Constrained optimization using interval analysis

An interval analysis method is described for finding the global maximum of a multimodal multivariable function subject to equality and/or inequality constraints. By discarding subregions where the global solution can not exist and applying the interval Newton method to solve the Lagrange equation, one can always find the solution with the rigorous error bound. Some numerical examples are given.     

Constrained particle swarm optimization using a bi-objective formulation

This paper introduces an approach for dealing with constraints when using particle swarm optimization. The constrained, single objective optimization problem is converted into an unconstrained, bi-objective optimization problem that is solved using a multi-objective implementation of the particle swarm optimization algorithm. A specialized bi-objective particle swarm optimization algorithm is presented and an engineering example problem is used to illustrate the performance of the algorithm. An additional set of 13 test problems from the literature is used to further validate the performance of the newly proposed algorithm. For the example problems considered here, the proposed algorithm produced promising results, indicating that it is an approach that deserves further consideration. The newly proposed algorithm provides performance similar to that of a tuned penalty function approach, without having to tune any penalty parameters.     

Financial model calibration using consistency hints

We introduce a technique for forcing the calibration of a financial model to produce valid parameters. The technique is based on learning from hints. It converts simple curve fitting into genuine calibration, where broad conclusions can be inferred from parameter values. The technique augments the error function of curve fitting with consistency hint error functions based on the Kullback-Leibler distance. We introduce an efficient EM-type optimization algorithm tailored to this technique. We also introduce other consistency hints, and balance their weights using canonical errors. We calibrate the correlated multifactor Vasicek model of interest rates, and apply it successfully to Japanese Yen swaps market and US dollar yield market.     

Constrained balanced optimization problems

We consider the balanced optimization problem with an additional linear constraint under a general combinatorial optimization setting. It is shown that this Constrained Balanced Optimization Problem (CBOP) can be solved in polynomial time whenever an associated minsum problem can be solved in polynomial time. A modification of this basic algorithm is also suggested with improved average performance. This modified algorithm is applicable to the unconstrained version also and has better average performance than existing algorithms. Computational results are presented which establish the superiority of the modified algorithm on both, constrained and unconstrained models. Some variants of CBOP are also discussed in brief.     

Robot calibration using the CPC error model

The complete and parametrically continuous (CPC) robot kinematic modeling convention has no model singularities and allows the modeling of the robot base and tool in the same manner by which the internal links are modeled. These two properties can be utilized to construct robot kinematic error models employing the minimum number of kinematic error parameters. These error parameters are independent and span the entire geometric error space. The BASE and TOOL error models are derived as special cases of the regular CPC error model. The CPC error model is useful for both kinematic identification and kinematic compensation. This paper focuses on the derivation of the CPC error models and their use in the experimental implementation of robot calibration.     

Constrained mesh optimization on boundary

This paper presents a new mesh optimization approach aiming to improve the mesh quality on the boundary. The existing mesh untangling and smoothing algorithms (Vachal et al. in J Comput Phys 196: 627–644, 2004; Knupp in J Numer Methods Eng 48: 1165–1185, 2002), which have been proved to work well to interior mesh optimization, are enhanced by adding constrains of surface and curve shape functions that approximate the boundary geometry from the finite element mesh. The enhanced constrained optimization guarantees that the boundary nodes to be optimized always move on the approximated boundary. A dual-grid hexahedral meshing method is used to generate sample meshes for testing the proposed mesh optimization approach. As complementary treatments to the mesh optimization, appropriate mesh topology modifications, including buffering element insertion and local mesh refinement, are performed in order to eliminate concave and distorted elements on the boundary. Finally, the optimization results of some examples are given to demonstrate the effectivity of the proposed approach.     

