基于CEEMDAN多尺度熵和SSA-SVM的滚动轴承故障诊断研究

针对支持向量机(SVM)应用在轴承故障分类时,传统的智能算法优化SVM的参数容易存在寻优速度慢、调节参数多,以及容易陷入局部最优值等问题,提出了一种基于CEEMDAN多尺度熵与SSA-SVM相结合的故障诊断方法。对滚动轴承的故障特征提取和SVM参数优化进行了研究,引入了一种新的群智能优化算法,用麻雀搜索算法(SSA)对SVM参数进行了优化,提高了寻优速度以及轴承的故障分类准确率;该方法先采用自适应白噪声完整经验模态分解(CEEMDAN)算法分解信号,获得了若干个固有模态函数(IMF);再采用相关系数方法选择有用IMF分量,并进行了重新组合;最后,计算重构信号的多尺度熵作为特征向量,输入SSA优化的SVM进行了故障分类。研究结果表明:采用该方法能够准确地获得故障信息,且识别准确率高;与PSO、GA优化的SVM相比,该方法的故障诊断分类性能更好。     

基于变步长天牛须搜索算法的空间直线度误差评定

为提高空间直线度评定精度,建立了空间直线度数学模型。采用变步长天牛须搜索算法对目标函数进行求解,主要包括天牛须搜索及变步长等步骤。针对原始天牛须搜索算法求解精度不高,易陷入局部最优等问题,设计变步长方法,增强算法多样性,从而充分提高计算精度。对其进行求解,并根据终止准则得到计算结果,将该寻优结果与其他方法的评定结果相比较,验证了该算法的可行性及优越性。     

基于启发式图搜索的最小测点集优选新算法

模拟故障字典的测点选择问题是目前的一个研究热点.本文将这一问题转化为启发式图搜索问题,测点选择过程就变成了图节点的扩展过程.运用信息理论和M进制编码规则,首先给出了启发函数的计算方法,推导出图节点的构造方法和扩展规则,然后在此基础上给出了用于测试节点优选的启发式图搜索算法,最后进行了分析实验.实验结果表明本算法既克服了局部寻优方法不能找到全局最小测点集的缺点,又能显著降低传统穷举搜索算法的时间复杂度和空间复杂度.     

GPS路径寻优算法的研究

本文主要研究了车辆导航系统及一种路径寻优算法--方向搜索算法。针对地理信息系统中特定的两点路径寻优问题,提出一种方向优先的快速搜索算法。该算法在路径搜索过程中,首先搜索与前进方向更加接近的方向,可以在搜索的早期找到最短路径,从而在以后的搜索中剪去更多的节点和分支,提高最优路径的搜索速度。     

基于Pareto优化的离散自由搜索算法求解多目标柔性作业车间调度问题

针对多目标柔性作业车间调度问题搜索空间的离散性和求解算法的收敛性,提出一种基于Pareto优化的离散自由搜索算法来求解多目标柔性作业车间调度问题。在建立基于Markov链数学模型的基础上,证明了算法以概率1收敛;引入首达最优解期望时间来分析算法收敛速度,并分析了算法时间复杂度。采用基于工序排序和机器分配的个体表达方式,在多目标柔性作业车间离散域,利用自由搜索算法在邻域小步幅精确搜索和在全局空间大步幅勘测进行寻优;通过自由搜索算法自适应赋予个体各异辨别能力和Pareto优化概念来比较个体优劣性,不仅保留优化个体,而且使个体寻优方向沿多目标柔性作业车间调度问题Pareto前沿逼近。通过对搜索过程中产生的伪调度方案进行可行性判定,以确保调度方案可行。采用10×10FJSP和8×8FJSP问题的实例进行寻优测试,验证了所提算法的可行性和有效性。     

非负二元函数最大值求解的复变函数法

二元函数优化问题在许多工程问题中广泛存在,其全局寻优方法一直是人们研究的热点问题之一。基于复变函数中解析函数最大模理论针对一类非负二元函数的全局寻优问题提出了一种高效方法,它可以将目标函数在有界二维区域上的寻优问题简化为一维全局优化问题的求解,给出了方法可行性的理论依据,并用三个算例验证了方法的有效性。方法和结论一方面可直接用于解决解析函数应用场合中的优化问题;另一方面对于适用的二维数学优化问题可实现高精度、高效率的全局寻优。     

快速全局优化算法在电磁场逆问题中的应用

为解决电磁场逆问题分析计算过度依赖计算机资源这一"瓶颈"问题,提出了一种新的快速全局优化算法.该算法首先应用基于紧支集径向基函数(CS-RBF)的改进表面响应模型(RSM)重构原优化问题;然后应用改进的粒子群算法(PSO)对重构的优化问题进行快速全局寻优.研究表明,典型数学函数验证以及电磁场逆问题的分析和计算,验证了算法的全局搜索和快速寻优能力.     

