非单调信赖域方法求解无约束非光滑优化问题

提出了非单调信赖域算法求解无约束非光滑优化问题,并和经典的信赖域方法作比较分析。同时,设定了一些条件,在这些假设条件下证明了该算法是整体收敛的。数值实验结果表明,非单调策略对无约束非光滑优化问题的求解是行之有效的,拓展了非单调信赖域算法的应用领域。     

解大规模无约束优化的自适应过滤信赖域法

提出一种解大规模无约束优化问题的自适应过滤信赖域法。用目标函数的梯度及迭代点的信息来构造目标函数海赛矩阵的近似数量矩阵,引进了过滤技术和自适应技术,大大提高了计算效率。从理论上证明了新算法的全局收敛性,数值试验结果也表明了新算法的有效性。     

一种求解无约束优化问题的非单调信赖域算法

提出了非单调信赖域算法求解基于锥模型的无约束优化问题,该算法在求解信赖域子问题时充分利用了当前迭代点的一阶梯度信息。提出了一个新的信赖域半径的选取机制,并和经典的信赖域方法作比较分析。设定了一些条件,在这些假设条件下证明了算法是整体收敛的。数值实验结果表明,该算法对基于锥模型的无约束优化问题的求解是行之有效的,拓展了非单调信赖域算法的应用领域。     

A scheme for determining stepsizes for unconstrained optimization methods

We present a new scheme for determining stepsizes for iterative unconstrained minimization methods. This scheme provides a stepsize estimate for the efficient Armijo-type stepsize determination rule and improves its performance. As examples for the new scheme, we also present a new gradient algorithm and a new conjugate gradient algorithm. These two algorithms are readily implementable and eventually demand only one trial stepsize at each iteration. Their global convergence is established without any convexity assumptions. The convergence ratio associated with the gradient algorithm is shown to converge to the canonical convergence ratio (that is, the best possible convergence ratio). The convergence rate of the conjugate gradient algorithm is n-step superlinear and n-step quadratic.     

关于楔形信赖域半径更新的两种方法

楔形信赖域算法用于求解无导数的优化问题,是基于传统的信赖域算法提出的。楔形信赖域算法的改进之处是在传统的信赖域子问题的基础上增加一个楔形约束,故称为“楔形信赖域”。信赖域半径的更新方法对于算法的收敛性有重要的影响。针对原楔形信赖域的半径更新方法的不足,提出了两种新的更新半径的策略。实验结果表明,在大多数测试问题上,改进的这两种算法的函数值计算次数大大减少。     

A class of nonmonotone stabilization trust region methods

A class of trust region methods in unconstrained optimization is presented, by adopting a nonmonotone stabilization strategy. Under some regularity conditions, the convergence properties of these methods are discussed. Extensive numerical results which are reported show that these methods are very efficient.     

一种求解约束优化问题的信赖域微粒群算法

提出了一种改进型信赖域微粒群算法来求解带有不等式约束优化问题。粒子群每一次进化后,对所有粒子执行信赖域搜索,寻找更优个体,从而增加了微粒群算法的局部搜索能力。把算法应用于供应商补货优化,实验结果表明,该方案能够有效地减少供应商的补货成本,具有较好的应用价值。     

Multi-fidelity wing aerostructural optimization using a trust region filter-SQP algorithm

A trust region filter-SQP method is used for wing multi-fidelity aerostructural optimization. Filter method eliminates the need for a penalty function, and subsequently a penalty parameter. Besides, it can easily be modified to be used for multi-fidelity optimization. A low fidelity aerostructural analysis tool is presented, that computes the drag, weight and structural deformation of lifting surfaces as well as their sensitivities with respect to the design variables using analytical methods. That tool is used for a mono-fidelity wing aerostructral optimization using a trust region filter-SQP method. In addition to that, a multi-fidelity aerostructural optimization has been performed, using a higher fidelity CFD code to calibrate the results of the lower fidelity model. In that case, the lower fidelity tool is used to compute the objective function, constraints and their derivatives to construct the quadratic programming subproblem. The high fidelity model is used to compute the objective function and the constraints used to generate the filter. The results of the high fidelity analysis are also used to calibrate the results of the lower fidelity tool during the optimization. This method is applied to optimize the wing of an A320 like aircraft for minimum fuel burn. The results showed about 9 % reduction in the aircraft mission fuel burn.     

Descent three-term conjugate gradient methods based on secant conditions for unconstrained optimization

The conjugate gradient method is an effective method for large-scale unconstrained optimization problems. Recent research has proposed conjugate gradient methods based on secant conditions to establish fast convergence of the methods. However, these methods do not always generate a descent search direction. In contrast, Y. Narushima, H. Yabe, and J.A. Ford [A three-term conjugate gradient method with sufficient descent property for unconstrained optimization, SIAM J. Optim. 21 (2011), pp. 212–230] proposed a three-term conjugate gradient method which always satisfies the sufficient descent condition. This paper makes use of both ideas to propose descent three-term conjugate gradient methods based on particular secant conditions, and then shows their global convergence properties. Finally, numerical results are given.     

