变化环境下汽轮机总装逆调度技术研究

汽轮机总装有严格的交货期,但装配过程中存在物料配送延迟和返工作业等干扰因素,导致原有装配计划无法执行。若频繁地调整装配作业顺序,则会加剧装配作业混乱,降低效率。针对汽轮机总装过程异常干扰影响装配作业计划问题,分析了工人和行车等约束,在满足交货期前提下,以最小化工人成本为目标,以装配工人分配和加班计划作为调整参数,建立多项目汽轮机总装逆调度模型。在此基础上,提出基于粒子群的装配工人动态分配和基于禁忌搜索的加班动态配置相融合的逆调度算法。通过案例测试,同时考虑工人分配与加班计划能够最大程度避免项目延期,同时降低装配成本。     

基于改进粒子群-禁忌搜索算法的FMS布局优化

针对待加工零件(族)的工艺特性已知,且加工设备型号与数量选定的柔性制造系统(FMS)布局问题,以FMS总物流成本最低为优化目标,采用改进的粒子群-禁忌搜索算法进行了布局优化。以实数映射编码的方法和自动换行策略对粒子进行初始化随机编码,并在粒子群迭代过程中引入自适应变异算子对粒子的位置进行随机变异,增加粒子群的多样性,避免陷入局部最优解。将得到的优化结果解码并进行禁忌搜索,提高算法的局部搜索能力和寻优质量。通过实例验证了使用改进粒子群-禁忌搜索算法优化FMS布局的可行性,得到了相比于标准粒子群算法更优的物料搬运总成本和布局方案序列,结果表明该算法可以有效提高求解布局优化问题的精度。     

基于VPS-PSO算法的装配序列规划方法

针对装配序列规划问题的特点,提出一种求解装配序列规划问题的变种群策略-粒子群优化(Various Population Strategy-Particle Swarm Optimization,VPS-PSO)算法。针对粒子群算法容易陷入局部最优的缺点,采用变种群策略,缩短进化停滞时间,提高粒子群算法进化效率,增强算法的寻优能力。并结合装配几何可行性、装配过程连续性、装配工具改变次数3个评价指标构建适应度函数,实现多目标优化。以经编机成圈传动机构装配序列规划实例验证VPS-PSO算法比较PSO算法具有更好的全局搜索能力。     

柔性调度多目标优化的混合粒子群算法研究

研究多目标柔性调度问题,提出了一种基于多目标粒子群优化算法和局域搜索技术相结合的新算法.建立以最大完成时间、机器总负载和最大机器负载为目标函数的多目标数学调度模型.将粒子群算法运用到机器分配子问题;局域搜索技术运用到工序排列子问题,对粒子群算法得到的结果进行再调度.粒子群优化算法的全局搜索能力与局域搜索技术相结合,加快了算法的收敛速度.最后通过与其他算法进行测试比较,验证了该算法的可行性及有效性.     

基于CPSO-BP的刀具寿命预测算法

为了改善刀具寿命预测的精准度,文章在已有的PSO-BP神经网络算法中引入混沌理论,提出了一种基于混沌粒子群算法优化BP神经网络(CPSO-BP神经网络)的刀具寿命预测方法。该方法采用粒子群算法优化网络权值和阈值,通过混沌扰动更新粒子的位置。CPSO-BP神经网络算法既避免了BP神经网络存在的收敛速度慢、易陷入局部最优的缺点,又改善了全局搜索能力,同时,降低了粒子群优化算法造成早熟收敛或停滞的可能性。仿真结果表明:与已有的PSO-BP神经网络算法相比,该文的CPSO-BP神经网络算法用于刀具寿命预测时收敛速度和预测精度均更胜一筹。     

基于支持向量机的船用柴油机装配质量预测

针对船用柴油机整机装配工序复杂、零部件种类多、多工序并行作业难度大,导致装配质量稳定性与一致性差等问题。基于粒子群-支持向量机建模原理,提出一种船用柴油机装配质量预测方法,以提高船用柴油机装配质量。通过对船用柴油机装配质量特性影响因素分析,结合灰色关联和主成分分析原理,筛选出关键装配质量参数作为支持向量机输入变量,利用粒子群优化算法对支持向量机预测模型关键参数进行优化,并结合实际装配参数,对预测模型进行了验证。结果表明,基于粒子群优化支持向量机的装配质量预测方法能够提高预测精度,缩短预测时间,为装配过程控制和异常诊断提供技术支持。     

