多目标模糊优化方法及其在可靠性设计中的应用

在基于模糊机会约束规划理论基础上,对多目标模糊优化方进行研究,通过模糊模拟求解模糊目标函数的理想极大值和极小值,建立了多目标优化问题的模糊机会约束规划模型,并给出了基于模糊模拟的遗传算法求解方法.通过对一个无约束处理的常规双目标模糊可靠性优化问题实例求解,验证了多目标模糊模拟遗传算法的有效性,并指出该算法的优点在于能较好处理多目标优化问题的分目标量纲不同及其函数值之间差距较大的情况.     

Multi-objective optimization of high-speed milling with parallel genetic simulated annealing

In this paper, the optimization of multi-pass milling has been investigated in terms of two objectives: machining time and production cost. An advanced search algorithm—parallel genetic simulated annealing (PGSA)—was used to obtain the optimal cutting parameters. In the implementation of PGSA, the fitness assignment is based on the concept of a non-dominated sorting genetic algorithm (NSGA). An application example is given using PGSA, which has been used to find the optimal solutions under four different axial depths of cut on a 37 SUN workstation network simultaneously. In a single run, PGSA can find a Pareto-optimal front which is composed of many Pareto-optimal solutions. A weighted average strategy is then used to find the optimal cutting parameters along the Pareto-optimal front. Finally, based on the concept of dynamic programming, the optimal cutting strategy has been obtained.     

车间多目标优化调度问题的研究方法和应用

系统地总结近年来车间多目标调度问题中常用的研究方法,介绍算法的基本思想和实际的使用情况,总结车间调度问题研究中的不足和局限性.     

Scheduling of an assembly line with a multi-objective genetic algorithm

Scheduling problems are difficult combinatorial problems because of the extremely large search space of possible solutions and the large number of local optima that arise. A multi-objective genetic algorithm is presented as an intelligent algorithm for scheduling of the mixed-model assembly line in this paper. The Pareto ranking method and distance-dispersed approach are employed to evaluate the fitness of the individuals. The computational results show that the proposed multi-objective genetic algorithm is quite effective.     

Improved multi-objective ant colony optimization algorithm and its application in complex reasoning

The problem of fault reasoning has aroused great concern in scientific and engineering fields.However,fault investigation and reasoning of complex system is not a simple reasoning decision-making problem.It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints.So far,little research has been carried out in this field.This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes.Three optimization objectives are considered simultaneously: maximum probability of average fault,maximum average importance,and minimum average complexity of test.Under the constraints of both known symptoms and the causal relationship among different components,a multi-objective optimization mathematical model is set up,taking minimizing cost of fault reasoning as the target function.Since the problem is non-deterministic polynomial-hard(NP-hard),a modified multi-objective ant colony algorithm is proposed,in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives.At last,a Pareto optimal set is acquired.Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set,through which the final fault causes can be identified according to decision-making demands,thus realize fault reasoning of the multi-constraint and multi-objective complex system.Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model,which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.     

车身结构NVH特性多目标拓扑优化研究

建立了某车身地板结构动力学有限元模型,通过分析自由模态固有频率和多点激励下频率响应,验证了车身地板数值分析模型的有效性。以地板结构各激励点最大振动速度的平方和最小化作为目标函数,建立了多目标拓扑优化模型。通过解读优化结果,提出了地板结构改进方案。改进后的车身地板结构各阶自由模态固有频率增加达10%以上,各激励点速度响应大大降低,NVH特性明显提高。研究表明,减小振动速度的多目标拓扑优化设计是一种改善车身NVH特性的有效方法。     

