改进的遗传算法在复合材料车间计划排产中的应用研究

采用自适应遗传算法使交叉概率和变异概率随种群中个体适应度值的大小进行自动调整,并设计三个体交叉算子保证了子代能够很好地继承父代最优个体的优良特性,根据复合材料车间的生产特点,建立排产的目标函数及适应度函数。通过对遗传算法上述参数的改进,降低算法陷入局部最优解的可能性,大大提高了算法的收敛速度。     

基于混合遗传算法的混合装配线排序问题研究

为使混合装配线有效运作,研究了混合装配线的生产排序问题。以装配线上各种零部件消耗速率均匀化和最小生产循环周期最短为优化目标,描述了多目标排序问题,并建立了优化模型。针对基本遗传算法在求解排序问题时的早熟收敛问题,提出一种改进混合遗传算法。该算法借助模拟退火算法思想对适应度尺度进行调整,使遗传进化初期削弱种群中个体适应度差异,而在遗传进化后期强化种群中个体适应度差异,以提高对最优解的搜索能力。同时,根据个体适应度自动调整遗传操作参数,既保存了种群中的优良个体,又不失个体的多样性。最后通过案例分析验证了算法的有效性。     

基于免疫算法的贝叶斯优化改进算法

贝叶斯优化算法是将贝叶斯网络模型引入到优化算法中而形成的一种新型的优化算法,它可以有效地避免连锁问题,但计算开销很大。为此,将免疫算法与贝叶斯优化算法相结合,利用免疫算法的导向性变异,对贝叶斯网络产生的解进行变异,从而提高种群中个体的适应度,减少贝叶斯网络的构建次数。仿真结果表明,与传统的贝叶斯优化算法相比,基于免疫算法的贝叶斯优化改进算法可以有效地减少计算量,缩短运算时间,并且寻优能力更强,将其应用于图像分割当中,效果较好。     

基于改进遗传算法的自由曲面测量路径优化

为提高三坐标测量机对自由加工曲面的测点检测效率,针对传统遗传算法收敛速度慢且易陷入局部最优解的问题,引入自适应调节机制,从种群个体的适应度分布情况与个体适应度值两个方面实现交叉与变异概率的自适应参数调节,提高了算法效率,降低了早熟概率;采用贪婪交叉算子与贪婪倒位变异算子,加快了算法的收敛速度。实验结果表明,改进的遗传算法能够更高效且优质地完成自由曲面测量路径优化。     

改进的遗传算法在结构动力模型修正中的应用

结构动力模型修正是一个复杂的非线性优化问题,常规优化算法都存在优化效率低或容易进入局部最优的问题。基于微种群遗传算法和模拟退火算法提出了一种改进的微种群遗传算法,算法采用父代参与竞争的联赛选择方式,同时引入模拟退火优选机制实现个体的选择,并使用最优保存策略来保证群体的高适应度和基因的多样性。实例将改进的算法应用到结构动力模型修正问题,结果证明算法在保证修正精度的同时,收敛速度得到明显提高,验证了改进的遗传算法的有效性。     

一种改进遗传算法及在结构优化设计中的应用

针对简单遗传算法中的线性适应度、恒定交叉与变异概率等不能动态地适应整个寻优过程，提出采用非线性适应度与自适应交叉、变异概率的改进遗传算法。以典型的遗传算法测试函数验证改进遗传算法的有效性与可行性，最后将改进遗传算法用于离散变量桁架结构优化设计，计算结果表明改进遗传算法是可行、有效的。     

一种混合智能排样优化算法的应用研究

基于智能优化方法的混合机理,将并行进化机制引入遗传算法和粒子群算法,提出一种混合智能优化排样方法(HGPA)。该算法依据个体适应度值的大小和相似性对整个种群进行合理划分,在每次迭代中,个体适应度值较好的子种群利用遗传算法进化,个体适应度值较差的子种群则利用粒子群算法处理,实现优化方法的优势互补和信息增值。同时通过设置多样性度量标准来控制种群特征信息和搜索空间。在求解不规则件排样问题的算例表明:该算法可平衡控制个体种群进化中的局部寻优和全局搜索,为智能优化的混合机理研究提供了一个新的思路。     

