement method. Through the solution of the inverse problem (using simulated measurements), different approaches of the application of the rearrangement of genes were studied and compared. Application of the rearrangement significantly improved the convergence performance and accuracy of the inverse solution compared to a real-valued genetic algorithm, which was adapted to the problem by the authors. In the algorithm that performed best, the rearrangement was applied in an approach different from Liu’s. The effect of random noise added to the temperature history and the effect of regularization was also studied. With significant improvement in computational efficiency, the proposed algorithm is likely to be very effective in evaluation of real measured temperature histories to determine thermophysical properties.     

用于发电系统的局部进气跨声速涡轮气动设计和优化

为了解决高速机载涡轮发电系统效率较低的问题,通过一维计算和三维数值模拟相结合的方法,对以拉法尔喷管作为静叶、三维叶片作为动叶的局部进气跨声速涡轮级进行了研究。在对原型涡轮级流场分析后,通过ISIGHT优化软件集成NX,NUMECA和ANSYS等模块,采用多岛遗传算法,以喷管扩张角、扩张部分长度、周向排布角度以及动叶叶片进口几何角、出口几何角和轴向弦长作为优化变量对涡轮级进行了优化设计。最终得到给定设计工况下的最优几何参数。优化结果表明：优化得到的涡轮级功率达到了74 530 W,效率达到了79.60%,较原型提升了5.1%。     

基于自适应遗传退火算法的配电网故障定位研究

针对标准遗传算法易早熟收敛以及收敛速度慢的问题,提出了一种混合遗传算法（自适应遗传退火算法）用于解决辐射状配电网故障定位问题。该算法采用轮盘赌和最优保存策略相结合的选择机制,使得当前最优个体始终保持在种群里,并结合自适应交叉、变异概率,扩大种群的搜索范围,继而引入模拟退火算法,加快迭代后期算法的收敛速度。最后,通过对IEEE-33节点配电系统进行仿真计算,结果表明,该算法能够对单点和多点故障进行实时、准确地定位,并在故障信息畸变的情况下,也能快速地得到准确结果。     

A new technique for solving the multi-objective optimization problem using hybrid approach

Energy efficiency, which consists of using less energy or improving the level of service to energy consumers, refers to an effective way to provide overall energy. But its increasing pressure on the energy sector to control greenhouse gases and to reduce CO₂ emissions forced the power system operators to consider the emission problem as a consequential matter besides the economic problems. The economic power dispatch problem has, therefore, become a multi-objective optimization problem. Fuel cost, pollutant emissions, and system loss should be minimized simultaneously while satisfying certain system constraints. To achieve a good design with different solutions in a multi-objective optimization problem, fuel cost and pollutant emissions are converted into single optimization problem by introducing penalty factor. Now the power dispatch is formulated into a bi-objective optimization problem, two objectives with two algorithms, firefly algorithm for optimization the fuel cost, pollutant emissions and the real genetic algorithm for minimization of the transmission losses. In this paper the new approach (firefly algorithm-real genetic algorithm, FFA-RGA) has been applied to the standard IEEE 30-bus 6-generator. The effectiveness of the proposed approach is demonstrated by comparing its performance with other evolutionary multi-objective optimization algorithms. Simulation results show the validity and feasibility of the proposed method.     

Comparison between five stochastic global search algorithms for optimizing thermoelectric generator designs

In this study, the best settings of five heuristics are determined for solving a mixed-integer non-linear multi-objective optimization problem. The algorithms treated in the article are: ant colony optimization, genetic algorithm, particle swarm optimization, differential evolution, and teaching-learning basic algorithm. The optimization problem consists in optimizing the design of a thermoelectric device, based on a model available in literature. Results showed that the inner settings can have different effects on the algorithm performance criteria depending on the algorithm. A formulation based on the weighted sum method is introduced for solving the multiobjective optimization problem with optimal settings. It was found that the five heuristic algorithms have comparable performances. Differential evolution generated the highest number of non-dominated solutions in comparison with the other algorithms.     

