Discrete variable structural optimization based on multidirectional fuzzy genetic algorithm

Round method is the common method for discrete variable optimization in optimal design of complex mechanical structures;however,it has some disadvantages such as poor precision,simple model and lacking...     

A single sensor and single actuator approach to performance tailoring over a prescribed frequency band

Restricted sensing and actuation control represents an important area of research that has been overlooked in most of the design methodologies. In many practical control engineering problems, it is necessitated to implement the design through a single sensor and single actuator for multivariate performance variables. In this paper, a novel approach is proposed for the solution to the single sensor and single actuator control problem where performance over any prescribed frequency band can also be tailored. The results are obtained for the broad band control design based on the formulation for discrete frequency control. It is shown that the single sensor and single actuator control problem over a frequency band can be cast into a Nevanlinna–Pick interpolation problem. An optimal controller can then be obtained via the convex optimization over LMIs. Even remarkable is that robustness issues can also be tackled in this framework. A numerical example is provided for the broad band attenuation of rotor blade vibration to illustrate the proposed design procedures.     

Optimal Placement of Active Members in Truss Adaptive Structures

The mathematical model of optimal placement of active members in truss adaptive structures is essentially a nonlinear multi-objective optimization problem with mixed variables,it is usually much difficult and costly to be solved.In this paper,the optimal location of active members is treated in terms of(0,1)discrete variables.Structural member sizes,control gains,and(0,1)placement variables are treated simultaneously as design variables.Then,a succinct and reasonable compromise scalar model,which is transformed from original multi-objective optimization,is established,in which the(0,1)discrete variables are converted into an equality constraint.Secondly,by penalty function approach,the subsequent scalar mixed variable compromise model can be formulated equivalently as a sequence of continuous variable problems.Thirdly,for each continuous problem in the sequence,by choosing intermediate design variables and temporary critical constraints,the approximation concept is carried out to generate a sequence of explicit approximate problems which enhance the quality of the approximate design problems.Considering the proposed method,a FORTRAN program OPAMTAS2.0 for optimal placement of active members in truss adaptive structures is developed,which is used by the constrained variable metric method with the watchdog technique(CVMW method).Finally,a typical 18 bar truss adaptive structure as test numerical examples is presented to illustrate that the design methodology set forth is simple,feasible,efficient and stable.The established scalar mixed variable compromise model that can avoid the ill-conditioned possibility caused by the different orders of magnitude of various objective functions in optimization process,therefore,it enables the optimization algorithm to have a good stability.On the other hand,the proposed novel optimization technique can make both discrete and continuous variables be optimized simultaneously.     

高维多目标优化设计的灰色微粒群算法

提出了一种基于混合离散变量多目标优化设计求解方法,该方法应用灰色相对关联度将多目标优化问题转化为单目标优化问题,再用改进微粒群算法求解.开发了混合离散变量优化的灰色改进微粒群法程序.该方法能合理地处理优化设计中混合离散变量的取值问题,通过对标准微粒群算法改进,引入动态罚函数,算法具有很强的全局寻优能力.优化设计实例表明,该算法对优化设计问题的特性无特殊要求,具有较好的普适性,而且程序运行可靠,全局收敛能力强.     

粒子群算法在工程优化设计中的应用

将粒子群算法与惩罚函数法相结合,建构一种离散粒子群算法,解决工程上非线性约束离散变量优化设计问题。为实现离散变量与连续变量的转化,构造了相应的扩张函数,提出惩罚因子的确定策略。通过容器设计算例验证,粒子群算法方法优于文献所列方法。应用粒子群算法、惩罚函数法及所提出的策略对波纹管工程实例进行优化设计,其单位重量下整体波纹管的补偿量比在用产品提高了79.96%,与理论解接近,进一步证明了离散粒子群算法及策略在处理工程非线性约束离散优化设计问题时的有效性,其为工程上类似优化设计提供借鉴。     

Buffer allocation in unreliable production lines based on design of experiments, simulation, and genetic algorithm

This paper presents a multiobjective formulation of the buffer allocation problem in unreliable production lines. Majority of the solution methods for buffer allocation problems assume that the process times, time between failures, and repair times are deterministic or exponentially distributed. This paper relaxes these restrictions by proposing a simulation-based methodology which can consider general function distributions for all parameters of production lines. Factorial design has been used to build a meta-model for estimating production rate based on a detailed, discrete event simulation model. We use genetic algorithm combined to line search method to solve the multiobjective model and determining the optimal (or near optimal) size of each buffer storage.     

