Optimum design of externally pressurized air bearing using Cluster OpenMP

This study presents a performance evaluation of a new portable parallel programming paradigm, the Cluster OpenMP (CLOMP) for distributed computing, in conducting an optimum design of air bearings. The multi-objective optimization was carried out by using a genetic algorithm (GA) incorporating Pareto optimality criterion. Since the GA is natural parallel evolution algorithm, the computation of the search was carried out in parallel by using the CLOMP. In this study, the performance of a CLOMP cluster of four dual-core computers for the air bearing optimization was compared with a shared-memory processing (SMP) computer equipped with two quad-core processors. To examine the parallel efficiency of the CLOMP in the GA optimization, several multithread applications of various task sizes were tested. It is shown that the air bearing optimization can be effectively dealt with by the CLOMP (parallel efficiency of 96.2-98.8%) as well as the SMP computing (93.1-99.4%) in the studied cases. The CLOMP retains the characteristics of directive-based OpenMP, such as incremental programming and serial-coding compatibility. The verified high parallel efficiency of the CLOMP cluster demonstrates its potential applications of the scalable computing in many tribological optimizations.     

基于遗传算法的弧齿锥齿轮传动的优化设计

遗传算法是一种借鉴与模拟生物进化过程自然选择与遗传机制求解极值问题的一类并行、随机的、自组织、自适应的智能搜索算法。其隐含并行性和对全局信息有效利用，使得该算法适合处理复杂和非线性优化问题。文章在介绍标准遗传算法的基础上，提出了基于遗传算法的弧齿锥齿轮优化设计方法。经实例计算，结果证明了遗传算法在弧齿锥齿轮传动优化设计中的有效性和正确性。     

Multi-objective optimization of air bearings using hypercube-dividing method

The commonly used genetic algorithm (GA) in solving a multi-objective optimization problem (MOOP) is replaced by the hypercube-dividing method (HDM) in this air bearing optimization study. In the new method the dividing of hypercubes in the design space is conducted based on the size and Pareto rank of hypercube. A comparison of the HDM- and GA-based method for the MOOP is performed. The results show that the solution obtained by the HDM is improved with more selections and less computing load. The search in the HDM can also be confined to some useful resolution to improve its global search capability.     

基于整数编码并行遗传算法的复合材料螺旋桨结构优化设计

提出一种针对大型复合材料结构铺层厚度和铺层顺序同时进行优化的整数编码并行遗传算法,并将该优化方法应用于复合材料螺旋桨结构优化问题.首先,在区域划分的基础上,对每一单层设定一个长度控制因子来决定单层的铺设区域,以实现对大型复合材料结构的整体一次性优化.然后对遗传算法进行改进,使之成为一种能同时优化铺层厚度和铺层顺序的高效算法.并采用并行编程语言标准MPI( message passing interface)构建并行编程环境,利用主从式并行遗传算法框架,实现遗传算法在单机多进程上的并行计算.最后针对复合材料螺旋桨结构进行优化设计,以验证该方法的高效性,并分析并行遗传算法的加速效果.     

基于降维的驾驶员侧约束系统高维目标优化

为解决将高维目标变为单目标优化时各子目标不能同时较优,而多目标算法直接用于高维目标优化时又存在难以找到一个有代表性的Pareto非劣解集问题,在某轿车驾驶员侧约束系统的优化过程中提出了乘员损伤准则与多目标算法协同优化的方法。在已有相关损伤准则基础上根据最新版的FMVSS 208和ECE R94法规提出了适合研究问题的损伤准则;以提出的损伤准则为媒介,将一个高维目标优化问题降为一个低维目标优化问题,通过灵敏度分析、实验设计、多项式近似模型筛选出优化设计变量并得到近似模型,用多目标算法NSGA-Ⅱ对近似模型进行计算得到Pareto非劣解集,将得到的Pareto非劣解集中的每个解代入损伤准则损伤值计算公式,升序排列得到各子目标同时较优而损伤值最小的优化解。最终的优化结果表明：该方法很好地解决了乘员约束系统的高维目标优化问题,优化效果明显。     