Error-model-based robot calibration using a modified CPC model

Selection of a proper robot kinematic model is a critical step in error-model-based robot calibration. The Denavit-Hartenberg (DH) model exhibits singularities in calibration of robots having consecutive parallel joint axes. The complete and parametrically continuous (CPC) modeling technique is one of the more versatile alternative modeling conventions designated to fit the needs of manipulator calibration. No modeling convention is, however, perfect. One “user-unfriendly” aspect of the CPC model is a condition handling technique needed, when constructing the error model, to avoid model singularities due to the adoption of the direction vectors of the joint axes as link parameters.This paper presents a modification to the CPC model which brings the model closer to the DH model. Rather than using the direction vectors of joint axes, the modified CPC (MCPC) model employs angular parameters to acommodate the required rotations for each link transformation. This modification results in a much simplified error model. The model, like the CPC model, is capable of completely describing the geometry and motion of the manipulator in a reference coordinate frame. Its error model possesses a minimum number of parameters to span the entire geometric error space and it can be made singularity-free by proper selection of the tool axis. This paper presents a calibration study of the PUMA robot using the MCPC model. A moving stereo camera system was employed for end-effector pose measurements. The MCPC error model was then used for kinematic identification. Results on the PUMA arm show that the MCPC performs well for robot calibration. The well-defined structure and user friendliness of the MCPC model may facilitate the implementation of robot calibration techniques on the factory floor.     

Constrained optimization of nacelle shapes in Euler flow using semianalytical sensitivity analysis

The problem of aerodynamic shape optimization in Euler flow is addressed. B-splines are used for parametrization of the shape. Cheap gradient calculation is obtained via sensitivity analysis and the solution of an adjoint equation; pseudo secondorder spatial accuracy is achieved by means of a semianalytical formulation. As a validation of the approach, several inverse and constrained optimization test problems are presented with emphasis on civil engine nacelle design. The handling of nondifferentiable quantities (such as maxima) in cost functions is allowed for via the use of the Kreisselmeier-Steinhauser function.     

Operation allocation in automated manufacturing system using GA-based approach with multifidelity models

The scheduling problem in manufacturing is considered as among the toughest to solve. The basic drawback of many proposed methods has been the huge amount of computation time for simulation. This paper proposes a framework to solve the operation allocation problem in automated manufacturing systems using the concept of multifidelity. The concept of multifidelity has been proposed by several researchers in order to reduce the computation time for simulation. In this paper, a GA-based heuristic procedure will be developed along with the multifidelity approach to solve a typical manufacturing scheduling problem. Four different fidelity models have been defined on which experimentation is carried out. The proposed method has been tested on a sample dataset and the results have been analysed to choose the fidelity model which best describes the scenario.     

Constrained global optimization for wine blending

Assemblage consists in blending base wines in order to create target wines. Recent developments in aroma analysis allow us to measure chemical compounds impacting the taste of wines. This chemical analysis makes it possible to design a decision tool for the following problem: given a set of target wines, determine which volumes must be extracted from each base wine to produce wines that satisfy constraints on aroma concentration, volumes, alcohol contents and price. This paper describes the modeling of wine assemblage as a mixed constrained optimization problem, where the main goal is to minimize the gap to the desired concentrations for every aromatic criterion. The deterministic branch and bound solvers Couenne and IbexOpt behave well on the wine blending problem thanks to their interval constraint propagation/programming and polyhedral relaxation methods. We also study the performance of other optimization goals that could be embedded in a configuration tool, where the different possible interactions amount to solving the same constraints with different objective functions. We finally show on a recent generic wine blending instance that the proposed optimization process scales up well with the number of base wines.     

基于凸优化的自适应CV模型*

针对CV模型分割精度不高、分割速度缓慢和易陷于局部最优等缺点,提出了一种新的基于凸优化的自适应CV模型。首先,引入了自适应权重项,对拟合中心的计算采用加权平均,提高了拟合中心计算的准确性;然后,在模型中加入了凸优化技术,以获取模型的全局最优解;最后,采用了Split Bregman方法进行快速求解,有效地提高了分割效率。实验结果表明,基于凸优化的自适应CV模型有效地提高了分割精度和效率,对初始化也具有较好的鲁棒性。     

Constrained clustering as an optimization method

A deterministic annealing approach to clustering is derived on the basis of the principle of maximum entropy. This approach is independent of the initial state and produces natural hierarchical clustering solutions by going through a sequence of phase transitions. It is modified for a larger class of optimization problems by adding constraints to the free energy. The concept of constrained clustering is explained, and three examples are are given in which it is used to introduce deterministic annealing. The previous clustering method is improved by adding cluster mass variables and a total mass constraint. The traveling salesman problem is reformulated as constrained clustering, yielding the elastic net (EN) approach to the problem. More insight is gained by identifying a second Lagrange multiplier that is related to the tour length and can also be used to control the annealing process. The open path constraint formulation is shown to relate to dimensionality reduction by self-organization in unsupervised learning. A similar annealing procedure is applicable in this case as well     

Constrained optimization with normed moving truncations penalty-functions

The normed moving truncations penalty-function method differs from the common exterior moving truncations penaltyfunction method in that the problem functions are normalized after every completed unconstrained optimization, by multiplying with the reciprocal values attained by the functions. Computational results on 12 problems of varying complexity are reported, and compared with the results achieved by two other penalty-function methods.     

