Matrix-free aerostructural optimization of aircraft wings

In structural optimization subject to failure constraints, computing the gradients of a large number of functions with respect to a large number of design variables may not be computationally practical. Often, the number of constraints in these optimization problems is reduced using constraint aggregation at the expense of a higher mass of the optimal structural design. This work presents results of structural and coupled aerodynamic and structural design optimization of aircraft wings using a novel matrix-free augmented Lagrangian optimizer. By using a matrix-free optimizer, the computation of the full constraint Jacobian at each iteration is replaced by the computation of a small number of Jacobian-vector products. The low cost of the Jacobian-vector products allows optimization problems with thousands of failure constraints to be solved directly, mitigating the effects of constraint aggregation. The results indicate that the matrix-free optimizer reduces the computational work of solving the optimization problem by an order of magnitude compared to a traditional sequential quadratic programming optimizer. Furthermore, the use of a matrix-free optimizer makes the solution of large multidisciplinary design problems, in which gradient information must be obtained through iterative methods, computationally tractable.     

Sequential optimization and fuzzy reliability analysis for multidisciplinary systems

Structural and Multidisciplinary Optimization - To meet the rising demand for high reliability in complex multidisciplinary engineering systems, more attention has been paid to reliability-based...     

Sequential optimization and reliability assessment for multidisciplinary systems design

With higher reliability and safety requirements, reliability-based design has been increasingly applied in multidisciplinary design optimization (MDO). A direct integration of reliability-based design and MDO may present tremendous implementation and numerical difficulties. In this work, a methodology of sequential optimization and reliability assessment for MDO is proposed to improve the efficiency of reliability-based MDO. The central idea is to decouple the reliability analysis from MDO with sequential cycles of reliability analysis and deterministic MDO. The reliability analysis is based on the first-order reliability method (FORM). In the proposed method, the reliability analysis and the deterministic MDO use two MDO strategies, the multidisciplinary feasible approach and the individual disciplinary feasible approach. The effectiveness of the proposed method is illustrated with two example problems.     

An optimization algorithm for production systems

As the scale of rule-based expert systems increases, the efficiency of production systems becomes a pressing concern. Recently developed production systems thus enable users to specify an appropriate ordering or clustering of join operations. Various efficiency heuristics have been introduced to optimize production rules manually. However, since the heuristics often conflict With each other, users have to proceed by trial and error. The problem addressed in this paper is how to automatically determine efficient join structures for production system programs. Our algorithm does not directly apply efficiency heuristics to programs, but rather enumerates possible join structures under various constraints and selects the best one. For this purpose, the cost model for production systems is introduced to estimate the run-time cost of join operations. Evaluation results demonstrate that the proposed algorithm can generate programs that are as efficient as those obtained by manual optimization, and thus can reduce the burden of manual optimization     

An optimization algorithm for pulse-frequency modulated control systems

A technique is presented for optimizing open-loop plants driven by PFM inputs. Ft. determines both the optimal amplitude and time of occurrence of the input pulses. It can be applied to linear or non-linear plants, to a large class of objective functionst to any number of input pulses, and for fixed or free end times. A step-by-step algorithm and a numerical example are given to demonstrate the technique.     

生化系统稳态优化的一种新算法

针对一类生化系统的稳态优化问题, 在已有间接优化方法(IOM)的线性优化问题中引入一个反映S–系统解和原模型解一致性的等式约束, 应用Lagrangian乘子法将修正后的非线性优化问题转化为一个等价的线性优化问题, 提出了一种改进的稳态优化新算法. 该优化算法不仅可以收敛到正确的系统最优解, 而且可用现有的线性规划算法去计算. 最后将算法应用于几个生化系统的稳态优化中, 结果表明, 本文提出的优化算法是有效的.     