集中式污水再生回用系统优化方法的比较

为解决具有多层次多目标特点的滇池流域集中式污水处理厂尾水再生回用及其远距离调度模型方法选用的问题,以传统内点算法和智能遗传算法为基础分别建立改进的系统优化方法。结合滇池流域规划决策实际,分析两种方法求解结果对该系统水量在设施、空间及用户供需分配上的定量差异。结果表明,基于遗传算法的优化方法较传统内点算法更适于该系统配置问题的求解。     

基于遗传算法的入湖河各口水环境容量计算模型及应用

针对传统计算入湖河口水环境容量采用混合区控制方法存在计算量和误差大的问题,提出一种以遗传算法为基础的湖泊污染源控制方法,基于反问题理念将遗传算法与湖泊二维水质模型相结合,仅需调用一次水量水质模型,即可计算入湖各河口的水环境容量。以太湖为例,构建了基于遗传算法的入湖各河口水环境容量计算模型,计算结果表明该模型简便、效率高,获得了太湖主要入湖各河口的COD水环境容量由大到小的排序规律。     

Selecting the optimum quasi-steady-state species for reduced chemical kinetic mechanisms using a genetic algorithm

A genetic optimization algorithm has been applied to the selection of quasi-steady-state (QSS) species in reduced chemical kinetic mechanisms. The algorithm seeks to minimize the error between reduced and detailed chemistry for simple reactor calculations approximating conditions of interest for a computational fluid dynamics simulation. The genetic algorithm does not guarantee that the global optimum will be found, but much greater accuracy can be obtained than by choosing QSS species through a simple kinetic criterion or by human trial and error. The algorithm is demonstrated for methane–air combustion over a range of temperatures and stoichiometries and for homogeneous charge compression ignition engine combustion. The results are in excellent agreement with those predicted by the baseline mechanism. A factor of two reduction in the number of species was obtained for a skeletal mechanism that had already been greatly reduced from the parent detailed mechanism.     

Optimization of heat conduction using combinatorial optimization algorithms

The Volume-to-Point (VP) problem is a base problem of heat conduction optimization. The nonlinear two-dimensional optimization problem of VP is discretized and transformed to a combinatorial optimization problem, which can be solved by some modern optimization algorithms. Algorithms for VP problem using simulated annealing and genetic algorithm are developed. Results for different cases are obtained using these algorithms. Analyses of the results and algorithms are also presented, that shows these algorithms are better than bionic optimization algorithm and constructal theory for VP problem, and can be generalized to complex conditions.     

Power generation expansion planning with adaptive simulated annealing genetic algorithm

In this paper, an adaptive simulated annealing genetic algorithm is proposed to solve generation expansion planning of Turkey's power system. Least‐cost planning is a challenging optimization problem due to its large‐scale, long‐term, nonlinear, and discrete nature of power generation unit size. Genetic algorithms have been successfully applied during the past decade, but they show some limitations in large‐scale problems. In this study, simulated annealing is used instead of mutation operator to improve the genetic algorithm. The improved algorithm is applied to the power generation system with seven types of generating units and a 20‐year planning horizon. The planning horizon is divided into four equal periods. The new algorithm provides approximately 6.6 billion US$ (3.2%) cheaper solution than GA and also shows faster convergence. Copyright © 2006 John Wiley & Sons, Ltd.     

混合遗传算法在贮灰坝监测系统上的应用

BP网络能较好地解决贮灰坝监测系统的预报问题，但容易陷入局部极小值；遗传算法能寻求到全局最优解，因此，将二者结合的突出优点在于可以克服前者经常得出局部极小值解的缺点，而取得全局最优解。将BP算法用于加强遗传算法的局部搜索，并且应用到贮灰坝的监测系统上，取得了较好的效果。