遗传算法中适应度函数的改进术

遗传算法引导搜索的主要依据就是个体的适应度值,因此适应度函数的设计显得尤为重要。本文兼顾保持种群的多样性和算法的收敛性,提出了一种基于指数变换的、指数系数可随进化代数动态调整的非线性适应度函数。以两个典型的测试函数为例,在相同的遗传操作和参数下,分别采用本文提出的适应度函数、线性拉伸变换及一般的指数变换适应度函数进行优化计算,计算结果表明采用提出的新适应度函数能极大地提高算法的优化精度、收敛速度和收敛概率。     

改进粒子群优化BP神经网络短期负荷预测研究

为了提高短期电力负荷预测的准确性,提出了一种改进型粒子群优化BP神经网络预测模型。在改进的粒子群每次迭代过程中求出种群平均适应度值,并将每一粒子适应度值与种群平均适应度值比较,当粒子适应度值劣于种群平均适应度值时,对其空间位置初始化处理,随机生成新的位置,当粒子适应度值优于或等于种群平均适应度时,保持位置不变,通过此种方式,保留了种群中优良粒子,在搜索空间不断缩小的后期拓展了搜索空间,保持了种群多样性,利用改进的粒子群算法优化BP神经网络的初始参数,再将训练样本训练BP神经网络求得最优参数。将此模型应用到河南省某地区短期电力负荷预测中,结果表明此种方法有效提高了预测精度。     

一种基于改进遗传算法的神经网络优化算法研究

遗传算法是目前优化搜索算法中应用比较广泛的一种,但基本遗传算法存在收敛速度慢、易于陷入局部最优等缺点。针对上述问题对遗传算法(GA)的选择算子进行改进,在最优保存策略的基础上将每代种群按照适应度由小到大排序,平均分成前中后3段,按照0.6、0.8、1的比例进行选择;从尾段中随机抽取个体来补足种群由于选择操作而损失的个体;既利用了最优保存策略的全局收敛特性同时也保持了种群的多样性;用改进的遗传算法调整神经网络的权值形成了新的改进遗传算法优化BP神经网络(IGA-BP);通过与选择算子为适应度比例选择算子的GA-BP网络进行比较,结果表明算法改进后缩短了收敛时间同时减少了运行误差;最后将该改进算法应用于水泥回转窑的故障诊断中,验证了算法的可行性。     

离散变量桁架结构拓扑优化的混合遗传算法

为了避免结构拓扑优化过程中杆件和节点的增删带来的计算上的麻烦，在对桁架结构受力分析的基础上，提出一种启发式方法，以快速产生符合机动性要求的拓扑结构形式；然后在既定的拓扑结构形式下采用混合遗传算法——拟满应力遗传算法进行截面优化。该方法通过在遗传算法中嵌入拟满应力算子，同时对基本遗传算法采用最优个体保留、最差个体替换和控制种群个体差异等改进措施，有效提高遗传算法求解的效率和质量。算例结果表明，该方法用于离散变量桁架结构拓扑优化是有效的。     

变量相关情况下基于杂交GA-PSO算法的结构协同优化

为解答实际工程中变量相关情况下的高维小概率失效问题,将子集模拟与重要抽样法结合起来,根据重要抽样的概率密度函数获取的相关变量的样本点来构造中间失效事件,从而将小失效概率问题转化为一条由一系列易于求解的较大条件失效概率的连乘积组成的马尔可夫链(Markov chain,MC),并直接抽取相关样本点来高效模拟结构的可靠性灵敏度。由此创建失效概率对各变量均值、方差(包含相关系数)的可靠性灵敏度最低以及体积最小的多目标优化问题,并提出多目标协同优化的思想,同时,针对可靠性灵敏度作为目标函数因误差导致多目标协同优化难以收敛的问题,提出利用误差的思想与方法;为加速遗传算法(Genetic algorithm,GA)与粒子群优化(Particle swarm optimization,PSO)算法的收敛,提出克隆与进化同时并举的精英策略及相似交配的思想,并用此GA得到的个体与PSO算法杂交,以进一步提高其收敛性;最后,以盾构三级行星减速器的三个行星架为例,运用上述算法对所建数学模型进行求解,结果表明:①所提直接抽取相关样本的MC能很好地模拟出相关变量的可靠性及灵敏度,免除了变量独立化过程反复转换的繁琐;②提出的杂交GA-PSO协同算法较GA与PSO算法有更快的收敛速度,当相关系数为0.7时,可使该行星架的总体积减小7.06%;③证实将可靠性灵敏度作为目标函数时所提利用误差的思想与方法的可行性与正确性。     