基于协同进化的约束多目标优化算法

针对约束多目标优化算法存在难以有效地兼顾收敛性和多样性的问题,提出一种基于协同进化的约束多目标优化算法.第一阶段,通过基于稳态演化的可行解搜索方式得到一个具有一定数量可行解的种群;第二阶段,将这个种群拆分为两个子种群,并通过双子种群协同进化的方式实现对收敛性和多样性的兼顾;最后采用标准约束多目标优化问题CF1～CF7、...     

An automated binary change detection model using a calibration approach

An automated binary change detection model using a threshold-based calibration approach was introduced in the study. The burdensome processes required in binary change detection, including calibration, calculation of accuracy, extraction of optimum threshold(s), generation of a binary change mask, and removal of “salt-and-pepper” noise were integrated and automated in the model. For practical purpose, the model was implemented as a dynamic linked library in ESRI ArcMap 9.1 using Visual Basic. This study demonstrated the model with a variety of single and multiple variables (layers) extracted from multiple-date QuickBird imagery for three study sites in Las Vegas, NV and two study sites in Tucson, AZ. The use of multiple variables in binary change detection resulted in significantly better performance than single variables.     

基于灰色预测模型的跨层切换优化

随着移动互联网的飞速发展,无线通信一直在寻求一种更好的支持无缝切换的机制。首先使用改进的指数加权移动平均算法对接收信号强度进行平滑处理,然后提出了一种在移动WIMAX环境中基于运动预测的快速跨层切换机制。使用灰色预测模型可以预测移动用户的接收信号强度。在预测模型的帮助下,3层切换可以提前2层切换触发,因此总的切换时延可以降低。     

Constrained space deformation techniques for design optimization

We present a novel shape deformation method for its use in design optimization tasks. Our space deformation technique based on moving least squares approximation improves upon existing approaches in crucial aspects: It offers the same level of modeling flexibility as surface-based deformations, but it is independent of the underlying geometry representation and therefore highly robust against defects in the input data. It overcomes the scalability limitations of existing space deformation techniques based on globally supported radial basis functions while providing the same high level of deformation quality. Finally, unlike existing space deformation approaches, our technique directly incorporates geometric constraints–such as preservation of critical feature lines, circular couplings, planar or cylindrical construction parts–into the deformation, thereby fostering the exploration of more favorable and producible shape variations during the design optimization process.     

采用dsDNA-MC优化的非线性系统预测控制

将基于DNA双链结构的膜计算优化方法（dsDNA-MC）用于输入受限的非线性预测控制器设计,提出了基于dsDNA-MC优化的非线性系统预测控制算法。在对单输入单输出非线性系统预测控制分析的基础上,将非线性系统预测控制问题归结为具有输入约束的非线性系统优化问题,并采用dsDNA-MC算法来求解这一问题。仿真结果表明该算法可行、有效。     

Constrained latent variable model predictive control for trajectory tracking and economic optimization in batch processes

A constrained latent variable model predictive control (LV-MPC) technique is proposed for trajectory tracking and economic optimization in batch processes. The controller allows the incorporation of constraints on the process variables and is designed on the basis of multi-way principal component analysis (MPCA) of a batch data array rearranged by means of a regularized batch-wise unfolding. The main advantages of LV-MPC over other MPC techniques are: (i) requirements for the dataset are rather modest (only around 10–20 batch runs are necessary), (ii) nonlinear processes can efficiently be handled algebraically through MPCA models, and (iii) the tuning procedure is simple. The LV-MPC for tracking is tested through a benchmark process used in previous LV-MPC formulations. The extension to economic LV-MPC includes an economic cost and it is based on model and trajectory updating from batch to batch to drive the process to the economic optimal region. A data-driven model validity indicator is used to ensure the prediction’s validity while the economic cost drives the process to regions with higher profit. This technique is validated through simulations in a case study.