基于序列二次规划算法的圆柱度误差评定方法

圆柱的形状误差为研究对象,为提高圆柱体形状误差评定精度,增强其理论和工程应用价值,提出一种基于序列二次规划算法的圆柱度误差评定方法。定义了测量点到圆柱面的符号距离函数,建立了圆柱度误差评定的数学模型,应用最小二乘方法将圆柱进行粗定位,将拟合圆柱和测量点进行坐标变换简化了误差评定数学模型,运用运动几何学的知识和序列二次规划(SQP)算法解决了满足最小区域原则的圆柱度评定的优化问题。实验结果表明:提出的圆柱度的评定算法稳定性较好,效率和精度较高,所得到的误差值是有效的。     

混合教与学算法的圆柱度误差评定方法研究

为提高圆柱度评定精度,建立了圆柱度数学模型,将圆柱度误差最小区域求解转化为求目标函数的最小值优化问题。采用二次退火教与学算法对目标函数进行求解,主要包括学生分组排序,"教"阶段与"学"阶段等步骤。针对原始教与学算法求解精度不高,易陷入局部最优等问题,在"教"与"学"两个阶段两次采用模拟退火算法,以一定概率选择较差解来更新最优解,增强算法多样性,从而充分提高计算精度。最后进行求解,并根据终止准则求得计算结果。将该寻优结果与其他方法的评定结果相比较,验证了该算法的可行性及优越性。     

基于改进果蝇优化的机械圆度误差评定研究

针对机械零件圆度误差的最小区域圆法评定问题,提出一种基于改进果蝇优化算法的评定圆度误差新方法。首先根据最小区域圆法圆度误差数学描述的优化模型设计了果蝇种群个体的编码方式以及新型味道浓度判定函数;其次在保持基本果蝇优化算法典型流程的基础上,引入增强搜索和交互学习机制来增强算法的学习效率以及保持种群的多样性;最后采用3个典型的圆度误差评定问题来验证算法性能。实例分析计算表明该方法是可行有效的,其结果具有较高精度且优于传统的评定方法,适用于求解圆度误差的评定优化问题。     

评定圆柱度误差软件的开发

针对圆柱度误差评定的特点，建立了用Madab实现圆柱度误差最小区域法、最小二乘圆柱法、最小外接圆柱法和最大内接圆柱法评定时目标函数数学模型的计算方法。用不同评价方法对圆柱度误差在不同初始值下进行多次评定，并据此编制成相应的软件，用户输入测量数据后，即可同时得到四种方法的评定结果。     

改进教与学算法在圆度误差评定中的应用

为了提高圆度误差的评定精度和计算收敛速度,提出了一种改进教与学算法的圆度误差评定方法。首先,通过圆度误差最小区域原则的数学模型,建立算法的目标函数。其次,在标准教与学算法的基础上,设计了两阶段爬山搜索策略增强局部开发能力,进一步提高算法精度和收敛速度。最后通过三坐标测量的圆度测量数据进行求解验证,并将计算结果与常用的最小二乘法,遗传算法,粒子群算法等进行对比。实例表明,改进教与学算法在圆度误差评定上的计算精度和收敛速度都优于传统算法,体现了其优越性。     

改进天牛须搜索算法在圆度误差评定中的研究

为了提高圆度误差的评定精度和计算收敛速度,提出了一种改进天牛须搜索算法的圆度误差评定方法。首先,通过圆度误差最小区域原则建立了数学模型和目标函数。其次,在标准天牛须算法的基础上,设计了变步长法,进一步提高算法的计算精度和收敛速度。最后通过三坐标测量的圆度测量数据进行求解验证,并将计算结果与常用的最小二乘法和粒子群算法等进行对比。实例表明,改进的天牛须搜索算法在圆度误差评定上的计算精度和收敛速度都优于传统算法,体现了该算法的优越性。     

圆柱度误差的数据处理系统

根据圆柱度误差评定的数学模型,建立了圆柱度误差最小区域法、最小二乘圆柱法、最小外接圆柱法和最大内接圆柱法评定的目标函数,利用MATLAB强大的数学计算功能,开发了网柱度误差的数据处理系统,检测人员只要输入测点的数据,可任意选择评定方法,得到圆柱度误差值。测量示例证明这种方法具有很高的评定精度和很好的实用性。     

基于海鸥算法的圆柱度误差评定方法

圆柱体零件的几何精度直接影响到机械设备的总体性能，而圆柱度误差是圆柱体零件的几何误差之一，对圆柱度误差进行精确测量和评估十分重要。针对最小区域圆柱度误差评定能否达到全局最优的问题，提出了一种基于新型元启发式海鸥算法(SOA)的圆柱度误差评定方法。首先，对圆柱体轮廓要素的提取进行了阐述，并建立了基于最小区域法的圆柱度误差评定模型；然后，介绍了海鸥优化算法中海鸥的位置更新原理、算法的优化准则和算法流程；最后，用Talyrond 585LT圆柱度仪提取了6个圆柱零件的轮廓数据，并进行了评定，通过对比不同种群数的优化结果，找到了最佳种群数，同时将其所得结果与采用遗传算法(GA)所得结果进行了对比。研究结果表明：种群数的选择对海鸥算法的优化结果影响较大，在种群数为30时能达到最优解，其精度比遗传算法高，其运行时间随着种群数的增加而增加。海鸥算法优化过程稳定，在评定最小区域圆柱度误差(MZC)方面有较好的适应性。     