基于差分进化算法的手眼标定方法

针对眼在手机器人视觉系统的手眼标定问题，提出了一种基于差分进化算法的手眼标定方法。首先建立了眼在手机器人视觉系统手眼标定的数学模型，通过李群李代数理论，将手眼标定问题转化为误差函数的优化问题，同时保证了优化问题的最优解落在特殊欧氏群ＳＥ（３）上。通过差分进化算法对优化问题进行求解，避免了求解过程中的局部最优问题。最后在实物平台上进行了手眼标定实验，实验结果验证了算法的可行性和有效性。     

Using PCR-TOPSIS to optimise Taguchi's multi-response problem

The Taguchi method is an efficient method used in off-line quality control where experimental design is combined with quality loss. This method includes three stages—system design, parameter design, and tolerance design. In the real world it is obvious that more than one quality characteristic should be considered for most industrial products; i.e., in most applications the customer's concern is with multi-response problems. Nevertheless, the Taguchi method is not appropriate for optimising a multi-response problem since engineering judgment is the main optimisation procedure in Taguchi method. In order to overcome this problem, this paper proposes an effective procedure called PCR-TOPSIS that is based on process capability ratio (PCR) theory and on the theory of order preference by similarity to the ideal solution (TOPSIS) to optimise multi-response problems. Using PCR-TOPSIS, multiple responses in each experiment will be transformed into a performance index. Therefore, the optimal factors/levels combinations for the multi-responses can be determined. Two case studies in Tarng et al. and Reddy et al. are resolved using the proposed procedure. The result indicates that PCR-TOPSIS can yield a satisfactory solution for multi-response problems.     

Rule-based production planning in flexible manufacturing systems

Production planning in flexible manufacturing may require the solution of a large-scale discrete-event dynamic stochastic optimization problem, due to the complexity of the system to be optimized, and to the occurrence of discrete events (new orders and hard failures). The production planning problem is here approached for a multistage multipart-type manufacturing shop, where each work cell can share its processing time among the different types of parts. The solution of this problem is obtained by an open-loop-feedback control strategy, updated each time a new event occurs. At each event time, two coupled problems are solved: 1) a product-order scheduling problem, conditioned on estimated values of the production capacities of all component work cells; and 2) a production-capacity planning problem, conditioned on predefined sequences of the product orders to be processed. In particular, the article aims at defining a production planning procedure that integrates both analytical tools, derived from mathematical programming, and knowledge-based rules, coming from experience. The objective is to formulate a hybrid (knowledge-based/analytical) planning architecture, and to analyze its use for multicell multipart-type manufacturing systems.     

A hybrid assembly sequence planning approach based on discrete particle swarm optimization and evolutionary direction operation

Assembly sequence planning (ASP) has always been an important part of the product development process, and ASP problem can usually be understood as to determine the sequence of assembly. A good assembly sequence can reduce the time and cost of the manufacturing process. In view of the local convergence problem with basic discrete particle swarm optimization (DPSO) in ASP, this paper presents a hybrid algorithm to solve ASP problem. First, a chosen strategy of global optimal particle in DPSO is introduced, and then an improved discrete particle swarm optimization (IDPSO) is proposed for solving ASP problems. Through an example study, the results show that the IDPSO algorithm can obtain the global optimum efficiently, but it converges slowly compared with the basic DPSO. Subsequently, a modified evolutionary direction operator (MEDO) is used to accelerate the convergence rate of IDPSO. The results of the case study show that the new hybrid algorithm MEDO-IDPSO is more efficient for solving ASP problems, with excellent global convergence properties and fast convergence rate.     

