Optimization in engineering design

This paper presents a method for solving a class of constrained optimization problems in finite-dimensional spaces. This type of problem usually arises in connection with parameter optimization in engineering design. Most often, these problems consist of incorporating upper and/or lower bounds on the variables in otherwise unconstrained optimization problems. The proposed method replaces the original optimization problem withm constraints (m>1) by a sequence of optimization problems with one constraint only. The theory behind this method is discussed in the subsequent sections.This research was supported by NSF Grant No. GK-4984.     

An interval branch and bound method for global Robust optimization

In this paper, we design a Branch and Bound algorithm based on interval arithmetic to address nonconvex robust optimization problems. This algorithm provides the exact global solution of such difficult problems arising in many real life applications. A code was developed in MatLab and was used to solve some robust nonconvex problems with few variables. This first numerical study shows the interest of this approach providing the global solution of such difficult robust nonconvex optimization problems.

     

无线通信系统设计中的两个优化问题和相关优化方法

无线通信系统设计中的许多问题可建模为优化问题.一方面,这些优化问题常常具有高度的非线性性,一般情况下难于求解;另一方面,它们又有自身的特殊结构,例如隐含的凸性、可分性等.利用优化的方法结合问题的特殊结构求解和处理无线通信系统设计问题是近年来学术界研究的热点.本文重点讨论无线通信系统设计中的两个优化问题和相关优化方法,包括多用户干扰信道最大最小准则下的联合传输/接收波束成形设计和多输入多输出(Multi-Input Multi-Output,MIMO)检测问题,主要介绍现代优化技术结合问题的特殊结构在求解和处理上述两个问题的最新进展.     

Generalized B-Well-Posedness for Set Optimization Problems

This paper aims at studying the generalized well-posedness in the sense of Bednarczuk for set optimization problems with set-valued maps. Three kinds of B-well-posedness for set optimization problems are introduced. Some relations among the three kinds of B-well-posedness are established. Necessary and sufficient conditions of well-posedness for set optimization problems are obtained.     

Pointwise well-posedness in set optimization with cone proper sets

This paper deals with the well-posedness property in the setting of set optimization problems. By using a notion of well-posed set optimization problem due to Zhang et al. (2009) [18] and a scalarization process, we characterize this property through the well-posedness, in the Tykhonov sense, of a family of scalar optimization problems and we show that certain quasiconvex set optimization problems are well-posed. Our approach is based just on a weak boundedness assumption, called cone properness, that is unavoidable to obtain a meaningful set optimization problem.     

Augmented Lagrangian Homotopy Method for the Regularization of Total Variation Denoising Problems

This paper presents a homotopy procedure which improves the solvability of mathematical programming problems arising from total variational methods for image denoising. The homotopy on the regularization parameter involves solving a sequence of equality-constrained optimization problems where the positive regularization parameter in each optimization problem is initially large and is reduced to zero. Newton’s method is used to solve the optimization problems and numerical results are presented.     

Existence of a Saddle Point in Nonconvex Constrained Optimization

The existence of a saddle point in nonconvex constrained optimization problems is considered in this paper. We show that, under some mild conditions, the existence of a saddle point can be ensured in an equivalent p-th power formulation for a general class of nonconvex constrained optimization problems. This result expands considerably the class of optimization problems where a saddle point exists and thus enlarges the family of nonconvex problems that can be solved by dual-search methods.     

Set covering-based surrogate approach for solving sup-{\mathcal{T}} equation constrained optimization problems

This work considers solving the sup-T{\mathcal{T}} equation constrained optimization problems from the integer programming viewpoint. A set covering-based surrogate approach is proposed to solve the sup-T{\mathcal{T}} equation constrained optimization problem with a separable and monotone objective function in each of the variables. This is our first trial of developing integer programming-based techniques to solve sup-T{\mathcal{T}} equation constrained optimization problems. Our computational results confirm the efficiency of the proposed method and show its potential for solving large scale sup-T{\mathcal{T}} equation constrained optimization problems.     

