实数编码遗传算法的缺陷分析及其改进

首先分析了一个典型的遗传算法处于某一阶段时各参数间的制约关系。指出由于实数编码遗传算法中交叉与变异操作改造个体时,是通过改变构成个体的部分或全部实数（而不是位）实现的,造成了个体转移矩阵（与位编码方式相比）呈现出特殊的模式,而正是这种特殊模式,使实数编码遗传算法更易于失效。基于这一结论,提出了采用次级遗传算法改造实数编码遗传算法中交叉、变异算子的改进方法。该次级遗传算法将解空间各维上的实数增量组成基因串,旨在提高交叉、变异中个体转移方向与优化对象的符合程序。实验结果表明,改进后的算法在解决文中所涉及的优化问题时,性能明显优于原算法。由于改进后的算法是一个纯粹的遗传算法,因此能更好地保留遗传算法的各项优点。     

基于进化策略的平面度误差评定

本文针对平面度误差评定的特点,提出了将进化策略应用于平面度误差评定中的算法。该算法基于实数编码,采用（μ＋λ）选择策略和高斯变异算子,即父代种群参与竞争,算法简单、鲁棒性强、优化效率高;同时给出进化策略评定平面度误差时目标函数的计算方法。最后,通过不同评价方法对实测平板的平面度误差进行评定,结果证明该方法不仅能快速找到最小区域解,而且计算结果的稳定性好,易于在其他形状误差评定中推广使用。     

浮点数编码改进遗传算法在平面度误差评定中的研究

随着智能制造系统的迅猛发展,应用元启发模式计算方法快速、准确地求解平面度误差值凸显出重大现实意义。为进一步提高平面度误差计算精度,研究了一种基于浮点数编码的改进遗传算法,在原有遗传算法的交叉变异基础之上,引入模拟退火思想,建立最小包容区域法的数学模型,通过计算机仿真获得了最佳适应度收敛曲线和平均适应度收敛曲线,优化结果表明相比传统遗传算法,平面度误差计算精度提高了33.67%。本算法采用浮点数编码、三段式交叉、转轮式选择和最优保存策略,借助模拟退火算法的局部搜索优势,提升了算法的整体性能,且更便于计算机编程,可进一步推广应用到智能测量仪器的其他高精度形位尺寸计算问题领域。     

基于遗传算法和分割逼近法精确计算复杂曲面轮廓度误差

在超精密复杂零件加工与检测技术中,高精度轮廓度误差的评估方法一直是一个研究重点。在分析研究现状基础上,阐明精确计算复杂曲面轮廓度误差需要解决的关键问题,阐述复杂曲面轮廓度误差定义,建立复杂曲面轮廓度误差的数学模型。在分析基于NURBS描述复杂曲面特点基础上,提出分割逼近法计算测点到曲面的最小距离快速简便算法。分析传统遗传算法存在计算精度与编码长度、计算工作量之间的矛盾,提出改进型归一化实数编码的遗传算法,建立相应的交叉算子和变异算子,确立分割逼近法和归一化实数值编码遗传算法相结合计算复杂曲面轮廓度误差的具体步骤。该算法易于计算机实现,且计算精确度高,可以达到任意给定的精度,非常适用于三坐标测量机。     

改进蜂群算法在平面度误差评定中的应用

为了准确快速评定平面度误差,提出将改进人工蜂群( MABC)算法用于平面度误差最小区域的评定.介绍了评定平面度误差的最小包容区域法及判别准则,并给出符合最小区域条件的平面度误差评定数学模型.叙述了MABC算法,该算法在基本人工蜂群算法( ABC)模型的基础上引入两个牵引蜂和禁忌搜索策略.阐述了算法的实现步骤,通过分析选用两个经典测试函数验证了MABC算法的有效性.最后,应用MABC算法对平面度误差进行评定,其计算结果符合最小条件.对一组测量数据的评定显示,MABC算法经过0.436 s可找到最优平面,比ABC算法节省0.411 s,其计算结果比最小二乘法和遗传算法的评定结果分别小18.03μm和6.13 μm.对由三坐标机测得的5组实例同样显示,MABC算法的计算精度比遗传算法和粒子群算法更有优势,最大相差0.9 μm.实验结果表明,MABC算法在优化效率、求解质量和稳定性上优于ABC算法,计算精度优于最小二乘法、遗传算法和粒子群算法,适用于形位误差测量仪器及三坐标测量机.     