改进的惩罚函数优化法

用优化方法解决离散变量的工程问题，可采用惩罚函数法。本文对离散变量惩罚函数法作了改进，并应用实例证实了其可行性，为工程优化问题提供了有参考价值的优化算法。     

MODAL MODIFICATION OF VIBRATING SYSTEMS: SOME PROBLEMS AND THEIR SOLUTIONS

Experimental modal testing data form an incomplete set of natural frequencies and mode shapes. The incompleteness of modal representation is inherent also to continuous systems that are represented by discrete models. Furthermore, current state-of-the-art numerical methods for determining the eigenvalues of large discrete systems can extract only some eigenvalues and eigenvectors associated either with low frequencies or the high-frequency range. Therefore, when predicting the effect of structural modification on the spectrum of a modified structure an error called a ‘truncation error’ exists in the calculations.The paper focuses on the prediction of the error in the spectrum of a modified structure resulting from the incomplete representation of the model. An optimal solution in a Rayleigh–Ritz sense is derived and the truncation error is bounded. Then the inverse problem of determining the structural modification that is needed to assign the spectrum is considered. A mathematical formulation characterising a continuous family of solutions is first given. Then a procedure for determining physical realisable solutions is developed. Finally, it is shown that if both left and right eigenvectors of the system are known then it is possible to solve the inverse structural modification problem without truncation error. The extraction of left eigenvectors from experimental data, however, can be an ill-conditioned problem. Thus, the modification could be sensitive to noise in the measured data, and the truncation error alone might not be the dominant mechanism of error.     

Optimum path-tracking control for inverse problem of vehicle handling dynamics

A method based on optimal control theory is presented in this paper to solve path-tracking problems in inverse vehicle handling dynamics. The idea behind is to identify the optimal steering torque input along a prescribed path to generate an expected trajectory that guarantees minimum clearance. Based on this purpose, the path-tracking problem, treated as an optimal control problem, is first converted into a nonlinear programming problem by Gauss pseudospectral method (GPM) and is then solved with Sequential quadratic programming (SQP). Finally, a real vehicle test is executed to verify the rationality of the proposed model and methodology. Results show that the minimum lateral position error of the generated path-tracking trajectory can be a good solution for path-tracking problem in inverse vehicle handling dynamics for GPM. The algorithm has higher calculation accuracy compared with other methods to solve path-tracking problems. The study could help drivers identify safe lane-keeping trajectories and areas easily.     

Multipass, multicriterion optimisation in turning

A constrained optimisation analysis of the multipass turning operation, based on a combined cost and time objective is outlined. The work involves development of solution strategies for multipass multicriterion problems using geometric programming, dynamic programming and the weighting coefficient method. A parametric study is presented to demonstrate the utility and applicability of such strategies. An algorithm is developed which can be easily and effectively applied to investigate the optimal number of passes and corresponding parameters such as speed, feed and depth of cut in each pass for multipass, multicriterion plain turning problems. The constraints considered are speed, feed, depth of cut, surface finish, power, temperature, force and deflection. The available discrete values of speed and feed for a particular machine are involved to get the feasible optimal solution.     

基于DE算法的双波长光纤F-P传感系统结构优化

DE算法全局搜索能力强,收敛速度快,鲁棒性好,易于使用,能有效的解决复杂优化问题。针对光纤F-P传感器易偏离工作点问题,提出了双波长稳定技术,建立双波长稳定系统的数学优化模型,采用DE算法进行结构优化。结果表明,DE算法能在较短的运行时间内得到全局最优解,优化目标下降98.375%;证明该算法正确可行,高效可靠,能很好的用于光纤F-P传感器的结构优化设计。     