Composite Nonsmooth Optimization Using Approximate Generalized Gradient Vectors

A new approximation method is presented for directly minimizing a composite nonsmooth function that is locally Lipschitzian. This method approximates only the generalized gradient vector, enabling us to use directly well-developed smooth optimization algorithms for solving composite nonsmooth optimization problems. This generalized gradient vector is approximated on each design variable coordinate by using only the active components of the subgradient vectors; then, its usability is validated numerically by the Pareto optimum concept. In order to show the performance of the proposed method, we solve four academic composite nonsmooth optimization problems and two dynamic response optimization problems with multicriteria. Specifically, the optimization results of the two dynamic response optimization problems are compared with those obtained by three typical multicriteria optimization strategies such as the weighting method, distance method, and min–max method, which introduces an artificial design variable in order to replace the max-value cost function with additional inequality constraints. The comparisons show that the proposed approximation method gives more accurate and efficient results than the other methods.     

Global optimization by continuous grasp

We introduce a novel global optimization method called Continuous GRASP (C-GRASP) which extends Feo and Resende’s greedy randomized adaptive search procedure (GRASP) from the domain of discrete optimization to that of continuous global optimization. This stochastic local search method is simple to implement, is widely applicable, and does not make use of derivative information, thus making it a well-suited approach for solving global optimization problems. We illustrate the effectiveness of the procedure on a set of standard test problems as well as two hard global optimization problems.     

On Approximate Solutions in Vector Optimization Problems Via Scalarization

This work deals with approximate solutions in vector optimization problems. These solutions frequently appear when an iterative algorithm is used to solve a vector optimization problem. We consider a concept of approximate efficiency introduced by Kutateladze and widely used in the literature to study this kind of solutions. Necessary and sufficient conditions for Kutateladze’s approximate solutions are given through scalarization, in such a way that these points are approximate solutions for a scalar optimization problem. Necessary conditions are obtained by using gauge functionals while monotone functionals are considered to attain sufficient conditions. Two properties are then introduced to describe the idea of parametric representation of the approximate efficient set. Finally, through scalarization, characterizations and parametric representations for the set of approximate solutions in convex and nonconvex vector optimization problems are proved and the obtained results are applied to Pareto problems. AMS Classification:90C29, 49M37 This research was partially supported by Ministerio de Ciencia y Tecnología (Spain), project BFM2003-02194.     

A note on the optimization of constrained design problems

This paper describes a new algorithm for solving constrained optimization problems, based on a method proposed by Chattopadhyay. The proposed algorithm replaces the original problem withm constraints,m>1, by a sequence of optimization problems, with one constraint. Here, we modify the algorithm given by Chattopadhyay in order to make it applicable for a larger class of optimization problems and to improve its convergence characteristics.     

A branch-and-reduce approach to global optimization

This paper presents valid inequalities and range contraction techniques that can be used to reduce the size of the search space of global optimization problems. To demonstrate the algorithmic usefulness of these techniques, we incorporate them within the branch-and-bound framework. This results in a branch-and-reduce global optimization algorithm. A detailed discussion of the algorithm components and theoretical properties are provided. Specialized algorithms for polynomial and multiplicative programs are developed. Extensive computational results are presented for engineering design problems, standard global optimization test problems, univariate polynomial programs, linear multiplicative programs, mixed-integer nonlinear programs and concave quadratic programs. For the problems solved, the computer implementation of the proposed algorithm provides very accurate solutions in modest computational time.     

基于群智能算法的预防性维修周期优化

分析将蚁群优化算法应用于预防性维修周期工程寻优问题时遇到的算法参数选择困难等问题,提出将粒子群优化算法和空间划分方法引入该过程以改进原蚁群算法的寻优规则和历程.建立混合粒子群和蚁群算法的群智能优化策略:PS_ACO(Particle Swarm and Ant Colony Optimization),并将其应用于混联系统预防性维修周期优化过程中,以解决由于蚁群算法中参数选择不当和随机产生维修周期解值带来的求解精度差、寻优效率低等问题.算法的寻优结果对比分析表明:该PS_ACO算法应用于预防性维修周期优化问题,在寻优效率及寻优精度上有部分改进,且可相对削弱算法参数选择对优化结果的影响.     