基于粒子群算法的U型装配线平衡问题研究

针对给定节拍最小化工作站数的第一类U型装配线平衡问题,提出了一种改进的粒子群算法.由于粒子群算法不能直接用于求解离散空间的组合优化问题,故文章采用基于优先权的粒子群算法来求解,通过对任务赋以的权重的大小来选择任务,并具体说明了粒子速度和位置的更新.最后,对大量测试问题集进行了验证,说明了算法的有效性.     

基于多克隆粒子群的无心磨削工艺参数优化

为了选择无心磨削的最佳工艺参数,建立了磨削工艺参数优化模型,并提出了一种多克隆的粒子群优化算法。将克隆复制、克隆交叉、高频变异和克隆选择引入粒子群优化算法中,从而改善基本粒子群算法容易陷入局部极小值、收敛速度慢的缺点。与其他相关算法相比,新算法在进行磨削工艺参数优化时,搜索速度更快、搜索能力更强。     

基于改进粒子群算法的离散制造车间柔性调度优化

针对离散制造生产过程信息复杂、生产计划与作业计划难以均衡等问题,以提高产品质量,降低企业生产成本为目标,建立了面向柔性制造系统的车间调度模型,并设计了一种改进粒子群算法进行离散制造车间柔性调度优化。改进算法惯性权重能够余弦自适应调节,学习因子能够基于惯性权重动态变化。仿真实验结果表明,改进粒子群算法具有较快的收敛速度以及全局寻优能力。柔性车间调度对于缩短产品生产周期,提高生产线的生产效率,降低生产成本,提高企业的经济效益具有重要意义。     

一种考虑物流方式的飞机装配作业单元布局方法

针对飞机装配的装配作业单元布局问题,提出了一种考虑物流方式的装配作业单元布局方法.首先,构建了一个能够实现布局与物流集成设计的数学模型,该模型将飞机装配物流归纳为空间运输和平面运输两种方式,并分别给出了两种物流方式下的物流路径距离计算方法.然后,用序列对表示单元布局的解结构,并采用粒子群算法对模型进行了求解.最后,以飞机中机身壁板装配作业为例,验证了方法的可行性.     

Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.     

A hybrid CHAOS-PSO algorithm for dimensional synthesis of a redundant manipulator based on tracking trajectories without or with singularities

The paper presents a novel hybrid CHAOS-PSO algorithm for dimensional syntheses of redundant manipulators while not following common design principles of arm length or arm length ratio of a human in previous literatures. More specifically, we employ the clamping weighted least-norm (CWLN) method to solve inverse kinematics of the redundant manipulator tracking desired nonsingular or singular trajectories considering joint limits. Thus, an optimal solution of flexible D-H parameters of manipulator is explored in the search space based on evaluations by the defined fitness functions with the proposed algorithm. As an optimization tool in this work, the hybrid CHAOS-PSO algorithm incorporates chaos into the particle swarm optimization (PSO) algorithm as population initialization and global perturbation technique in order to enhance swarm diversity and escape from premature convergence. To demonstrate validity and practicability of the proposed algorithm compared to PSO, simulations of dimensional syntheses of a 7-degree-of-freedom (7-DOF) redundant manipulator with complex structures along the planed nonsingular or singular trajectories are performed.     

基于混合算法的典型调度问题求解研究

文章介绍了粒子群优化的搜索策略与基本算法,然后通过相关的位置和速度定义,构造一种特殊的粒子群优化算法,并将该算法与遗传算法结合,提出用于求解典型调度问题的混合算法.实验表明了该混合算法在求解调度问题的可行性及其优越性.     