求解二维Fredholm积分方程的参数化信赖域方法

给出了求解二维第一类Fredholm积分方程信赖域方法。通过引入正则化参数将离散后的Fredholm积分方程转化带参数的最优化问题,借助于KKT条件将二次信赖域子问题参数化,并进行分析求解,最后给出了数值模拟。     

Two modified spectral conjugate gradient methods and their global convergence for unconstrained optimization

In this paper, two modified spectral conjugate gradient methods which satisfy sufficient descent property are developed for unconstrained optimization problems. For uniformly convex problems, the first modified spectral type of conjugate gradient algorithm is proposed under the Wolfe line search rule. Moreover, the search direction of the modified spectral conjugate gradient method is sufficiently descent for uniformly convex functions. Furthermore, according to the Dai–Liao's conjugate condition, the second spectral type of conjugate gradient algorithm can generate some sufficient decent direction at each iteration for general functions. Therefore, the second method could be considered as a modification version of the Dai–Liao's algorithm. Under the suitable conditions, the proposed algorithms are globally convergent for uniformly convex functions and general functions. The numerical results show that the approaches presented in this paper are feasible and efficient.     

Deep reinforcement learning methods for structure-guided processing path optimization

Journal of Intelligent Manufacturing - A major goal of materials design is to find material structures with desired properties and in a second step to find a processing path to reach one of these...     

Data error analysis in unconstrained optimization problems with the CESTAC method

The CESTAC method—also known as the Permutation-Perturbation method—was first conceived to analyse the propagation of the round-off error when a numerical algorithm is run on a computer. Moreover, for iterative optimization methods it leads to the definition of an optimal termination criterion. In this paper we show how the same mecanisms can be used when data errors interfere with unconstrained optimization problems, and what the interests of this method are compared to the most frequently used ones: the crude Monte Carlo method and the linearisation of the optimization function.     

A modified trust region algorithm for nonlinear equations with new updating rule of trust region radius

In this article, we present a trust region algorithm for the nonlinear equations with a new updating rule of the trust region radius, which takes some function of the residual. We show that under the local error bound condition which is weaker than the non-singularity, the new algorithm converges quadratically to some solution of the nonlinear equations. Numerical results show that the new algorithm performs very well for some singular nonlinear equations.     

Explosion gravitation field algorithm with dust sampling for unconstrained optimization

Gravitation Field Algorithm (GFA) is a novel optimization algorithm derived from the Solar Nebular Disk Model (SNDM) in astronomy and inspired by the formation process of planets. Although it has achieved good performance when solving many unconstrained optimization problems, which demonstrated its promising application potential in many real-world problems, GFA still has much room for improvement, especially when it comes to the accuracy and efficiency of the algorithm.In this research, an improved GFA algorithm called Explosion Gravitation Field Algorithm (EGFA) is proposed for unconstrained optimization problems, with the introduction of two strategies: Dust Sampling (DS) and Explosion Operation. The task of DS is to locate the space that contains the optimal solution(s) by initializing the dust population randomly in the search space; while the Explosion Operator is to improve the accuracy of solutions and decrease the probability of the algorithm falling into local optima by generating the new population around the center dust to replace the original population.A comparison of experimental results on six classical unconstrained benchmark problems with different dimensions demonstrates that the proposed EGFA outperforms the original GFA and several classical metaheuristic optimization algorithms, such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), in terms of accuracy and efficiency in lower dimensions. Additionally, the comparison of results on three real datasets indicate that EGFA performs better than the original GFA and k-means for solving clustering problems.     

Convergence of trust region augmented Lagrangian methods using variable fidelity approximation data