扩展拉格朗日乘子粒子群算法解决工程优化问题

工程上很多优化问题,如容器设计、波纹管、板翅式换热器的结构优化设计等,皆为非线性约束优化设计问题,常采用惩罚函数法处理约束条件;为获得问题最优解,该方法需要合理确定初始惩罚因子,且需要动态惩罚因子无穷大。扩展拉格朗日乘子法是一种改进的惩罚函数法,可以克服惩罚函数法的不足,获得全局最优解,但目前对其研究和应用有限。对拉格朗日乘子法与粒子群算法相结合处理非线性约束问题进行研究,提出惩罚因子更新策略,确定扩展拉格朗日乘子粒子群算法合理的操作过程。标准测试函数结果显示：提出的方法及策略实现了扩展拉格朗日乘子粒子群算法解决非线性约束问题,并得到了问题的全局最优解;其在容器及波纹管系列优化设计中的应用进一步显示,提出的方法在处理非线性约束工程实际问题时,运行稳定可靠,可快捷获得问题的全局最优解或近似最优解。     

基于混合遗传算法的离散事件系统仿真优化方法研究

在分析仿真优化原理的基础上提出一种新的混合遗传算法，该算法集成变尺度遗传算法与禁忌搜索算法为一体，处理离散事件系统性能参数仿真优化的问题，大大提高了仿真优化的效率和解的质量，基于此算法开发了一个仿真优化软件系统，并将该仿真优化系统与自主开发的离散系统仿真软件进行了有机的集成，给出了一个仿真优化系统实例。     

GLOBAL OPTIMIZATION OF PUMP CONFIGURATION PROBLEM USING EXTENDED CROWDING GENETIC ALGORITHM

An extended crowding genetic algorithm (ECGA) is introduced for solving optimal pumpconfiguration problem, which was presented by T.Westerlund in 1994. This problem has been found tobe non-convex, and the objective hation contained several local optima and global optimality couldnot be ensured by all the traditional MINLP optimization method. The concepts of species conservingand composite encoding are introduced to crowding genetic algorithm (CGA) for mantain the diver-sity of population more effectively and coping with the continuous and/or discrete variables in MINLPproblem. The solution of three-levels pump configuration got from DICOPT++ software (OA algo-rithm) is also given. By comparing with the solutions obtained from DICOPT++, ECP method, andMIN-MIN method, the ECGA algorithm proved to be very effective in finding the global optimalsolution of multi-levels pump configuration via using the problem-specific information.     

用于供水系统直接优化调度的蚁群改进算法

在城市供水系统中建立了多目标在线直接优化调度模型，并对影响优化调度的各方面因素进行了系统的分析和挑选。使用化多为一的乘除法，将该多目标决策问题转化为单目标问题求解，提出了使用乘法形式的罚函数将模型中的约束函数转化为目标函数。采用蚁群算法求解调度模型。为了更好地得到全局最优解，对算法进行了改进，加入了更多的决策点，实现蚁群算法的二进制编码方法，并采用单只最优蚂蚁更新路径上的外激素值、外激素值限定在一定范围内等改进方法。使用改进算法实现了某小区供水系统的直接优化调度，并与遗传算法优化调度的过程进行了对比，新算法在优化时间及得到最优解的次数上都优于遗传算法。     

基于NSGAⅡ的齿轮减速器多目标优化研究

为了解决工程设计中有离散变量、多约束的多目标优化问题,对改进的非占优排序遗传算法(NSGAⅡ)进行了研究,通过基于拥挤距离的非占优排序,提出了离散变量和多约束的处理方法,利用Matlab软件编写了NSGAⅡ的多目标优化程序,并以二级减速器多目标优化设计为例,建立了多目标优化数学模型,运用NSGAⅡ算法求解得到了帕累托最优解集,根据模糊集合理论的有关方法选取了最优解,与传统方法得到的结果相比,体积、失效概率和传动误差都有不同程度的降低。研究结果表明,修改后的NSGAⅡ能用于有效地求解有离散变量、多约束的多目标优化设计问题。