A web services-based multidisciplinary design optimization framework for complex engineering systems with uncertainties

With the increased complexity of complex engineering systems (CES), more and more disciplines, coupled relationships, work processes, design data, design knowledge and uncertainties are involved. Currently, the MDO is facing unprecedented challenges especially in dealing with the CES by different specialists dispersed geographically on heterogeneous platforms with different analysis tools. The product design data integration and data sharing among the participants and the workflow optimization hamper the development and applications of MDO in enterprises seriously. Therefore, a multi-hierarchical integrated product design data model (MiPDM) supporting the MDO in web environment and a web services-based MDO framework considering aleatory and epistemic uncertainties are proposed in this paper. With the enabling technologies including web services, ontology, workflow, agent, XML, and evidence theory, the proposed framework enables the designers geographically dispersed to work collaboratively in the MDO environment. The ontology-based workflow enables the logical reasoning of MDO to be processed dynamically. Finally, a proof-of-concept prototype system is developed based on Java 2 Platform Enterprise Edition (J2EE) and an example of supersonic business jet is demonstrated to verify the web services-based MDO framework.     

Multiparametric optimization for multidisciplinary engineering design

The area of Multiparametric Optimization (MPO) solves problems that contain unknown problem data represented by parameters. The solutions map parameter values to optimal design and objective function values. In this paper, for the first time, MPO techniques are applied to improve and advance Multidisciplinary Design Optimization (MDO) to solve engineering problems with parameters. A multiparametric subgradient algorithm is proposed and applied to two MDO methods: Analytical Target Cascading (ATC) and Network Target Coordination (NTC). Numerical results on test problems show the proposed parametric ATC and NTC methods effectively solve parametric MDO problems and provide useful insights to designers. In addition, a novel Two-Stage ATC method is proposed to solve nonparametric MDO problems. In this new approach elements of the subproblems are treated as parameters and optimal design functions are constructed for each one. When the ATC loop is engaged, steps involving the lengthy optimization of subproblems are replaced with simple function evaluations.     

分布式系统TTCAN调度优化算法研究

总线技术的发展给线缆测试仪带来了分布式、信息化、网络化的新需求,且在分布式线缆测试仪工作过程中,测试线路的数目增加也对总线数据通讯的稳定性和通讯效率提出了更高的要求;针对分布式系统在线缆测试中的应用需要,设计并优化了分布式线缆测试仪工作的TTCAN应用层协议和其系统矩阵;对于分布式系统通信中的周期性消息形成的系统矩阵先后采用遗传算法、改进型差分进化算法进行优化,对于其中的非周期性消息采用基于松弛度的动态优先级算法;在MATLAB仿真环境中进行实验,实验结果表明,改进型差分算法比遗传算法能够更快、更稳定地计算出优化矩阵,经调度优化后的TTCAN总线工作时数据传输效率有显著提高;文章通过智能优化算法,有效提高了系统总线的通讯效率和稳定性。     

Computational algorithm for input-signal optimization in distributed-parameter systems identification

This paper deals with the problem of optimum choice of testing signals for identification in linear distributed-parameter systems. It is based on earlier theoretical results of one of the authors, which are extended here by investigating the structure of the optimal input signal. These investigations allow the proposal of an efficient computational algorithm. Its convergence is proved and the convergence rate is verified by numerical studies     

A computer algorithm for the optimization of discrete-time pulse frequency modulated systems

A computer algorithm is described for the optimization of discrete-time pulse frequency modulated systems with state and control constraints. The algorithm, based on a modified maximum principle [1], leads to the solution of a Boolean linear programming problem, for which many computer codes are available commercially. An application to a time-shared sampled-data control system is presented. Numerical examples are given.     

A surrogate based multistage-multilevel optimization procedure for multidisciplinary design optimization

Optimization procedure is one of the key techniques to address the computational and organizational complexities of multidisciplinary design optimization (MDO). Motivated by the idea of synthetically exploiting the advantage of multiple existing optimization procedures and meanwhile complying with the general process of satellite system design optimization in conceptual design phase, a multistage-multilevel MDO procedure is proposed in this paper by integrating multiple-discipline-feasible (MDF) and concurrent subspace optimization (CSSO), termed as MDF-CSSO. In the first stage, the approximation surrogates of high-fidelity disciplinary models are built by disciplinary specialists independently, based on which the single level optimization procedure MDF is used to quickly identify the promising region and roughly locate the optimum of the MDO problem. In the second stage, the disciplinary specialists are employed to further investigate and improve the baseline design obtained in the first stage with high-fidelity disciplinary models. CSSO is used to organize the concurrent disciplinary optimization and system coordination so as to allow disciplinary autonomy. To enhance the reliability and robustness of the design under uncertainties, the probabilistic version of MDF-CSSO (PMDF-CSSO) is developed to solve uncertainty-based optimization problems. The effectiveness of the proposed methods is verified with one MDO benchmark test and one practical satellite conceptual design optimization problem, followed by conclusion remarks and future research prospects.     