To date the primary focus of most constrained approximate optimization strategies is that application of the method should lead to improved designs. Few researchers have focused on the development of constrained approximate optimization strategies that are assured of converging to a Karush-Kuhn-Tucker (KKT) point for the problem. Recent work by the authors based on a trust region model management strategy has shown promise in managing the convergence of constrained approximate optimization in application to a suite of single level optimization test problems. Using a trust-region model management strategy, coupled with an augmented Lagrangian approach for constrained approximate optimization, the authors have shown in application studies that the approximate optimization process converges to a KKT point for the problem. The approximate optimization strategy sequentially builds a cumulative response surface approximation of the augmented Lagrangian which is then optimized subject to a trust region constraint. In this research the authors develop a formal proof of convergence for the response surface approximation based optimization algorithm. Previous application studies were conducted on single level optimization problems for which response surface approximations were developed using conventional statistical response sampling techniques such as central composite design to query a high fidelity model over the design space. In this research the authors extend the scope of application studies to include the class of multidisciplinary design optimization (MDO) test problems. More importantly the authors show that response surface approximations constructed from variable fidelity data generated during concurrent subspace optimization (CSSOs) can be effectively managed by the trust region model management strategy. Results for two multidisciplinary test problems are presented in which convergence to a KKT point is observed. The formal proof of convergence and the successful MDO application of the algorithm using variable fidelity data generated by CSSO are original contributions to the growing body of research in MDO.Nomenclature k Lagrangian iteration - s approximate minimization iteration - i, j, l variable indices - m number of inequality constraints - n number of design variables - p number of equality constraints - f(x) objective function - g(x) inequality constraint vector - g _j (x) j-th inequality constraint - h(x) equality constraint vector - h _j (x) i-th equality constraint - c(x) generalized constraint vector - c _i (x) i-th generalized constraint - c ₁,c ₂,c ₃,c ₄ real constants - m(x) approximate model - q(x) approximate model - q(x) piecewise approximation - r _p penalty parameter - t, t ₁,t ₂ step size length - x design vector, dimensionn - x _l l-th design variable - x ^U upper bound vector, dimensionn - x _l ^U l-th design upper bound - x ^L lower bound vector, dimensionn - x _l ^L l-th design lower bound - B approximation of the Hessian - K constraints residual - S design space - , ₁, ₂, scalars - ₁, ₂ convergence tolerances - ₀, ₁, ₂, , trust region parameters - Lagrange multiplier vector, dimensionm+p - _i i-th Lagrange multiplier - trust region ratio - (x) alternative form for inequality constraints - (x, ,r _p ) augmented Lagrangian function - approximation of the augmented Lagrangian function - fidelity control - . Euclidean norm - , inner product - gradient operator with respect to design vector x - P(y(x)) projection operator; projects the vector y onto the set of feasible directions at x - trust region radius - x step size     

Modeling and analysis of trust management with trust chain optimization in mobile ad hoc networks

We develop and analyze a trust management protocol for mission-driven group communication systems in mobile ad hoc networks using hierarchical modeling techniques based on stochastic Petri nets. Trust among mobile nodes is crucial for team collaborations with new coalition partners without prior interactions for mission-driven group communication systems in battlefield situations. In addition, ensuring a certain level of trust is also critical for successful mission completion. Our work seeks to identify the optimal length of a trust chain among peers in a trust web that generates the most accurate trust levels without revealing risk based on a tradeoff between trust availability and path reliability over trust space. We define a trust metric for mission-driven group communication systems in mobile ad hoc networks to properly reflect unique characteristics of trust concepts and demonstrate that an optimal trust chain length exists for generating the most accurate trust levels for trust-based collaboration among peers in mobile ad hoc networks while meeting trust availability and path reliability requirements.     

A heuristic iterated-subspace minimization method with pattern search for unconstrained optimization

Recently, an increasing attention was paid on different procedures for an unconstrained optimization problem when the information of the first derivatives is unavailable or unreliable. In this paper, we consider a heuristic iterated-subspace minimization method with pattern search for solving such unconstrained optimization problems. The proposed method is designed to reduce the total number of function evaluations for the implementation of high-dimensional problems. Meanwhile, it keeps the advantages of general pattern search algorithm, i.e., the information of the derivatives is not needed. At each major iteration of such a method, a low-dimensional manifold, the iterated subspace, is constructed. And an approximate minimizer of the objective function in this manifold is then determined by a pattern search method. Numerical results on some classic test examples are given to show the efficiency of the proposed method in comparison with a conventional pattern search method and a derivative-free method.     

Dynamic analysis of box girders with tee-stiffening using unconstrained optimization techniques

The paper presents a numerical procedure for dynamic analysis of box girders with tee-stiffeners utilizing unconstrained optimization techniques. Unlike the finite element or finite strip methods, the procedure does not require discretization to the whole structure, thus resulting in great savings in computational time. The potential and kinetic energy of the assembled structure is expressed in terms generalized functions that describe the longitudinal and transverse displacement profiles. The problem is then converted into uunconstrained optimization problem to determine the magnitude of the lowest natural frequency and the associated mode shape. Results are presented showing the sensitivity the natural frequency to the stiffener depth (d) and the flange width (b). It is shown that the number of longitudinal and transverse stiffeners largely influence the magnitude of the natural frequency (λ) of the box girder. Design guidelines are also provided to optimize the dynamic response of the structure. The procedure is very practical and can be utilized in the industry for the analysis of box girders.