A projection-based decomposition for the scalability of evolvable hardware

Scalability is a main and urgent problem in evolvable hardware (EHW) field. For the design of large circuits, an EHW method with a decomposition strategy is able to successfully find a solution, but requires a large complexity and evolution time. This study aims to optimize the decomposition on large-scale circuits so that it provides a solution for the EHW method to scalability and improves the efficiency. This paper proposes a projection-based decomposition (PD), together with Cartesian genetic programming (CGP) as an EHW system namely PD-CGP, to design relatively large circuits. PD gradually decomposes a Boolean function by adaptively projecting it onto the property of variables, which makes the complexity and number of sub-logic blocks minimized. CGP employs an evolutionary strategy to search for the simple and compact solutions of these sub-blocks. The benchmark circuits from the MCNC library, \(n\)-parity circuits, and arithmetic circuits are used in the experiment to prove the ability of PD-CGP in solving scalability and efficiency. The results illustrate that PD-CGP is superior to 3SD-ES in evolving large circuits in terms of complexity reduction. PD-CGP also outperforms GDD+GA in evolving relatively large arithmetic circuits. Additionally, PD-CGP successfully evolves larger \(n\)-even-parity and arithmetic circuits, which have not done by other approaches.     

飞机结构总体优化设计方法

优化设计可以改善结构的应力分布,合理布置材料,从而提高材料的利用率.通过对全机结构逐层分解,确定优化设计变量、优化区域以及响应约束,并应用MSC Nastran中的可行方向法和敏度分析方法,对MA700飞机的机翼进行优化设计方法研究,考虑稳定性因素,初步解决长桁尺寸确定的问题,为后续优化设计工作积累经验.     

Concurrent optimal design of modular robotic configuration

This paper presents a new optimization design methodology that is applicable to modular systems. This new methodology is called concurrent optimization design method (CODM). A modular robot is taken as a case study. The CODM is superior to the existing methods for modular robot configuration design in the sense that traditional type synthesis and dimensional synthesis now can be treated once. This mathematically implies that (i) variables are defined for both types and dimensions, and (ii) all the variables are defined in one optimization problem formulation. This paper illustrates that, in fact, optimization design for modular architectures necessitates a multiobjective optimization problem. A genetic algorithm is used to solve for this complex optimization model which contains both discrete and continuous variables. © 2001 John Wiley & Sons, Inc.     

L.S.I. circuit design

A c.a.d. system for the custom design of CMOS l.s.i. circuits, in use at ASEA-HAFO Sweden, is described. The software is based on programs developed inhouse for nonlinear d.c.- and transient simulation, logic simulation and layout activities, such as digitizing, checking, plotting and photomask data generation. The equipment used includes a PDP-15 minicomputer together with a computer-controlled photomask system and l.s.i. test systems.Twenty-two complex circuits have been designed using the system.     

Robustness-based design optimization under data uncertainty

This paper proposes formulations and algorithms for design optimization under both aleatory (i.e., natural or physical variability) and epistemic uncertainty (i.e., imprecise probabilistic information), from the perspective of system robustness. The proposed formulations deal with epistemic uncertainty arising from both sparse and interval data without any assumption about the probability distributions of the random variables. A decoupled approach is proposed in this paper to un-nest the robustness-based design from the analysis of non-design epistemic variables to achieve computational efficiency. The proposed methods are illustrated for the upper stage design problem of a two-stage-to-orbit (TSTO) vehicle, where the information on the random design inputs are only available as sparse point data and/or interval data. As collecting more data reduces uncertainty but increases cost, the effect of sample size on the optimality and robustness of the solution is also studied. A method is developed to determine the optimal sample size for sparse point data that leads to the solutions of the design problem that are least sensitive to variations in the input random variables.     