A hierarchy machine: Learning to optimize from nature and humans

This article proposes a competent hierarchical optimization method called the hierarchical Bayesian optimization algorithm (hBOA). hBOA extends the Bayesian optimization algorithm (BOA) by incorporating three important features for robust and scalable optimization of hierarchical problems: proper decomposition, chunking, and preservation of alternative solutions. Additionally, the article proposes a class of difficult hierarchical problems called hierarchical traps. hBOA is shown to provide a scalable solution to the class of hierarchically decomposable problems and anything easier. Specifically, hBOA can solve hierarchical traps and other nearly decomposable problems in approximately O(n^1.55 log n) to O(n²) function evaluations, where n is the number of decision variables in the problem. © 2003 Wiley Periodicals, Inc.     

SO-I: a surrogate model algorithm for expensive nonlinear integer programming problems including global optimization applications

This paper presents the surrogate model based algorithm SO-I for solving purely integer optimization problems that have computationally expensive black-box objective functions and that may have computationally expensive constraints. The algorithm was developed for solving global optimization problems, meaning that the relaxed optimization problems have many local optima. However, the method is also shown to perform well on many local optimization problems, and problems with linear objective functions. The performance of SO-I, a genetic algorithm, Nonsmooth Optimization by Mesh Adaptive Direct Search (NOMAD), SO-MI (Müller et al. in Comput Oper Res 40(5):1383–1400, 2013), variable neighborhood search, and a version of SO-I that only uses a local search has been compared on 17 test problems from the literature, and on eight realizations of two application problems. One application problem relates to hydropower generation, and the other one to throughput maximization. The numerical results show that SO-I finds good solutions most efficiently. Moreover, as opposed to SO-MI, SO-I is able to find feasible points by employing a first optimization phase that aims at minimizing a constraint violation function. A feasible user-supplied point is not necessary.     

Robust univariate spline models for interpolating interval data

We consider spline interpolation problems where information about the approximated function is given by means of interval estimates for the function values over ranges of x-values instead of specific knots. We propose two robust univariate spline models formulated as convex semi-infinite optimization problems. We present simplified equivalent formulations of both models as finite explicit convex optimization problems for splines of degrees up to 3. This makes it possible to use existing convex optimization algorithms and software.     

A novel elitist multiobjective optimization algorithm: Multiobjective extremal optimization

Recently, a general-purpose local-search heuristic method called extremal optimization (EO) has been successfully applied to some NP-hard combinatorial optimization problems. This paper presents an investigation on EO with its application in numerical multiobjective optimization and proposes a new novel elitist (1 + λ) multiobjective algorithm, called multiobjective extremal optimization (MOEO). In order to extend EO to solve the multiobjective optimization problems, the Pareto dominance strategy is introduced to the fitness assignment of the proposed approach. We also present a new hybrid mutation operator that enhances the exploratory capabilities of our algorithm. The proposed approach is validated using five popular benchmark functions. The simulation results indicate that the proposed approach is highly competitive with the state-of-the-art multiobjective evolutionary algorithms. Thus MOEO can be considered a good alternative to solve numerical multiobjective optimization problems.     

张量分析和多项式优化的若干进展

张量分析 (也称多重数值线性代数) 主要包括张量分解和张量特征值的理论和算法,多项式优化主要包括目标和约束均为多项式的一类优化问题的理论和算法. 主要介绍这两个研究领域中若干新的研究结果. 对张量分析部分,主要介绍非负张量H-特征值谱半径的一些性质及求解方法,还介绍非负张量最大 (小) Z-特征值的优化表示及其解法;对多项式优化部分,主要介绍带单位球约束或离散二分单位取值、目标函数为齐次多项式的优化问题及其推广形式的多项式优化问题和半定松弛解法. 最后对所介绍领域的发展趋势做了预测和展望.