An ordinal optimization theory-based algorithm for large distributed power systems

In this paper, we propose an ordinal optimization (OO) theory-based algorithm to solve the yet to be explored distributed state estimation with continuous and discrete variables problems (DSECDP) of large distributed power systems. The proposed algorithm copes with a huge amount of computational complexity problem in large distributed systems and obtains a satisfactory solution with high probability based on the OO theory. There are two contributions made in this paper. First, we have developed an OO theory-based algorithm for DSECDP in a deregulated environment. Second, the proposed algorithm is implemented in a distributed power system to select a good enough discrete variable solution. We have tested the proposed algorithm for numerous examples on the IEEE 118-bus and 244-bus with four subsystems using a 4-PC network and compared the results with other competing approaches: Genetic Algorithm, Tabu Search, Ant Colony System and Simulated Annealing methods. The test results demonstrate the validity, robustness and excellent computational efficiency of the proposed algorithm in obtaining a good enough feasible solution.     

抗干扰的多智能体系统固定时间分布式优化算法

针对一阶多智能体系统提出一种抗干扰的分布式控制算法,在固定时间内解决具有状态约束和外部扰动存在情况下的多智能体系统凸优化问题.该算法分为两部分:第1部分使得每个智能体在任意初始条件下都能在固定时间内收敛到一致;第2部分在满足状态约束条件的同时,使所有局部目标函数的总和在固定时间内取得最小值.该算法能够在外部有界扰动存在的情况下抑制干扰信号,获得最优解,且收敛时间不受初始状态和外部扰动的影响,可以根据任务需求离线地预分配任务建立时间.利用凸优化和固定时间李雅普诺夫稳定性理论证明算法在有界扰动存在时的固定时间收敛性,最后通过智能电网中经济调度问题的实例验证算法的有效性和优越性.     

一种多学科设计优化工作流验证方法

在多学科设计优化集成系统中,设计过程和优化求解算法均通过可视化工作流实现,工作流有效性验证对提高设计效率和提高系统的用户体验具有重要意义。当前验证方法主要针对办公自动和企业管理系统中的工作流验证问题,多学科设计优化集成系统中的工作流验证问题研究较少。在分析前期工作验证技术的基础上,针对以循环结构为特征的优化环,提出一种基于图论方法的,名为浓缩环(concentration-loop)的验证算法。结合发射平台数字化设计系统的设计与实现,对该算法进行了验证。     

A Newton-like optimization algorithm for digital systems with time delay

The design and computation problems of optimum nonlinear digital time-delay control systems is considered. A minimum principle is presented which solves the design problem, and a second-order computational algorithm is developed which solves the computation problem. The minimum principle is shown to be a special case of a generalized minimum principle in. Hilbert space. The computational algorithm is actually a Newton-like hill-climbing technique for iteratively improving the guessed initial values of the conjugate vector λ so as to satisfy the final condition of the problem. The main advantage of the present algorithm is that it does not require excess memory as in other algorithms.     

Multi-objective reliability optimization for dissimilar-unit cold-standby systems using a genetic algorithm

A genetic algorithm approach is used to solve a multi-objective discrete reliability optimization problem in a k dissimilar-unit non-repairable cold-standby redundant system. Each unit is composed of a number of independent components with generalized Erlang distributions arranged in a series–parallel configuration. There are multiple component choices with different distribution parameters available for being replaced with each component of the system. The objective of the reliability optimization problem is to select the best components, from the set of available components, to be placed in the standby system in order to minimize the initial purchase cost of the system, maximize the system MTTF (mean time to failure), minimize the system VTTF (variance of time to failure) and also maximize the system reliability at the mission time. Finally, we apply a genetic algorithm with double strings using continuous relaxation based on reference solution updating (GADSCRRSU) to solve this multi-objective problem, using goal attainment formulation. The results are also compared against the results of a discrete-time approximation technique to show the efficiency of the proposed GA approach.