A Kriging-based algorithm to optimize production systems approximated by analytical models

Production system optimization still remains a difficult problem even if fast analytical methods are used to estimate their mean performance measures. This paper addresses optimization problems in which the system performance measures are obtained from analytical methods implemented in computer codes that are usually time expensive. A global search algorithm is proposed to solve the addressed optimization problem. A Kriging metamodel is built to approximate the system performance function on the basis of the deterministic output values provided by the analytical model. Then a standard optimization method is applied on the explicit metamodel expression. The main advantages of the proposed method are its generality and ease of use. Indeed, the algorithm can be applied to optimize any production system assessable by an analytical method. Also, the Kriging technique allows contemporarily building the approximation of the unknown function and assessing its quality. Numerical results are satisfactory and prove the applicability of the method to real problems.     

不确定广义系统的时滞依赖鲁棒镇定

研究不确定广义系统的时滞依赖鲁棒镇定问题。利用Lyapunov泛函方法,得到一个线性矩阵不等式(LMIs)形式的时滞依赖稳定与镇定判据。新方法考虑一些以前方法中通常忽略的有用的项,引入一些自由权重矩阵,估计Lyapunov泛函导数的上界;再用凸优化算法,进一步给出状态反馈控制器的设计方法。最后通过两个仿真示例表明了新方法的有效性。     

Solution quality of random search methods for discrete stochastic optimization

In this paper, we propose a framework for selecting a high quality global optimal solution for discrete stochastic optimization problems with a predetermined confidence level using general random search methods. This procedure is based on performing the random search algorithm several replications to get estimate of the error gap between the estimated optimal value and the actual optimal value. A confidence set that contains the optimal solution is then constructed and methods of the indifference zone approach are used to select the optimal solution with high probability. The proposed procedure is applied on a simulated annealing algorithm for solving a particular discrete stochastic optimization problem involving queuing models. The numerical results indicate that the proposed technique indeed locate a high quality optimal solution.     

Mobile robot localization based on PSO estimator

Localization is fundamental to autonomous operation of the mobile robot. A particle filter (PF) is widely used in mobile robot localization. However, the robot localization based PF has several limitations, such as sample impoverishment and a degeneracy problem, which reduce significantly its performance. Evolutionary algorithms, and more specifically their optimization capabilities, can be used in order to overcome PF based on localization weaknesses. In this paper, mobile robot localization based on a particle swarm optimization (PSO) estimator is proposed. In the proposed method, the robot localization converts dynamic optimization to find the best robot pose estimate, recursively. Unlike the localization based on PF, the resampling step is not required in the proposed method. Moreover, it does not require noise distribution. It searches stochastically along the state space for the best robot pose estimate. The results show that the proposed method is effective in terms of accuracy, consistency, and computational cost compared with localization based on PF and EKF.     

基于蚁群算法的IP网络流量矩阵估计

针对IP网络流量矩阵(TM)估计的高度病态性,导致很难精确估计网络流量矩阵,因此提出了一种基于蚁群优化(ACO)算法的IP网络流量矩阵估计方法。通过适当的建模,将流量矩阵估计问题转化为最优化问题,再通过蚁群算法求解模型,有效解决了网络流量矩阵估计。通过测试结果分析,与现存的方法相比,所提算法的精度比最大熵和二次规划稍差,但这两种方法复杂度太高,不适用于大规模网络,因此,在网络规模较大的情况下,算法是较优的,可提高流量矩阵估计的精度。     

Genetic algorithm based NARX model identification for evaluation of insulin sensitivity

The evaluation of insulin sensitivity plays an important role in the clinical investigation of glucose related diseases. Mathematical models based on non-invasive tests provide an estimate of insulin sensitivity by solving a nonlinear optimization problem. However traditional optimization methods suffer from convergence problem and the final estimate is heavily dependent on the initial parameterization. This paper proposes a model based on the hybrid approach of nonlinear autoregressive with exogenous input (NARX) modeling and genetic algorithm (GA) for deriving an index of insulin sensitivity. The model does not need an initial parameterization and the convergence is always guaranteed. The index derived from the proposed model is found to correlate well with the widely used minimal model based insulin sensitivity, with a significantly higher accuracy of fit.     

Algorithms for optimal partial matching of free-form objects with scaling effects

A free-form object matching problem is addressed in this paper. Two methods are proposed to solve a partial matching problem with scaling effects and no prior information on correspondence or the rigid body transformation involved. The first method uses umbilical points, which behave as fingerprints of a surface and their qualitative properties can be used for matching purposes. The second method uses an optimization scheme based on the extension of the KH curvature matching method [Comput. Aided Design 35 (2003) 913], first introduced in the context of a matching problem without scaling effects. Two types of curvatures, the Gaussian and the mean curvatures, are used to establish correspondences between two objects. The curvature matching method is formulated in terms of minimization of an objective function depending on the unknown scaling factor, and the rigid body transformation parameters. The accuracy and complexity of the proposed methods as well as the convergence for the optimization approach are analyzed. Examples illustrate the two methods.     