Multidisciplinary modeling and surrogate assisted optimization for satellite constellation systems

Satellite constellation system design is a challenging and complicated multidisciplinary design optimization (MDO) problem involving a number of computation-intensive multidisciplinary analysis models. In this paper, the MDO problem of a constellation system consisting of small observation satellites is investigated to simultaneously achieve the preliminary design of constellation configuration and the satellite subsystems. The constellation is established based on Walker-δ configuration considering the coverage performance. Coupled with the constellation configuration, several disciplines including payload, power, thermal control, and structure are taken into account for satellite subsystems design subject to various constraints (i.e., ground resolution, power usage, natural frequencies, etc.). Considering the mixed-integer and time-consuming behavior of satellite constellation system MDO problem, a novel sequential radial basis function (RBF) method using the support vector machine (SVM) for discrete-continuous mixed variables notated as SRBF-SVM-DC is proposed. In this method, a discrete-continuous variable sampling method is utilized to handle the discrete variables, i.e., the number of orbit planes and number of satellites, in the satellite constellation system MDO problem. RBF surrogates are constructed and gradually refined to represent the time-consuming simulations during optimization, which can efficiently lead the search to the optimum. Finally, the proposed SRBF-SVM-DC utilized to solve the satellite constellation system MDO problem is compared with a conventional integer coding based genetic algorithm (ICGA). The results show that SRBF-SVM-DC significantly decreases the system mass by about 28.63% subject to all the constraints, which greatly reduces the cost of the satellite constellation system. Moreover, the computational budget of SRBF-SVM-DC is saved by over 85% compared with ICGA, which demonstrates the effectiveness and practicality of the proposed surrogate assisted optimization approach for satellite constellation system design.     

下三角矩阵神经网络逻辑电路的设计

由于人工神经网络的卓越优点，为制造超高速，高可靠和可编程的数字集成电路提供了新途径，具有下三角形连接矩阵的Ｈｏｐｆｉｅｌｄ模型在同一输入下仅有唯一的平衡点。本文将讨论基于这种网络模型的组合逻辑电路的逻辑设计方法，以最小化神经元个数为目标的启发式优化算法及权电阻网络参数的计算方法。     

基于神经网络的不确定随机非线性时滞系统自适应有界镇定

针对一类不确定严格反馈随机非线性时滞系统的自适应有界镇定问题,利用神经网络参数化和Backstepping方法,提出一种新的且含较少学习参数的神经网络自适应控制策略,以保证系统半全局随机有界.稳定性分析证明闭环系统的所有误差信号概率意义下有界.仿真结果表明所提出控制器设计方法的有效性.     

A new problem in control system design [and circuit design]

An optimisation problem arising in the design of circuits and control systems is formulated, and an outer approximation algorithm for solving it is presented. The problem is characterized by constraints that must be satisfied for all functions in a given subset of a function space. It is shown that an outer-approximation-type algorithm increases the number of variables and constraints at each iteration. A practical algorithm will require a constraint dropping scheme     

Analog and digital FPGA implementation of BRIN for optimization problems.

The binary relation inference network (BRIN) shows promise in obtaining the global optimal solution for optimization problem, which is time independent of the problem size. However, the realization of this method is dependent on the implementation platforms. We studied analog and digital FPGA implementation platforms. Analog implementation of BRIN for two different directed graph problems is studied. As transitive closure problems can transform to a special case of shortest path problems or a special case of maximum spanning tree problems, two different forms of BRIN are discussed. Their circuits using common analog integrated circuits are investigated. The BRIN solution for critical path problems is expressed and is implemented using the separated building block circuit and the combined building block circuit. As these circuits are different, the response time of these networks will be different. The advancement of field programmable gate arrays (FPGAs) in recent years, allowing millions of gates on a single chip and accompanying with high-level design tools, has allowed the implementation of very complex networks. With this exemption on manual circuit construction and availability of efficient design platform, the BRIN architecture could be built in a much more efficient way. Problems on bandwidth are removed by taking all previous external connections to the inside of the chip. By transforming BRIN to FPGA (Xilinx XC4010XL and XCV800 Virtex), we implement a synchronous network with computations in a finite number of steps. Two case studies are presented, with correct results verified from simulation implementation. Resource consumption on FPGAs is studied showing that Virtex devices are more suitable for the expansion of network in future developments.     