几何变换在圆柱度评定优化算法中的研究

针对利用最小区域方法评定圆柱度时速度慢、算法复杂等问题,采用降维策略进行优化。在原有最小区域法几何模型的基础上利用最小二乘拟合轴线,建立了一种改进的圆柱度评定几何模型。通过对测量数据进行几何变换,将三维圆柱面转化为二维平面,根据最小条件原则将转换后的平面度误差,等效为原始测量数据的圆柱度误差值。经实验验证,在满足精度要求的前提下,圆柱度误差计算速度相较于现有最小区域法提升了约20%。实验结果表面,利用几何变换方法进行误差评定具有实用价值。     

改进蜂群算法及其在圆度误差评定中的应用

针对基本人工蜂群算法(Artificial bee colony algorithm,ABC)的缺点,提出一种改进人工蜂群算法(Improved artificial bee colony algorithm,IABC),并应用于圆度误差最小区域评定中。该改进算法利用信息熵初始化种群,增强种群的多样性,并在引领蜂和跟随蜂搜索阶段,提出一种新的搜索策略,平衡算法的探索与开发能力。详细阐述IABC算法的基本原理与实现步骤,给出圆度误差满足最小包容区域条件的优化目标函数和收益度函数。通过基准测试函数验证IABC算法的有效性和准确性;通过对由三坐标机测得的多组测量数据进行圆度误差评定试验,结果表明IABC算法的评定精度优于最小二乘法、遗传算法以及粒子群算法等其他优化算法,且在求解质量和稳定性上优于ABC算法,验证了IABC算法不仅正确,而且适用于圆度误差的评定优化。     

Optimization of multi-pass turning economies through a hybrid particle swarm optimization technique

Enhancing the performance of manufacturing operations represents a significant goal, especially when cost savings are linked with economies of scale to be exploited. In the area of machining optimization, the selection of optimal cutting parameters subjected to a set of technological constraints plays a key role. This paper presents a novel hybrid particle swarm optimization (PSO) algorithm for minimizing the production cost associated with multi-pass turning problems. The proposed optimization technique consists of a PSO-based framework wherein a properly embedded simulated annealing (SA), namely an SA-based local search, aims both to enhance the PSO search mechanism and to move the PSO away from being closed within local optima. In order to handle the numerous constraints which characterize the adopted machining mathematical model, a constraint violation function integrated with a suitable objective function has been engaged. In addition, a twofold strategy has been implemented to manage the equality constraint between the provided total depth of cut and the number of passes to be performed. Firstly, an accurate problem encoding involving only five cutting parameters has been performed. Secondly, a proper repair procedure that should be run just before any solution evaluation has been engaged. Five different test cases based on the multi-pass turning of a bar stock have been used for comparing the performance of the proposed technique with other existing methods.     

APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOPSCHEDULING PROBLEM

A new heuristic algorithm is proposed for the problem of finding the minimum makespan in the job-shop scheduling problem. The new algorithm is based on the principles of particle swarm optimization (PSO). PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. It combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. Simulated annealing (SA) employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule. By reasonably combining these two different search algorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, is developed. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated by applying it to some benchmark job-shop scheduling problems and comparing results with other algorithms in literature. Comparing results indicate that PSO-based a     

Machine–part cell formation using a hybrid particle swarm optimization

Cell formation (CF) is a key step in group technology (GT). This combinatorial optimization problem is NP-complete. So, meta-heuristic algorithms have been extensively adopted to efficiently solve the CF problem. Particle swarm optimization (PSO) is a modern evolutionary computation technique based on a population mechanism. Since Kennedy and Eberhart invented the PSO, the challenge has been to employ the algorithm to different problem areas other than those that the inventors originally focused on. This paper investigates the first applications of this emerging novel optimization algorithm into the CF problem, and a newly developed PSO-based optimization algorithm for it is elaborated. Forming manufacturing cells lead to process each part family within a machine group with reduction intracellular travel of parts and setup time. A maximum number of machines in a cell and the maximum number of cells are imposed. Some published results in various problem sizes have been used as benchmarks to assess the proposed algorithm. Overall, the advantages of the proposed PSO are that it is rapidly converging towards an optimum, there are fewer parameters to adjust, it is simple to compute, it is easy to implement, it is free from the complex computation, and it is very efficient to use in CF with a wide variety of machine/part matrices.     

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.     