A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation

Metaheuristic optimization algorithms have become a popular choice for solving complex problems which are otherwise difficult to solve by traditional methods. However, these methods have the problem of the parameter adaptation and many researchers have proposed modifications using fuzzy logic to solve this problem and obtain better results than the original methods. In this study a comprehensive review is made of the optimization techniques in which fuzzy logic is used to dynamically adapt some important parameters in these methods. In this paper, the survey mainly covers the optimization methods of Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Ant Colony Optimization (ACO), which in the last years have been used with fuzzy logic to improve the performance of the optimization methods.     

有色冶金过程不确定优化方法探讨

有色冶金过程受原料来源多样、工况条件波动、生产成分变化等因素的影响,存在大量的不确定性,严重影响了冶炼生产的稳定性与可靠性.鉴于此,综述不同类型不确定性优化问题的描述方法,具体包括概率不确定优化问题、模糊不确定优化问题和区间不确定优化问题.通过分析有色冶金生产过程的特点与需求,以3种典型的有色冶金过程不确定优化问题为例,探讨不同类型的有色冶金过程不确定优化方法.针对氧化铝生料浆配料过程的概率不确定优化问题,采用基于Hammersley sequence sampling(HSS)的方法实现不确定模型的确定性转换;针对湿法炼锌除铜过程的模糊不确定优化问题,采用基于模糊规则的方法进行确定性评估;针对锌电解分时供电过程的区间不确定优化问题,采用基于min-max的方法求解鲁棒解.工业运行数据均验证了上述方法的有效性.     

一种自适应鲁棒最小体积高光谱解混算法

对高光谱图像解混的目的在于从低空间分辨率的高光谱图像中找到端元与对应的丰度.本文根据解混算法中的最小体积准则,提出了一种自适应鲁棒最小体积高光谱解混算法（Robust minimum volume based algorithm with automatically estimating regularization parameters for hyperspectral unmixing,RMVHU）.本算法通过引入负数惩罚正则项,替换了同类算法中的丰度非负性约束（Non-negativity constraint,ANC）,使算法对图像中的噪声与异常值具有更强的鲁棒性;采用循环最小化方法,将非凸优化问题分解为凸优化子问题,然后应用交替方向乘子法解决随着像素点个数增大带来的求解困难问题;对于正则项系数,本算法提出了一种自适应调整策略,提高了算法的收敛性,并且通过定性分析,说明了该调整方法的合理性.将算法应用于合成数据与实际数据,实验结果表明,与同类算法相比,本文提出的算法能够取得更为优秀的效果.     

基于扩张状态观测器的动态抗饱和补偿器设计方法

针对一类受外界扰动以及执行器饱和影响的不确定非线性系统,提出一种基于扩张状态观测器的动态抗饱和补偿器设计方法.首先通过将系统的不确定项以及外部扰动作为扩张状态,设计线性扩张状态观测器(ESO)对系统的总扰动进行估计;然后,在控制器中引入对扩张状态的估计值,对系统的总扰动进行补偿,设计了动态抗饱和补偿器,将控制器、观测器以及动态抗饱和补偿器的参数求解问题转化为基于LMI不等式组约束的优化问题,确保系统具有尽可能大的收敛域;最后通过数值仿真验证所提出设计方法的有效性.     

Likelihood-based representation of epistemic uncertainty and its application in robustness-based design optimization

In this paper, we propose a new likelihood-based methodology to represent epistemic uncertainty described by sparse point and/or interval data for input variables in uncertainty analysis and design optimization problems. A worst-case maximum likelihood-based approach is developed for the representation of epistemic uncertainty, which is able to estimate the distribution parameters of a random variable described by sparse point and/or interval data. This likelihood-based approach is general and is able to estimate the parameters of any known probability distributions. The likelihood-based representation of epistemic uncertainty is then used in the existing framework for robustness-based design optimization to achieve computational efficiency. The proposed uncertainty representation and design optimization methodologies are illustrated with two numerical examples including a mathematical problem and a real engineering problem.     

Parameter estimation of chaotic systems by a nonlinear time-varying evolution PSO method

An important issue in nonlinear science is parameter estimation for Lorenz chaotic systems. There has been increasing interest in this issue in various research fields, and it could essentially be formulated as a multidimensional optimization problem. A novel evolutionary computation algorithm, nonlinear time-varying evolution particle swarm optimization (NTVEPSO), is employed to estimate these parameters. In the NTVEPSO method, the nonlinear time-varying evolution functions are determined by using matrix experiments with an orthogonal array, in which a minimal number of experiments would have an effect that approximates tothe full factorial experiments. The NTVEPSO method and other PSO methods are then applied to identify the Lorenz chaotic system. Simulation results demonstrate the feasibility and superiority of the proposed NTVEPSO method.     