一种求解Job-shop调度问题的遗传局部搜索算法

利用遗传局部搜索算法求解了作业车间调度问题,遗传算法中的染色体编码采用基于工序的编码,并用插入式贪婪解码机制将染色体解码至主动调度。为了克服传统遗传算法易于早熟收敛的缺点,设计了一种改进的优先操作交叉IPOX操作和子代产生模式的遗传算法。对于遗传算法每个染色体个体,使用基于N6邻域结构的局部搜索进一步使它们得到改善。利用所提出的混合遗传算法求解基准问题,验证了算法的有效性。     

基于改进遗传算法的最小二乘法的应用

为了克服基本遗传算法参数较多时编码表示冗长、烦杂以及实数编码等寻优效果的不足,提出了一种改进编码的遗传算法——矩阵编码遗传算法.它是在遗传算法大的框架不变的情况下仅改变其编码,即遗传编码改变的同时,相应的遗传操作,包括交叉、变异等都要随之变化.除此之外,适应度函数和计算也要变化.最小二乘法是系统辨识常用方法之一,将改进...     

平面度误差评定的快速精确算法研究

根据最小二乘法、基于遗传算法的平面度误差评定方法以及最小包容区域法的算法特点,提出一种可以快速、精确评定平面度误差的算法。该算法解决了初始参数寻优范围大,影响计算效率的问题,是一种可兼顾计算速度与精确性的平面度误差评定方法。     

改进差分进化算法在大型工件平面度评定中的应用

生产线上检测大型复杂工件平面度误差时,存在检测面积较大、数据量较多的问题,为了提高检测效率及精度,采用优化算法提高其平面度误差评定速度。提出将差分进化(DE)算法应用在其平面度误差的评定中,并提出将粒子群(PSO)算法的优化方法融入差分进化算法的框架,改进变异操作以提高标准DE算法的收敛速度。介绍了大型工件平面度误差评定采用最小区域法的数学模型,阐述了改进的DE算法的原理和实现步骤,最后以叉车外壁板为例,通过对外壁板平面度误差的评定以验证算法的收敛速度与精度。结果表明,改进的DE算法在大型工件平面度误差评定中收敛结果稳定,误差接近于0;精度较遗传算法提高36.83%;收敛速度较遗传算法提高58.33%,较标准的DE算法提高28.57%。可以很好地应用在大型工件平面度误差检测中,提高检测效率。     

基于实数遗传算法的几何约束求解

提出一种用于几何约束求解改进的实数编码遗传算法 ,它在一般实数编码遗传算法基础上 ,增加基因初始种群的人工选择以及对交叉、变异算子的改进 ,并通过添加外循环的过程 ,大大提高遗传算法的求解能力 ,最后通过变量区间的二分和组合 ,求出约束方程组的全部解     

Product configuration optimization using a multiobjective genetic algorithm

Product configuration is one of the key technologies in the environment of mass customization. Traditional product configuration technology focuses on constraints-based or knowledge-based application, which makes it very difficult to optimize design of product configuration. In this paper, an approach based on multiobjective genetic algorithm is proposed to solve the problem. Firstly, a configuration-oriented product model is discussed. A multiobjective optimization problem of product configuration according to the model is described and its mathematical formulation is designed. Secondly, a multiobjective genetic algorithm is designed for finding near Pareto or Pareto optimal set for the problem. A matrix method used to check constraint is proposed, and the coding and decoding representation of the solution are designed, then a new genetic evaluation and select mechanism is proposed. Finally, performance comparison of the proposed genetic algorithm with three other genetic algorithms is made. The result shows that the proposed genetic algorithm outperforms the other genetic algorithms in this problem.     

RESEARCH ON THE MINIMUM ZONE CYLINDRICITY EVALUATION BASED ON GENETIC ALGORITHMS

A genetic algorithm (GA)-based method is proposed to solve the nonlinear optimization problem of minimum zone cylindricity evaluation. First, the background of the problem is introduced. Then the mathematical model and the fitness function are derived from the mathematical definition of dimensioning and tolerancing principles. Thirdly with the least squares solution as the initial values, the whole implementation process of the algorithm is realized in which some key techniques, for example, variables representing, population initializing and such basic operations as selection, crossover and mutation, are discussed in detail. Finally, examples are quoted to verify the proposed algorithm. The computation results indicate that the GA-based optimization method performs well on cylindricity evaluation. The outstanding advantages conclude high accuracy, high efficiency and capabilities of solving complicated nonlinear and large space problems.     

基于遗传算法的圆度误差评估

将遗传算法应用于圆度误差的评定.首先简介了误差评定背景和遗传算法及其特点.然后根据尺寸和公差的数学定义^[1]给出满足最小区域条件的圆度公差评定的数学模型和适应度函数.接着,以最小二乘解作为初始值,对圆度误差的遗传优化过程进行了详细的论述.最后用实例对算法进行验证.优化过程和实验结果显示了遗传算法在解决形状公差的评定这类非线性问题的优越性,通过并行搜索能最大限度地保证解的全局最优,计算精度高、效率高,且易于理解和实现.     