基于坐标测量机和拟粒子群进化算法的圆柱度误差检测与评定

建立了任意位置下基于坐标测量机检测的圆柱度误差最小区域解的数学模型,提出了采用拟粒子群进化算法求解最小区域圆柱度误差新方法。该算法使用实数编码,由拟随机Halton序列产生粒子的初始位置和速度,基于浓缩因子法修改粒子的速度。为了验证算法的有效性,对文献中测量数据采用提出的方法进行圆柱度误差计算并将结果与多种算法计算结果进行比较,同时在加工中心加工大量轴类零件,使用三坐标测量机对零件进行实测,应用该进化算法计算最小区域圆柱度误差并与三坐标测量机给出的结果进行比较。实验结果均证实了提出的方法不仅优化速度快、计算精度高,而且算法简单,需设置参数少,便于推广应用。     

Integrating theory of constraints and particle swarm optimization in order planning and scheduling for machine tool production

Order planning and scheduling has become a significant challenge in machine tool enterprises, who want to meet various demands of different customers and make full use of existing resources in enterprises simultaneously. Based on the Theory of Constraints, a three-stage order planning and scheduling solution is proposed to optimize the whole system performance with bottleneck resources' capability as the constraints. After the identification of bottleneck resources, multicriteria priority sequencing is made with order per-contribution rate, order delivery urgency, and customer importance as the evaluation criteria, and the evaluation result deduced from the ideal point function can decide the production mode of all orders and products. Then, a PSO-based multiobjective optimization model is set up with minimizing bottleneck machines' makespan and minimizing total products' tardiness as the two objectives. Finally, the proposed solution is applied in one machine tool enterprise by integrating into Baosight MES (Manufacturing Execution System) system. In addition, some comparisons are carried out to evaluate the proposed PSO optimization method. The comparison with actual report shows that PSO can satisfy enterprise's needs better than before; the comparisons with genetic algorithm and ant colony optimization algorithms indicate that PSO is more effective than the others because of its faster convergence rate.     

A hybrid particle swarm optimization algorithm for a no-wait flow shop scheduling problem with the total flow time

This paper proposes a particle swarm optimization (PSO) algorithm based on memetic algorithm (MA) that hybridizes with a local search method for solving a no-wait flow shop scheduling problem. The main objective is to minimize the total flow time. Within the framework of the proposed algorithm, a local version of PSO with a ring-shape topology structure is used as global search. In addition, a self-organized random immigrant's scheme is extended into our proposed algorithm in order to further enhance its exploration capacity for new peaks in search space. The experimental study over the moving peaks benchmark problem shows that the proposed PSO-based MA is robust. Finally, the analysis of the computational results and conclusion are given.     

PSO优化的能耗均衡WSNs路由算法

无线传感器网络(WSNs)节点能量和计算能力有限,所以WSNs路由设计的重要目标是降低节点能源损耗。提出PSO优化的能耗均衡WSNs路由算法,用路径探测包收集链路状态信息,将路由优化计算在Sink节点处进行,用PSO进行路由优化,减少了节点负担。算法对PSO的运算规则结合路由问题进行了重新定义,在设计适用度函数时,考虑了节点的剩余能量,以达到能耗均衡。当网络有Qo S需求时,只需在路由探测包和适应度函数上加入相应参数即可,易于扩展。最后用仿真对算法的优越性进行了验证。     

具有二重机制的生物地理优化算法及圆柱度 误差评定研究*

生物地理学优化算法(Biogeography-base optimization, BBO)是一种新型的智能算法,因其参数少、易于实现等优点而受到学界的广泛关注和研究,并显示出了广阔的应用前景。为了提高算法的优化性能,对BBO算法提出一种改进。改进的算法在将差分优化算法(Differential evolution, DE)中的局部搜索策略同BBO算法中的迁移策略相结合的基础上,针对迁移算子和变异算子分别做出改进,并通过基准函数的测试证明了改进后的算法在迭代过程中种群进化、寻优能力以及算法的收敛性能得到进一步提升。尝试将改进了的生物地理学优化算法应用于圆柱度误差评定。依据国家标准,结合最小区域法,以圆柱度误差数学模型为目标函数,该算法实现了误差评定优化求解。通过该寻优结果与其他方法的评定结果的比较,验证了该种算法的可行性和正确性及其优越性。     

Method for cylindricity error evaluation using Geometry Optimization Searching Algorithm

According to the geometrical characteristics of cylindricity error, a method for cylindricity error evaluation using Geometry Optimization Searching Algorithm (GOSA) has been presented. The optimization method and linearization method and uniform sampling could not adopt in the algorithm. The principle of the algorithm is that a hexagon are collocated based on the reference points in the starting and the end measured section respectively, the radius value of all the measured points are calculated by the line between the vertexes of the hexagon in the starting and the end measured section as the ideal axes, the cylindricity error value of corresponding evaluation method (include minimum zone cylinder method (MZC), minimum circumscribed cylinder method (MCC) and maximum inscribed cylinder method (MIC)) are obtained according to compare, judgment and arranged hexagon repeatedly. The principle and step of using the algorithm to solve the cylindricity error is detailed described and the mathematical formula and program flowchart are given. The experimental results show that the cylindricity error can be evaluated effectively and exactly using this algorithm.