形式验证中同步时序电路的VHDL描述到S2-FSM的转换

符号模型检查（ＳｙｍｂｏｌｉｃＭｏｄｅｌＣｈｅｃｋｉｎｇ,ＳＭＣ）是一种有效的形式验证方法．该方法主要有２个难点：一个是建模,即如何建立并用有限内存来表示电路的状态机模型;另一个是在此模型基础上的验证算法．由于验证时间和有限状态机模型的大小是直接相关的,因而模型的大小就成为ＳＭＣ中的关键问题．本文提出一种基于同步电路行为描述的新的有限状态机模型Ｓ２－ＦＳＭ,并给出从同步电路的ＶＨＤＬ描述建立这种模型的过程．由于该模型的状态转换函数是基于时钟周期的,消去了与时钟无关的大量中间变量,所以同Ｄｅｈａｒｂｅ提出的模型相比,它的状态数大大减少．若干电路的实验结果表明,该模型由于减少了状态规模,建模时间和可达性分析时间大大减少,效果十分显著．     

Optimum design of trusses with discrete sizing and shape variables

The objective here is to present a method for optimizing truss structures with discrete design variables. The design variables are considered to be sizing variables as well as coordinates of joints. Both types of variables can be discrete simultaneously. Mixed continuous-discrete variables can also be considered. To increase the efficiency of the method, the structural responses, such as forces and displacements are approximated in each design cycle. The approximation of responses is carried out with respect to the design variables or their reciprocals. By substituting these approximate functions of the responses into the original design problem, an explicit high quality approximation is achieved, the solution of which does not require the detailed finite element analysis of the structure in each sub-optimization iteration. First it is assumed that all the design variables are continuous and a continuous variable optimization is performed. With the results of this step, the branch and bound method is employed on the same approximate problem to achieve a discrete solution. The numerical results indicate that the method is efficient and robust in terms of the required number of structural analyses. Several examples are presented to show the efficiency of the method.     

Optimal design of large-scale space steel frames using cascade enhanced colliding body optimization

In structural size optimization usually a relatively small number of design variables is used. However, for large-scale space steel frames a large number of design variables should be utilized. This problem produces difficulty for the optimizer. In addition, the problems are highly non-linear and the structural analysis takes a lot of computational time. The idea of cascade optimization method which allows a single optimization problem to be tackled in a number of successive autonomous optimization stages, can be employed to overcome the difficulty. In each stage of cascade procedure, a design variable configuration is defined for the problem in a manner that at early stages, the optimizer deals with small number of design variables and at subsequent stages gradually faces with the main problem consisting of a large number of design variables. In order to investigate the efficiency of this method, in all stages of cascade procedure the utilized optimization algorithm is the enhanced colliding bodies optimization which is a powerful metaheuritic. Three large-scale space steel frames with 1860, 3590 and 3328 members are investigated for testing the algorithm. Numerical results show that the utilized method is an efficient tool for optimal design of large-scale space steel frames.     

Application of genetic algorithms in problems of optimization of the physical design in microelectronics

The problem of adaptation of standard cells and small circuits in the VLSI design flow at the stage of physical design is discussed. A method of multiobjective optimization of standard circuits for application in currently available design flows is proposed. Experimental results show that the energy consumption characteristics and area are enhanced by 12–18%.     

Variables Bounding Based Retiming Algorithm

Retiming is a technique for optimizing sequential circuits.In this paper,we discuss this problem and propose an improved retiming algorithm based on varialbes bounding.Through the computation of the lower and upper bounds on variables,the algorithm can significantly reduce the number of constratints and speed up the execution of retiming.Furthermore,the elements of matrixes D and W are computed in a demand-driven way,which can reduce the capacity of memory,It is shown through the experimental results on ISCAS89 benchmarks that our algorithm is very effective for large-scale seuqential circuits.     