Layout optimization of looped networks by constrained ant colony optimisation algorithm

A constrained version of ant colony optimisation algorithm (ACOA) is proposed in this paper for layout optimization of looped water distribution networks. A novel formulation is used to represent the layout optimization problem of pipe networks in the proper form required for the application of the ant algorithm. The proposed formulation is based on the engineering concept of reliability in which the number of independent paths from the source node to each of the network nodes is considered as a measure of reliability. In the proposed formulation, the ants are constrained to choose from the options provided by a constraining procedure so that only looped layouts are constructed by the ant leading to huge reduction of search space size compared to the original search space. Three different constraining procedures are used leading to three different algorithms. The proposed methods are used to find the optimal layout of three benchmark examples from the literature and the results are presented and compared to the results of the conventional ant colony optimization algorithm. The results show the efficiency and effectiveness of the proposed method for optimal layout determination of looped networks.     

Artificial Bee Colony Algorithm-based Parameter Estimation of Fractional-order Chaotic System with Time Delay

It is an important issue to estimate parameters of fractional-order chaotic systems in nonlinear science, which has received increasing interest in recent years. In this paper, time delay and fractional order as well as system's parameters are concerned by treating the time delay and fractional order as additional parameters. The parameter estimation is converted into a multi-dimensional optimization problem. A new scheme based on artificial bee colony (ABC) algorithm is proposed to solve the optimization problem. Numerical experiments are performed on two typical time-delay fractional-order chaotic systems to verify the effectiveness of the proposed method.      

Stochastic and global real time optimization of Tennessee Eastman challenge problem

A stochastic real time optimization (SRTO) which has an efficient result has been implemented on the Tennessee Eastman (TE) challenging problem. In this article a novel stochastic optimization method, the so-called heuristic random optimization (HRO) proposed by Li & Rhinehart is used which attempts to rationally combine features of both deterministic and random (stochastic) methods. Further, an on-line nonlinear identifier via extended Kalman filter (EKF) is used to supply the plant model for model-based optimization algorithm. Using the information obtained from EKF an on-line HRO is accomplished by a random search method whose search directions and steps are considerably reduced by some heuristic rules. In order to compare and prove the performance of HRO method, the problem was solved again via sequential quadratic programming (SQP) which is the most efficient algorithms among the deterministic methods. The optimizer initiates every 8 h and determines the optimal set points of the PI controllers in the plant. The calculations are completed in about 15 s by HRO method. Simulations have been done using an Intel P4 2.8 GHz, and 256 MB of RAM.     

基于改进最低水平线方法与遗传算法的矩形件排样优化算法

传统的最低水平线方法用于矩形件排样时可能产生较多未被利用的空白区域,造 成不必要的材料浪费。针对此缺陷,在搜索过程中引入启发式判断,实现空白区域的填充处理, 提高板材利用率。在应用遗传算法优化矩形件排样顺序时,在进化过程中采用分阶段设置遗传 算子的方法,改善算法的搜索性能与效果。通过改进最低水平线方法与基于分阶段遗传算子的 遗传算法相结合,共同求解矩形件排样问题。排样测试数据表明,所提出的矩形件排样优化算 法能够有效改善排样效果,提高材料利用率。     

Decremental algorithm for adaptive routing incorporating traveler information

Routing in a stochastic and dynamic (time-dependent) network is a crucial transportation problem. A new variant of adaptive routing, which assumes perfect online information of continuous real-time link travel time, is proposed. Driver's speed profile is taken into consideration to realistically estimate travel times, which also involves the stochasticity of links in a dynamic network. An adaptive approach is suggested to tackle the continuous dynamic shortest path problem. A decremental algorithm is consequently developed to reduce optimization time. The impact of the proposed adaptive routing and the performance of the decremental approach are evaluated in static and dynamic networks under different traffic conditions. The proposed approach can be incorporated into vehicle navigation systems.     

Enhanced process activation method to remove harmonics and input nonlinearity

A new closed-loop process activation method is proposed to reduce the harmonics and obtain more accurate frequency data compared with the previous methods. It has another advantage that the effect of the input nonlinearity can be removed by the proposed method combined with disturbance rejection techniques. The proposed method combines 10 pulses to generate one period of the relay signal. The 10 pulses are combined in an optimal way by solving a constrained nonlinear optimization problem to reduce the harmonics as much as possible. In the implementation step, this study uses the obtained optimal solution without continuing to solve the optimization problem. So, the implementation is almost as simple as that of the previous methods. The proposed method shows significantly smaller harmonics compared with the conventional relay feedback method. The previous approaches, which use a sinusoidal signal, multi-step signal, saturation-relay signal or preload relay signal to reduce the harmonics, cannot manipulate the input nonlinearity. However, the proposed method can estimate the frequency data set of the process effectively for the case where the process has an input nonlinearity because the proposed method uses pulse signals.