随机环境下锻造多级多目标组批全局与局部协同优化

为解决多级多目标组批投放决策协同优化算法,对建模工具进行分析,选择基于微软Aglet的移动智能体（Agent）和基于Flexsim的仿真相结合的建模技术,系统控制采用混合控制结构以避免传统多Agent系统的缺陷;设计了锻造多级、多目标组批协同求解框架,给出了多Agent协作模型和基于统一建模语言的协同过程。提出基于强化Q学习的改进型多目标前向投放控制策略作为多级多目标协同算法。对可能影响实验结果的生产系统参数设置作了对照实验,实验研究了不确定条件下不同算法的拖期数和成本表现,结果表明本文算法明显优于传统的前向投放策略,生产系统的输出具有很好的稳定性。     

一类多层生产排序问题的建模与求解

针对一类航天企业普遍存在的多层生产排序问题,建立了整体优化模型,并根据多层生产排序的特点,提出了一种分解-协调的建模思想及其求解策略.在建立三层子问题独立优化模型的基础上,依据各层工件可开工时间及设备最早可用时间对优化解进行自下而上的协调,逐步获得各层子问题的最优解,进而获得整体问题的满意解.采用该方法对来源于实际企业的多层排序问题进行建模和求解,所得结果较实际结果有显著提高,验证了其有效性和实用性.     

基于多目标遗传算法优化板料拉深成形工艺参数

利用人工神经网络构建了板料拉深成形的目标函数模型,建立了板料拉深成形工艺参数和性能评价指标之间的映射关系.以多种工艺参数(压边力、摩擦因数等)作为优化变量,多种成形缺陷(起皱、破裂等)作为优化目标,结合多目标遗传算法和数值模拟,建立了板料拉深成形工艺参数的优化设计模型,大大提高了优化的效率.以油底壳下盖为例,对其拉深成形工艺参数进行了优化,通过对优化结果进行数值模拟可以看出,该优化参数完全避免了各种缺陷的产生,这说明该优化算法具有较好的优化结果.     

Multi-objective optimization of surface grinding process with the use of evolutionary algorithm with remembered Pareto set

In this paper the multi-objective optimization of a surface grinding process making use of an evolutionary algorithm is presented. Such factors as wheel speed, workpiece speed, depth of dressing and lead of dressing are optimized in order to minimize production cost and surface roughness or to minimize production cost and maximize production rate. In the algorithm, the optimization is introduced in Pareto’s sense, all acceptable and non-dominated solutions are remembered, and therefore the final result is not a single solution, but a whole set. The proposed method based on an example chosen from literature is tested, and the results obtained are compared with the results obtained by the use of other methods.     

复杂机械协同优化设计的规划策略研究

为高效地实现复杂机械产品的协同优化,提出了一种协同优化进程规划策略,该规划包含求解进程规划和分解建模规划两个核心环节。制定规划的根据是产品模型的特征,包括模型中包含的节点类型、节点之间的依赖层次、节点之间的相关强度、节点内部性能属性之间的交叉强度、模型的相对计算量和模型的优化难度等。基于相关特征给出了相应的求解进程规划准则、分解建模规划的目标和约束,从而为复杂系统的协同优化提供了一种新的有效的实施策略。该研究成果在挖掘机优化设计中得到了成功的应用。     

Investigating material removal rate and surface roughness using multi-objective optimization for focused ion beam (FIB) micro-milling of cemented carbide

Cemented carbide has been investigated as a useful material for the fabrication of micro devices. Focused ion beam (FIB) micro-milling has been found to be one of the most appropriate methods for the fabrication of micro devices. The experimental FIB micro-milling on cemented carbide have been conducted according to the L₁₆ orthogonal array of Taguchi technique. Beam current, extraction voltage, angle of beam incidence, dwell time and percentage overlap between beam diameters have been considered as process variables of FIB micro-milling in experimental design. Material removal rate (MRR) and surface roughness have been determined experimentally for FIB micro-milling of cemented carbide and beam current has been identified as the most significant parameter. The minimum surface roughness of 5.6 nm has been reported on cemented carbide, which is not a usual practice to achieve on such polycrystalline material, and hence it may be considered as a significant research contribution. Maximum MRR of 0.4836 μm³/s has been reported. Moreover, genetic algorithm toolbox of MATLAB has been utilized for multi-objective optimization between MRR and surface roughness. The corresponding optimum values of MRR and surface roughness for multi-objective optimization have been represented by pareto optimum solution generated by genetic algorithm. The research work presented in this paper determines the setting of process parameters of FIB micro-milling for achieving a specific combination of MRR and surface roughness on cemented carbide.     