基于JIT的面向JSSP一种启发式快速搜索算法

将准时制(JIT)生产模式与启发式搜索算法相结合,应用到作业车间调度问题(Job Shop Scheduling Problem,JSSP)上,针对目前主流的启发式搜索算法(遗传算法GA等)存在的缺陷,提出了一种面向JSSP快速任务调度搜索算法.对国际上通用的benchmark算例模拟实验表明,该算法求解速度快,解质量稳定,应用于实时调度和大规模调度问题具有一定的优势.     

基于随机聚焦搜索算法的冲压成形工艺优化

针对板料冲压成形工艺优化问题,研究了一种群集智能算法。该方法通过正交实验与数字化仿真技术相结合获取神经网络的学习样本,利用反向传播神经网络构建随机聚焦搜索算法的目标函数模型。在此模型基础上,应用随机聚焦搜索算法对板料冲压成形的工艺参数进行优化。以深盒形件为例,将优化后的工艺参数输入eta／DYNAFORM仿真模型进行验证,结果表明该算法可获得较好的成形质量。为了进一步验证随机聚焦搜索算法在执行效率及寻优的全局搜索方面的优越性,与遗传算法的优化结果进行对比分析,说明随机聚焦搜索在板料冲压成形工艺参数优化方面是一种较好的优化算法。     

多对多板坯倒垛问题的一种邻域搜索算法

为解决热轧生产计划中出现的板坯倒垛问题,建立了基于轧制位置与库内板坯多对多对应关系的问题模型,提出了一种新的邻域搜索算法。该算法考虑到轧制计划中的板坯规格组在库内垛位中连续堆放的特点,引入了Sequence邻域概念,使算法能够更好地利用问题的特征,并通过两阶段的寻优替换对问题进行求解。实验结果表明了所提模型和算法的可行性和有效性。     

A two-stage genetic algorithm for large size job shop scheduling problems

The majority of large size job shop scheduling problems are non-polynomial-hard (NP-hard). In the past few decades, genetic algorithms (GAs) have demonstrated considerable success in providing efficient solutions to many NP-hard optimization problems. But there is no literature available considering the optimal parameters when designing GAs. Unsuitable parameters may generate an inadequate solution for a specific scheduling problem. In this paper, we proposed a two-stage GA which attempts to firstly find the fittest control parameters, namely, number of population, probability of crossover, and probability of mutation, for a given job shop problem with a fraction of time using the optimal computing budget allocation method, and then the fittest parameters are used in the GA for a further searching operation to find the optimal solution. For large size problems, the two-stage GA can obtain optimal solutions effectively and efficiently. The method was validated based on some hard benchmark problems of job shop scheduling.     

Ordinal optimisation of genetic control parameters for flow shop scheduling

Genetic algorithm (GA) has been widely applied to many non-polynomial hard optimisation problems, such as flow shop and job shop scheduling. It is well known that the efficiency and effectiveness of GA highly depend on its control parameters, but even setting suitable parameters often suffers from tedious trial and error. Currently, setting optimal parameters is still an open problem and one of the most important and promising areas for GA. In this paper, the determination of optimal GA control parameters with limited computational effort and total simulation replication constraint, namely, population size, crossover and mutation probabilities, is firstly formulated as a stochastic optimisation problem. Ordinal optimisation and optimal computing budget allocation are then applied to select the optimal GA control parameters while providing reasonable performance evaluation for hard flow shop scheduling problems. Lastly the effectiveness of the methodology is demonstrated by simulation results based on benchmarks.     

The ordinal optimisation of genetic control parameters for flow shop scheduling

Genetic algorithms (GAs) have been widely applied for many non-polynomial hard optimisation problems, such as flow shop and job shop scheduling. It is well known that the efficiency and effectiveness of a GA is highly depend on its control parameters, but setting suitable parameters often involves tedious trial and error. Currently, setting optimal parameters is still a substantial problem and is one of the most important and promising areas for GAs. In this paper, the determination of optimal GA control parameters with limited computational effort and simulation replication constraints, namely, population size, crossover and mutation probabilities, is firstly formulated as a stochastic optimisation problem. Then, the ordinal optimisation (OO) and the optimal computing budget allocation (OCBA) are applied to select the optimal GA control parameters, thereby providing a reasonable performance evaluation for hard flow shop scheduling problems. The effectiveness of the methodology is demonstrated by simulation results based on benchmarks.     