柔性剪切蒙皮支撑结构的拓扑优化

为了求解关于柔性剪切蒙皮支撑结构的多目标拓扑优化问题,提出了一种带有多重约束处理能力的位矩阵表示的非支配排序遗传算法。采用位矩阵作为遗传算法的染色体并引入基于矩阵操作的遗传算子,利用Ansys有限元分析获得结构质量、面内剪切性能和面外承载能力等目标。利用Matlab处理结构连通性和面内应变等约束并实现了基于矩阵的优化算法,获得了一系列可行的柔性剪切蒙皮支撑结构,在实际应用中可以根据需要选择合适的结构。从研究结果可以看出,该算法可以给多目标二维结构拓扑优化问题提供可行有效的解。     

Application of the Genetic Algorithm to the Multi-Objective Optimization of Air Bearings

A feasible solution must be obtained in a reasonable time with high probability of global optimum for a complex tribological design problem. To meet this decisive requirement in a multi-objective optimization problem, the popular and powerful genetic algorithms (GAs) are adopted in an illustrated air bearing design. In this study, the goal of multi-objective optimization is achieved by incorporating the criterion of Pareto optimality in the selection of mating groups in the GAs. In the illustrated example the diversity of group members in the evolution process is much better maintained by using Pareto ranking method than that with the roulette wheel selection scheme. The final selection of the optimal point of the points satisfied the Pareto optimality is based on the minimum–maximum objective deviation criterion. It is shown that the application of the GA with the Pareto ranking is especially useful in dealing with multi-objective optimizations. A hybrid selection scheme combining the Pareto ranking and roulette wheel selections is also presented to deal with a problem with a combined single objective. With the early generations running the Pareto ranking criterion, the resultant divergence preserved in the population benefits the overall GA's performance. The presented procedure is readily adoptable for parallel computing, which deserves further study in tribological designs to improve the computational efficiency.     

Heuristic optimization of state-of-charge feedback controller parameters for output power dispatch of hybrid photovoltaic/battery energy storage system

Output power fluctuation of photovoltaic (PV) sources is a problem of practical significance to utilities. To mitigate its impacts, particularly on a weak electricity network, a battery energy storage (BES) system can be used to smooth out and dispatch the output to the utility grid on an hourly basis. This paper presents an optimal control strategy of BES state-of-charge feedback (SOC-FB) control scheme used for output power dispatch of PV farm. The SOC-FB control parameters are optimized by using heuristic optimization techniques such as genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) in Matlab. In addition, an improved BES model is developed in PSCAD/EMTDC software package, in which GA is used to evaluate the optimal parameters. The studied multi-objective optimization problem also considers the evaluation of the optimal size of the BES. The performance of the proposed optimal SOC-FB control scheme is validated by comparing the results obtained from Matlab and PSCAD/EMTDC and with results from previous works. Finally, the best set of parameters are used to further validate the proposed method by using data obtained from the actual output of a grid-connected PV system.     

基于压缩频响函数的结构损伤识别

结构的损伤识别可作为一个优化问题来处理。本文直接应用频响函数(FRF)进行结构的损伤识别。通过对FRF的主成分分析(PCA)实现数据压缩和特征提取,建立基于压缩FRF的优化目标函数。为了提高算法的收敛速度,以结合局部搜索算法(LS)的遗传算法(GA)为优化工具,并进一步结合子结构识别法来求解。基于桁架的计算结果表明,这种方法具有很好的鲁棒性和识别效果。     

Integrating Box-Behnken design with genetic algorithm to determine the optimal parametric combination for minimizing burr size in drilling of AISI 316L stainless steel

This paper illustrates the methodology of genetic algorithm (GA) based multi-objective drilling process optimization. The optimal values of cutting speed, feed, point angle and lip clearance angle for a specified drill diameter were determined using GA, which simultaneously minimize burr height and burr thickness at the exit of holes during drilling of AISI 316L stainless steel. The burr size models required for GA optimization were developed using response surface methodology (RSM) with drilling experiments planned as per Box-Behnken design. The GA optimization results reveal that point angle has a significant role in controlling the burr size.     