Tolerance design optimization of machine elements using genetic algorithm

An important problem that faces design engineers is how to assign tolerance limits. In practical applications, tolerances are most often assigned as an informal compromise between functionality, quality and manufacturing cost. Frequently, the compromise is obtained iteratively by trial and error. A more scientific approach is often desirable for better performance. In this paper, a genetic algorithm (GA) is used for the design of tolerances of machine elements to obtain the global optimal solution. The objective is to design the optimum tolerances of the individual components to achieve the required assembly tolerance, zero percentage rejection of the components and minimum cost of manufacturing. The proposed procedure using GA is described in this paper for two tolerance design optimization problems: gear train and overrunning clutch assemblies. Results are compared with conventional techniques and the performances are analyzed.     

贴片机贴装路径优化研究

贴片机贴装路径与表面组装生产线的装配效率紧密相关,深入分析贴装路径优化问题,提出该问题的数学模型,应用遗传算法实施贴装路径优化,并给出相应的MATLAB程序.通过仿真实验,验证了该方法是可行的.     

利用协同优化方法实现复杂机械系统的设计优化

利用协同优化方法实现了四辊轧机机座的结构参数设计优化。根据多学科设计优化对复杂系统进行设计优化的思想,提出将复杂机械系统分解为若干个简单的子系统进行设计优化。协同优化是近年来发展较快的一种多学科设计优化算法,描述了应用于复杂工程系统优化设计的协同优化方法。在协同优化的系统级优化中,引入松弛变量,将一致性等式约束转化为不等式约束,松弛变量的取值影响系统级优化的收敛速度。通过对四辊轧机机座的结构参数设计优化,给出了利用协同优化方法进行复杂机械系统设计优化的一般思路,为解决更为复杂的机械系统的设计优化问题打下了基础。算例证明了协同优化算法解决复杂机械系统设计优化问题的有效性。     

机械光学混合测量法在复杂结构逆向工程中的应用研究

探讨了如何融合CMM(三坐标测量仪)和Photogrammetry(摄影测量)两种技术,来生成用来拼接点云数据（point cloud)的参考点系统(reference pointsystem),同时给出了一种更为简便的坐标变换方法。该融合技术可以有效地应用在对复杂结构的逆向工程中。     

Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization

A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions.     

基于遗传算法的孔群加工路径优化

为提高孔群加工的效率,采用遗传算法对孔群加工空行程路径进行了优化。对遗传算法的实施过程进行了细致的描述,通过实例演示了遗传算法的孔群路径优化性能,并对遗传算法的运行效率及影响因素进行了研究。实验表明,运用遗传算法对孔群路径进行优化能提高孔群加工的效率,减小孔群加工中空行程的消耗;遗传算法运行中可以根据最佳个体的保持时间在一个代中加入活性因子,防止早熟的出现;遗传算法的各个参数对遗传算法的运行有一定的影响。选用合适的运行参数,本程序可应用于工程中,提高少批量孔加工的生产效率。     

遗传算法在函数优化问题中的应用研究

系统地论述了遗传算法在函数优化问题中的应用,提出了基于遗传算法的函数优化问题的通用框架,通过实例的分析及计算,得出较为理想的结果。     

基于改进遗传算法的函数优化及其性能分析

工程应用中,常涉及到最优化问题.对很多问题进行数学建模后,都可以抽象为一个数值函数的优化问题.实践表明,遗传算法求解最优化问题的计算效率很高.提出了一种对简单遗传算法的编码方式、选择算子、交叉算子及变异算子都进行改进的算法,仿真试验表明,改进算法的函数优化计算在搜索效率和收敛速度方面都有很大提高.