面向大规模定制的装配线优化调度研究

针对大规模定制生产模式下汽车装配线调度存在的问题,提出一种多目标优化调度的方法,设计了相应的目标函数。提出一种多目标遗传算法,设计了相应的编码、选择和交换方案,在算法实现中对精英策略和选择机制进行了改进。仿真实验说明该算法可行有效,优于VEGA、PGA和NPGA等其他遗传算法。     

Multi-objective optimization and evaluation method of modular product configuration design scheme

Product configuration is one of the key technologies for mass customization. Traditional product configuration optimization targets are mostly single. In this paper, an approach based on multi-objective genetic optimization algorithm and fuzzy-based select mechanism is proposed to solve the multi-objective configuration optimization problem. Firstly, the multi-objective optimization mathematical model of product configuration is constructed, the objective functions are performance, cost, and time. Then, a method based on improved non-dominated sorting genetic algorithm (NSGA-II) is proposed to solve the configuration design optimization problem. As a result, the Pareto-optimal set is acquired by NSGA-II. Due to the imprecise nature of human decision, a fuzzy-based configuration scheme evaluation and select mechanism is proposed consequently, which helps extract the best compromise solution from the Pareto-optimal set. The proposed multi-objective genetic algorithm is compared with two other established multi-objective optimization algorithms, and the results reveal that the proposed genetic algorithm outperforms the others in terms of product configuration optimization problem. At last, an example of air compressor multi-objective configuration optimization is used to demonstrate the feasibility and validity of the proposed method.     

基于遗传算法的摆动活齿传动多目标优化设计

本文建立了摆动活齿传动的优化设计数学模型,提出了优化设计方法。为求得全局最优解,引进了遗传算法。文中对遗传算法提出了一些改进,并用改进前、后的遗传算法分别对该模型进行了优化仿真计算,结果表明改进后的遗传算法优于改进前的遗传算法。     

基于进化思想的汽车造型优化设计方法研究

结合遗传算法等工程方法和交互式评价等设计方法,提出一种整合了工程方法和造型方法的汽车造型优化设计方法。方法整体以进化思想为指导,首先将汽车造型设计问题映射到数学模型上,利用遗传算法生成新设计方案,并以多目标设计空间来约束生成的设计方案,再以目标车型的造型意象为标杆,采用交互式评价方式评价输出的设计方案,并将评价结果作为适应度代入遗传算法中继续迭代直至设计方案符合设计的迭代终止条件,最终形成一个综合解决多方问题的汽车造型多目标优化设计方法。以某小型两厢车车型为例,初步证明了该方法的合理性和可行性。该方法可以推广到汽车造型多目标优化概念辅助生成和汽车造型评价等领域。     

Constrained minimization utilizing GA based pattern recognition of immune system

The immune system has pattern recognition capabilities based on reinforced learning, memory and affinity maturation interacting between antigens and antibodies. The paper deals with the adaptation of artificial immune system into genetic algorithm based design optimization. The present study utilizes the pattern recognition from the immune system and the evolution from genetic algorithm. The basic idea is derived from the fact that designs should be distinguished whether they are usable/feasible or infeasible and should be improved towards the optimal solution. For the expression of design solutions, binary coded strings are used to represent antigens and antibodies in artificial immune system and chromosomes in genetic algorithm. The paper discusses the procedure of constrained optimization that does not rely on any detailed mathematical formulation for constraint handling. A number of mathematical function minimization problems are examined for verification, and practical engineering optimization problems including inequality constraints are explored to support the proposed strategy.     

Reliability-based total cost of ownership approach for supplier selection using cuckoo-inspired hybrid algorithm

In today’s highly competitive environment, an effective supplier selection process is very important to the success of any manufacturing organization. A number of models and techniques have been developed to deal with supplier selection and evaluation methods. Traditional supplier selection methods are often based on the quoted initial price, which ignores the significant direct and indirect costs associated with quality, usage, maintenance, and service cost. This paper will look at the reliability-based total cost of ownership (RBTCO) approach which accounts both direct and indirect costs, as applied to the supplier selection process. The mathematical formulation of RBTCO for supplier selection problem fits into the nonlinear integer programming problem, which belongs to the NP-hard category. In this paper, a recently developed meta-heuristic optimization algorithm, cuckoo search (CS) hybridized with well-known genetic algorithm called HCSGA is proposed to solve the supplier selection problem. By embedding the genetic operators in standard CS, the balance between the exploration and exploitation ability further improved and more search space are observed during the algorithms’ performance. The computational test results show that the proposed hybrid algorithm significantly improves the original cuckoo search algorithm for small and larger sized problem instances.     

基于遗传算法的球度误差评定

首先对球度公差评定问题进行了综述.然后根据圆度公差的数学定义,引申提出球度公差最小区域条件下的评定模型,并给出遗传算法的适应度函数.随后给出算法实现中的中的关键问题.最后用实例对算法进行了检验,计算结果表明基于遗传算法的球度误差优化算法不仅符合最小区域的条件,而且易于理解和实现,能够获得全局最优解,保证了高精度、高效率.