Estimation of Optimum Genetic Control Parameters for Job Shop Scheduling

Genetic algorithms (GA) have demonstrated considerable success in providing good solutions to many non-polynomial hard optimization problems. GAs are applied for identifying efficient solutions for a set of numerical optimization problems. Job shop scheduling (JSS) has earned a reputation for being difficult to solve. Many workers have used various values of genetic parameters. This paper attempts to tune the control parameters for efficiency, that are used to acceleate the genetic algorithm (applied to JSS) to converge on an optimal solution. The genetic parameters, namely, number of generations, probability of crossover, probability of mutation, are optimized relating to the size of problems. The results are validated in job shop scheduling problems. The results indicate that by using an appropriate range of parameters, the genetic algorithm is able to find an optimal solution faster. RID=" ID=" <E5>Correspondence and offprint requests to</E5>: Dr S. G. Ponnambalam, Department of Production Engineering, Regional Engineering College, Tiruchirapalli, 620 015, India. E-mail: pons&commat;rect.ernet.in     

车身复杂曲线匹配优化问题研究

在汽车白车身的过程中，往往会遇到两个具有复杂曲面轮廓的三维零件的匹配校园优化问题，为此提出了一种新的解决三维曲线空间匹配位置优化的优化算法，通过构造适当的优化具有目标函数，提高优化求解的收敛速度，增强该算法的实用性和灵活性。最后讨论了该遗传算法的具体结构和改进的算法。     

Bi-objective redundancy allocation problem for a system with mixed repairable and non-repairable components

Traditionally, in the redundancy allocation problem (RAP), two general classes of optimization problems are considered; reliability optimization and availability optimization. Contrary to reliability optimization, fewer researchers have studied availability optimization to find out the optimal combination of components type and redundancy levels for each subsystem in a system for maximizing (or minimizing) the objectives. In each problem it is assumed that either the entire components are repairable or they are non-repairable. However, in real world situations, systems usually consist of both repairable and non-repairable components. In this paper a new Mixed Integer Nonlinear Programming (MINLP) model is presented to analyze the availability optimization of a system with a given structure, using both repairable and non-repairable components, simultaneously. To find the solution of the introduced MINLP, an efficient Genetic Algorithm (GA) is also developed. Furthermore, to show the efficiency of the proposed GA, a numerical example is presented. Experimental results demonstrate that the proposed GA has a better performance compared to one of the most recommended algorithm in the literature.     

Modeling of binocular vision system for 3D reconstruction with improved genetic algorithms

This paper models the binocular vision system focused on 3D reconstruction and describes an improved genetic algorithm (GA) for estimating camera system parameters. The two-camera system model that takes into account camera radial distortion includes a total of 24 parameters. The proposed improved GA is used to solve this nonlinear optimization problem with high dimension. In our improved GA, the adaptive control of camera parameter search interval and the catastrophe strategy with elitist preservation are employed. The experimental results indicate that our improved GA is effective to solve the multi-peak function optimization problem and the 3D reconstruction accuracy of the binocular vision system is promising.     

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。     

基于父子混合选择遗传算法的大型压力机主梁的最优设计

主梁是大型压力机的关键受力构件之一,其最优设计问题是一类很难求解的非线性规划问题.传统的优化方法在解决这类复杂优化问题时显示出自身的一些不足.针对传统优化算法的局限性,提出用简单实用的基于父子混合选择遗传算法,对大型压力机的主梁进行优化设计.并用一个仿真算例验证了该算法的有效性和可行性.     

A hybrid particle swarm optimization for parallel machine total tardiness scheduling

The parallel machine scheduling problem has received increasing attention in recent years. This research considers the problem of scheduling jobs on parallel machines with a total tardiness objective. In the view of its non-deterministic polynomial-time hard nature, the particle swarm optimization (PSO), which is inspired by the swarming or collaborative behavior of biological populations, is employed to solve the parallel machine total tardiness problem (PMTP). Since it is very hard to directly apply standard PSO to this problem, a new solution representation is designed based on real number encoding, which can conveniently convert the job sequences of PMTP to continuous position values. Moreover, in order to enhance the performance of PSO, we introduce clonal selection algorithm (CSA) into PSO and therefore propose a new CSPSO method. The incorporation of CSA can greatly improve the swarm diversity and avoid premature convergence. We further investigate three parameters of PSO and CSPSO, finding that the parameters have marginal impact on CSPSO, which indicates that CSPSO is a very stable and robust method. The performance of CSPSO is evaluated in comparison with traditional genetic algorithm (GA) and standard PSO on 250 benchmark instances. Experimental results show that CSPSO significantly outperforms GA and PSO, with obtaining the optimal solutions of 237 instances. Additionally, PSO appears more effective than GA.