贪心遗传算法求解组合优化问题

许多问题最终可以归结为求解一个组合优化问题,GA是求解组合优化问题的一个强有力的工具,但遗传算法在应用中常出现收敛过慢和封闭竞争问题,本文提出贪心遗传算法。该算法的初始种群建立、交叉和变异等过程,都引入贪心选择策略指导搜索;移民操作向种群引进新的遗传物质,克服了封闭竞争缺点。贪心遗传算法可以避免早熟收敛并改进算法的性能,算法搜索起步阶段的效率是非常高的,本文通过TSP问题仿真试验证明了算法的有效性,在较少的计算量下,得到令人满意的结果。     

Optimum design of rotor-bearing system stability performance comparing an evolutionary algorithm versus a conventional method

The main intent of this paper is to formulate, demonstrate, and validate a practical means of implementing an evolutionary optimization technique in a rotor-bearing system. The optimum design of a flexible rotor supported on three-lobe bearings is studied for the optimal performance considering system stability along with other design criteria such as fluid film thickness, power loss, film temperature, and film pressure using the genetic algorithm and the method of feasible directions. The results for different operating speed values obtained and presented in this study are to provide a comparison of these two methods, and to show the potential of the genetic algorithm in optimization of rotor-bearing systems. The genetic algorithm obtained reasonably good results for the objective function and comparable to the method of feasible directions. Thus, the genetic algorithm provides the designer an alternative design optimization approach for rotor-bearing systems.     

Simultaneous topology and size optimization of locomotive structure using multinary genetic algorithms

Structural optimization of a typical locomotive carbody subjected to various operational loads is investigated using a simultaneous topology and size optimization approach. The proposed approach builds on a string representation of the structure in which the presence or absence of a structural element between specific nodes, as well as its cross-section, are represented by integers. Strings representing various structure topologies are then evolved according to a modified Genetic algorithm with improved genetic operators. The efficiency of the proposed model is verified through applying it to optimize the carbody structure of ER24PC locomotive. Operational load cases and performance criteria are adopted from the European standard EN12663. It is shown that the results based on the present optimal design have higher safety factors compared to the original design without a significant increase in weight. Besides, the computational cost of the optimization process is shown to be considerably less than that of the classical binary Genetic algorithm.

     

Computer-based Creativity Enhanced Conceptual Design Model for Non-routine Design of Mechanical Systems

Computer-based conceptual design for routine design has made great strides, yet non-routine design has not been given due attention, and it is still poorly automated. Considering that the function-behavior-structure（FBS） model is widely used for modeling the conceptual design process, a computer-based creativity enhanced conceptual design model（CECD） for non-routine design of mechanical systems is presented. In the model, the leaf functions in the FBS model are decomposed into and represented with fine-grain basic operation actions（BOA）, and the corresponding BOA set in the function domain is then constructed. Choosing building blocks from the database, and expressing their multiple functions with BOAs, the BOA set in the structure domain is formed. Through rule-based dynamic partition of the BOA set in the function domain, many variants of regenerated functional schemes are generated. For enhancing the capability to introduce new design variables into the conceptual design process, and dig out more innovative physical structure schemes, the indirect function-structure matching strategy based on reconstructing the combined structure schemes is adopted. By adjusting the tightness of the partition rules and the granularity of the divided BOA subsets, and making full use of the main function and secondary functions of each basic structure in the process of reconstructing of the physical structures, new design variables and variants are introduced into the physical structure scheme reconstructing process, and a great number of simpler physical structure schemes to accomplish the overall function organically are figured out. The creativity enhanced conceptual design model presented has a dominant capability in introducing new deign variables in function domain and digging out simpler physical structures to accomplish the overall function, therefore it can be utilized to solve non-routine conceptual design problem.     