Initial design domain reset method for genetic algorithm with parallel processing

The Genetic Algorithm (GA), an optimization technique based on the theory of natural selection, has proven to be a relatively robust means of searching for global optimum. It converges to the global optimum point without auxiliary information such as differentiation of function. In the case of a complex problem, the GA involves a large population number and requires a lot of computing time. To improve the process, this research used parallel processing with several personal computers. Parallel process technique is classified into two methods according to subpopulation’s size and number. One is the fine-grained method (FGM), and the other is the coarse-grained method (CGM). This study selected the CGM as a parallel process technique because the load is equally divided among several computers. The given design domain should be reduced according to the degree of feasibility, because mechanical system problems have constraints. The reduced domain is used as an initial design domain. It is consistent with the feasible domain and the infeasible domain around feasible domain boundary. This parallel process used the Message Passing Interface library.     

基于遗传算法和神经网络的六自由度并联平台位置正解

位置正解是并联平台机构应用的基础。提出了用GA +BP混合算法求六自由度并联平台位置正解的方法。首先用改进的Newton Raphson法对数学迭代模型进行求解运算 ,将所得的输入输出数据组作为训练样本 ,再用GA+BP混合算法对该模型进行了精确求解 ,仿真研究表明GA +BP混合算法运算速度快、计算精度高 ,用于求解并联平台机构的位置正解是一种比较理想的方法。     

Genetic Algorithm (GA) for Multivariable Surface Grinding Process Optimisation Using a Multi-objective Function Model

A genetic algorithm (GA) based optimisation procedure has been developed to optimise the surface grinding process using a multi-objective function model. The following ten process variables are considered in this work: wheel speed, workpiece speed, depth of dressing, lead of dressing, cross-feedrate, wheel diameter, wheel width, grinding ratio, wheel bond percentage, and grain size. The procedure evaluates the production cost and production rate for the optimum grinding conditions, subject to constraints such as thermal damage, wheel-wear parameters, machine-tool stiffness and surface finish. A worked example is used to illustrate how this procedure can be used to produce optimum production rate, low production cost, and fine surface quality for the surface grinding process.     

基于Internet网络的主从式并行遗传算法及其在叶栅优化中的应用

为了解决遗传算法在优化中由于适应度评价很费时而导致计算时间过长的问题 ,本文发展了一种基于In ternet网络实现的主从式并行遗传算法。在函数优化的测试实验中 ,通过控制待优化函数适应度评价的时间 ,验证了主从式模型在适应度评价很费时且远远超过通讯时间时将获得接近于线性的加速比 ,讨论了主从式并行遗传算法应用于气动性能优化中的可行性。通过二维叶栅的优化算例 ,证明了本文提出的算法适合于需要大计算资源的叶栅气动优化设计     

基于多目标遗传算法的混合动力电动汽车控制策略优化

混合动力电动汽车是一个高度复杂的非线性系统,并且影响其控制策略的参数较多,要对这样的系统进行优化,常规的优化算法显得无能为力,模型的精确程度也直接影响了选取参数的可靠性.应用汽车动力性、排放性高级模拟分析软件AVL CRUISE,联合Matlab/Simulink软件,建立合动力电动城市客车整车动态性能仿真分析模型,以百公里油耗和排放指标为优化目标,运用多目标遗传优化算法,针对欧洲、日本及中国的城市公交循环工况对混合动力系统工作模式的选择和能量流的分配进行全局优化,减少了运算时间,获得一组可靠的可行解,精确地确定出控制逻辑参数.该解集在很大程度上同时提高了原车的燃料经济性和排放性能,并且为混合电动车的设计和控制提供了一个适宜的选择范围,设计者可以按不同的要求进行不同的方案选择.     