Logic programming and digital circuit analysis

We show how the unique character of logic programming can be exploited for the purpose of specifying and automatically reasoning about electrical circuits. Although propositional logic has long been used for describing the truth functions of combinational circuits, the more powerful predicate calculus on which logic programming is based has seen relatively little use in design automation. Previous researchers have introduced a number of techniques similar to logic programming, but many of the useful consequences of the methodology have not been demonstrated. We describe particular consequences of using this method for writing directly executable specifications of circuits, including the use of quantified variables, verification of hypothetical states, and sequential simulation. We have used these methods to solve problems in gate assignment, specialization of standard definitions, and determination of signal flow.     

Discrete variable optimization of plate structures using dual methods

This study presents an efficient method for optimum design of plate and shell structures, when the design variables are continuous or discrete. Both sizing and shape design variables are considered. First the structural responses, such as element forces, are approximated in terms of some intermediate variables. By substituting these approximate relations into the original design problem, an explicit nonlinear approximate design task with high quality approximation is achieved. This problem with continuous variables can be solved very efficiently by means of numerical optimization techniques, the results of which are then used for discrete variable optimization. Now, the approximate problem is converted into a sequence of second level approximation problems of separable form, each of which is solved by a dual strategy with discrete design variables. The approach is efficient in terms of the number of required structural analyses, as well as the overall computational cost of optimization. Examples are offered and compared with other methods to demonstrate the features of the proposed method.     

电源网络拓扑优化方法研究

电压降问题日益严重,为了加速电源网络设计收敛,本文对底层规划阶段电源网络拓扑优化展开研究,将其描述为带约束的优化问题,并提出一种近似直接搜索法,通过不断更新电源网络拓扑,在满足约束条件的前提下,逼近目标函数.实验结果表明,该方法比均匀分布法最大节省20.12%的电源线面积,并且优化性能随电源网络规模的增大而提高.     

A comparison of global optimization algorithms applied to a ride comfort optimization problem

This study presents a comparison of global optimization algorithms applied to an industrial engineering optimization problem. Three global stochastic optimization algorithms using continuous variables, i.e. the domain elimination method, the zooming method and controlled random search, have been applied to a previously studied ride comfort optimization problem. Each algorithm is executed three times and the total number of objective function evaluations needed to locate a global optimum is averaged and used as a measure of efficiency. The results show that the zooming method, with a proposed modification, is most efficient in terms of number of objective function evaluations and ability to locate the global optimum. Each design variable is thereafter given a set of discrete values and two optimization algorithms using discrete variables, i.e. a genetic algorithm and simulated annealing, are applied to the discrete ride comfort optimization problem. The results show that the genetic algorithm is more efficient than the simulated annealing algorithm for this particular optimization problem.     

Stacking sequence optimization with genetic algorithm using a two-level approximation

We propose a new method for laminate stacking sequence optimization based on a two-level approximation and genetic algorithm (GA), and establish an optimization model including continuous size variables (thicknesses of plies) and discrete variables (0/1 variables that represent the existence of each ply). To solve this problem, a first-level approximate problem is constructed using the branched multipoint approximate (BMA) function. Since mixed-variables are involved in the first-level approximate problem, a new optimization strategy is introduced. The discrete variables are optimized through the GA. When calculating the fitness of each member in the population of GA, a second-level approximate problem that can be solved by the dual method is established to obtain the optimal thicknesses corresponding to the each given ply orientation sequence. The two-level approximation genetic algorithm optimization is performed starting from a ground laminate structure, which could include relatively arbitrarily discrete set of angles. The method is first applied to cylindrical laminate design examples to demonstrate its efficiency and accuracy compared with known methods. The capacity of the optimization strategy to solve more complex problems is then demonstrated using a design example. With the presented method, the stacking sequence in analytical tools can be directly taken as design variables and no intermediate variables need be adopted.