卫星天线臂结构优化设计的分级遗传算法

为满足刚度大、强度高、质量小的设计要求,本文针对卫星天线臂的结构优化设计提出了分级遗传算法。首先,依据设计与制造要求,将天线臂结构优化设计分解为拓扑构型与杆件尺寸两级优化问题。然后,将单元材料相对密度作为基因,整体结构的相对密度作为染色体,将刚度与质量转化为适应度函数,形成拓扑构型的遗传算法。其次,在保持拓扑构型不变的条件下,将组成拓扑构型的各杆件的剖面面积作为设计变量,杆件结构的质量作为目标函数,形成杆件尺寸优化模型,通过引入遗传算子,形成第二级遗传算法。最后,给出了某卫星天线臂结构优化设计实例,证实了本文分级遗传算法的有效性。     

薄板成形中变压边力优化设计方法

变压边力的最大优点是可以根据成形过程不同阶段的变形特点来设置不同压边力 ,从而可以充分利用板料的成形性能 ,然而如何设计变压边力的形式及大小一直未成定论。T Ohata提出的基于网格法和单纯形法的混合算法在求解约束问题时存在解的可行性和有效性问题 ,针对这种不足提出一种改进的混合优化算法 ,使之适合求解非线性约束优化问题 ,并将这种改进的优化算法与薄板成形数值仿真软件相结合 ,提出应用优化理论和数值仿真来确定薄板成形中的变压边力设计方法。将这种方法应用到一球头柱形杯零件的变压边力方案设计时 ,大大减小了零件局部最大减薄量 ,改善了零件的总体质量     

Design optimization of low-speed axial flow fan blade with three-dimensional RANS analysis

This work presents a numerical optimization procedure for a low-speed axial flow fan blade with polynomial response surface approximation model. Reynolds-averaged Navier-Stokes equations with SST turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. The blade profile as well as stacking line is modified to enhance blade total efficiency, i.e., the objective function. The design variables of blade lean, maximum thickness and location of maximum thickness are selected, and a design of experiments technique produces design points where flow analyses are performed to obtain values of the objective function. A gradient-based search algorithm is used to find the optimal design in the design space from the constructed response surface model for the objective function. As a main result, the efficiency is increased effectively by the present optimization procedure. And, it is also shown that the modification of blade lean is more effective to improve the efficiency rather than modifying blade profile. This paper was presented at the 9^th Asian International Conference on Fluid Machinery (AICFM9), Jeju, Korea, October 16–19, 2007.     

Microrobot based micro-assembly sequence planning with hybrid ant colony algorithm

Robot-based assembly sequence planning plays an important role in product design and has been widely researched in the macro world. But in the micro world, the characteristics of microrobot-based assembly, such as complexity and scaling effects, make the assembly problems much more difficult and seldom researched. In this paper, the microrobot-based micro-assembly sequence planning problem is discussed. The problem is transferred as a combinatorial optimization problem with several matrixes, such as the moving wedge matrix, the microrobot performance matrix, and the sensing matrix. Furthermore, the geometrical and visibility constraints of assembly sequence and evaluation criteria for optimization are given. A particle swarm optimization (PSO) algorithm modified ant colony optimization (ACO) algorithm, called a hybrid PS-ACO, is devised to solve the problem efficiently. The combination of local search and global search of PSO is introduced into the ACO algorithm, which can balance the exploration and exploitation performances of searches. The experimental results have shown that the PS-ACO can solve the micro-assembly sequence planning problem with better convergence performance and optimizing efficiency than basic ACO and GA.