Multi-objective optimization of green sand mould system using evolutionary algorithms

The quality of cast products in green sand moulds is largely influenced by the mould properties, such as green compression strength, permeability, hardness and others, which depend on the input (process) parameters (that is, grain fineness number, percentage of clay, percentage of water and number of strokes). This paper presents multi-objective optimization of green sand mould system using evolutionary algorithms, such as genetic algorithm (GA) and particle swarm optimization (PSO). In this study, non-linear regression equations developed between the control factors (process parameters) and responses like green compression strength, permeability, hardness and bulk density have been considered for optimization utilizing GA and PSO. As the green sand mould system contains four objectives, an attempt is being made to form a single objective, after considering all the four individual objectives, to obtain a compromise solution, which satisfies all the four objectives. The results of this study show a good agreement with the experimental results.     

基于Pareto解集的多目标优化方法及其应用

针对传统多目标优化设计方法的弱点，基于Pareto概念，借助遗传算法所具有的并行搜索特性，引入群体排序技术、小生境技术求得多目标优化问题的Pareto解集，实现了先寻优后决策的求解模式。实际工程算例表明，该模式可同时获得多个Pareto最优解，据此决策能有效弱化设计人员先验知识不足的影响，因而较传统多目标优化方法更为实用有效。     

A Divide-and-Conquer Parallel Computing Scheme for the Optimization Analysis of Tribological Systems

The trend of using commercial products and open source packages to construct a scalable computer cluster for distributed computing to minimize the execution time of numerical optimization has long been expected. However, in the tribology field progress has been slow due to the complexity of parallel coding and the lack of easy-to-implement parallel algorithms. This study presents an optimization analysis of constrained problems by using a divide-and-conquer scheme suitable for parallel computation. A porous air bearing model of moderate computational load is used to illustrate the optimization procedure. In the optimization process, the design space is subdivided and each of the subdivisions is dealt with by Taguchi's Design of Experiments to achieve the local optimum. The global optimum is then determined when all the local optima are obtained. Two task-assignment strategies in the cluster computing are implemented and discussed. Reasonable speedup and parallel efficiency were obtained for the highly uneven task-load calculations. The approach does not require the knowledge of parallel programming techniques associated with message passing libraries. The presented scheme has high portability, low cost of evaluation process, and algorithm-machine scalability, which should be an easy-to-implement and efficient tool for many tribological studies.     

Multi-objective optimization of electric-discharge machining process using controlled elitist NSGA-II

Parametric optimization of electric discharge machining (EDM) process is a multi-objective optimization task. In general, no single combination of input parameters can provide the best cutting speed and the best surface finish simultaneously. Genetic algorithm has been proven as one of the most popular multi-objective optimization techniques for the parametric optimization of EDM process. In this work, controlled elitist non-dominated sorting genetic algorithm has been used to optimize the process. Experiments have been carried out on die-sinking EDM by taking Inconel 718 as work piece and copper as tool electrode. Artificial neural network (ANN) with back propagation algorithm has been used to model EDM process. ANN has been trained with the experimental data set. Controlled elitist non-dominated sorting genetic algorithm has been employed in the trained network and a set of pareto-optimal solutions is obtained.

     

Dynamic balancing of planar mechanisms using genetic algorithm

This paper presents an optimization technique to dynamically balance the planar mechanisms in which the shaking forces and shaking moments are minimized using the genetic algorithm (GA). A dynamically equivalent system of point-masses that represents each rigid link of a mechanism is developed to represent link’s inertial properties. The shaking force and shaking moment are then expressed in terms of the point-mass parameters which are taken as the design variables. These design variables are brought into the optimization scheme to reduce the shaking force and shaking moment. This formulates the objective function which optimizes the mass distribution of each link. First, the problem is formulated as a single objective optimization problem for which the genetic algorithm produces better results as compared to the conventional optimization algorithm. The same problem is then formulated as a multi-objective optimization problem and multiple optimal solutions are created as a Pareto front by using the genetic algorithm. The masses and inertias of the optimized links are computed from the optimized design variables. The effectiveness of the proposed methodology is shown by applying it to a standard problem of four-bar planar mechanism available in the literature.