基于PSO-ACO融合算法的物流车辆路径优化与控制研究

传统蚁群算法在解决物流配送路径问题时容易出现“早熟”问题,使路径寻找速度和优化结果受到影响。为更合理进行车辆路径调度管理,提出一种粒子群-蚁群相融合的物流配送路径规划算法,该算法充分利用粒子群较强的全局搜索能力和搜索速度快的特点,将得到的次优解转化为蚁群算法中的初始信息素的增量,最后利用蚁群算法的正反馈机制求解问题的精确解。研究结果表明:与单一算法相比,融合算法能快速有效地确定物流配送路径,具有较快的寻优速度和收敛精度,更合理的控制物流配送成本。     

基于优化RBF神经网络挤出机温度压力系统辨识

为了精确在线辨识橡胶复合挤出机控制过程中主要干扰变量与内部耦合关系,更好地实现对挤出机温度压力耦合系统的精准控制,采用RBF神经网络进行系统辨识研究,同时结合PSO算法引入GA算法中编码、杂交、交叉、变异等概念,设计了混合型PSO算法进一步优化RBF神经网络,完成对温度压力耦合系统的精准在线辨识。借助MATLAB软件进行神经网络训练,辨识系统耦合关系,同时与混合型PSO算法优化神经网络权值所辨识的效果进行对比。试验结果表明:采用混合型PSO算法优化RBF神经网络训练效果更佳,可以实现RBF神经网络高精度系统辨识;混合型PSO算法优化RBF神经网络应用于挤出机温度压力控制系统辨识,可以在一定程度上提升系统的辨识精度以及挤出机械的智能化水平。     

A novel artificial immune clonal selection classification and rule mining with swarm learning model

Metaheuristic optimisation algorithms have become popular choice for solving complex problems. By integrating Artificial Immune clonal selection algorithm (CSA) and particle swarm optimisation (PSO) algorithm, a novel hybrid Clonal Selection Classification and Rule Mining with Swarm Learning Algorithm (CS²) is proposed. The main goal of the approach is to exploit and explore the parallel computation merit of Clonal Selection and the speed and self-organisation merits of Particle Swarm by sharing information between clonal selection population and particle swarm. Hence, we employed the advantages of PSO to improve the mutation mechanism of the artificial immune CSA and to mine classification rules within datasets. Consequently, our proposed algorithm required less training time and memory cells in comparison to other AIS algorithms. In this paper, classification rule mining has been modelled as a miltiobjective optimisation problem with predictive accuracy. The multiobjective approach is intended to allow the PSO algorithm to return an approximation to the accuracy and comprehensibility border, containing solutions that are spread across the border. We compared our proposed algorithm classification accuracy CS² with five commonly used CSAs, namely: AIRS1, AIRS2, AIRS-Parallel, CLONALG, and CSCA using eight benchmark datasets. We also compared our proposed algorithm classification accuracy CS² with other five methods, namely: Naïve Bayes, SVM, MLP, CART, and RFB. The results show that the proposed algorithm is comparable to the 10 studied algorithms. As a result, the hybridisation, built of CSA and PSO, can develop respective merit, compensate opponent defect, and make search-optimal effect and speed better.     

An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry

The focus of this research is on a hybrid method combining immune algorithm with a hill climbing local search algorithm for solving complex real-world optimization problems. The objective is to contribute to the development of more efficient optimization approaches with the help of immune algorithm and hill climbing algorithm. The hybrid algorithm combines the exploration speed of immune algorithm with the powerful ability to avoid being trapped in local minimum of hill climbing. This hybridization results in a solution that leads to better parameter values. This research is the first application of immune algorithm to the optimization of machining parameters in turning and also shape design optimization problems in the literature. The results of single-objective benchmark problem, multi-objective disc-brake problem, an automobile shape design optimization problem taken from the literature and case studies for multi-pass turning operation have demonstrated the superiority of the proposed hybrid over the other techniques in terms of solution quality and convergence rates.     

基于混沌PSONN-PID神经网络的热连轧活套系统自适应解耦控制

为改善粒子群优化算法的寻优性能,提出了一种新的算法———混沌粒子群算法。该算法将混沌搜索机制引入到粒子群算法中来增加粒子的多样性,同时采用增加粒子交互性策略及先增后减的惯性权重因子模型来设置惯性权重因子,改善了递减策略中存在的缺陷。将改进后的算法与PID型单神经元相结合,并将其用于热连轧活套解耦控制系统。仿真试验表明：该算法较好地克服了粒子群算法易早熟和陷入局部最优的缺点,为解决活套系统高度张力耦合问题提供了一种新的有效途径。