一种求解车间调度的混合免疫遗传算法

为了克服传统免疫遗传算法(IGA)在车间调度问题上易陷入局部最优的缺点,将免疫遗传算法(IGA)与模拟退火算法(SA)进行了结合,提出一种应用于车间作业调度的混合免疫遗传算法。为了有效的提高免疫遗传算法收敛速度和避免算法陷入局部最优解,此算法设计了一种基于适应度和浓度的自适应精英保留策略且重新设置了变异算子,即将变尺度变异和自适应变异算子进行了融合。最后利用"Muth and Thompson"基准问题进行仿真实验,验证了该算法在JSP问题中的高效性和可行性。     

基于混合遗传算法的Job Shop问题研究与实现

遗传算法具有良好的全局搜索能力,在调度问题中得到了广泛的应用。通过对遗传算法进行改进,可以有效避免在求解过程中容易陷入局部最优域的问题。通过采用混合遗传算法,即将模拟退火算法与遗传算法结合,在种群更迭过程中引入了模拟退火操作来求解Job Shop问题。通过实验验证了混合遗传算法的特性,最终算法显示出了遗传算法较好的搜索能力和模拟退火避免过收敛的特性,改进了收敛性能。系统的运行结果满足调度要求,实现了良好的有效性和实用性。     

混合遗传算法在桥式起重机结构优化中的应用

针对工程中的变量离散化问题，提出了一种将遗传算法和模拟退火算法相结合的混合算法。该算法发挥了遗传算法和模拟退火算法的优越性，避免了遗传算法的早熟收敛问题，增强了算法的全局收敛性，并提高了算法的收敛速度。通过对桥式起重机金属结构进行优化，其结果与MDOD和改进遗传算法2种的结果进行比较，表明此算法能够很好处理工程离散化问题。     

改进混合遗传算法的作业车间调度研究

针对作业车间调度问题,结合遗传算法和模拟退火算法的优点,提出一种改进混合遗传模拟退火算法.首先,加入自适应调整的遗传操作以及精英替换策略,并对模拟退火算子进行改进,增加记忆功能以防止遗失当前最优解;然后,对于当前状态,采用多次搜索策略代替单次比较方式,以接受区域内的最优状态;其次,加入升温策略,从而激活各个状态的接受概率;最后,将提出的改进混合遗传模拟退火算法应用于FT系列和LA系列标准算例,并与多种智能算法进行比较,验证了该算法的有效性和高效性.改进的策略不仅可以避免算法陷入局部最优,同时加快了算法收敛速度,提高了算法的寻优能力.     

超磁致伸缩执行器位移模型的参数辨识

准确辨识模型参数是提高超磁致伸缩执行器位移控制精度的关键,针对单一算法难以实现对超磁致伸缩磁滞非线性模型参数准确识别的问题,将遗传算法与模拟退火算法融合,首先利用遗传算法的快速搜索能力得到一个较优群体,再利用模拟退火算法的突跳能力对整个群体进行优化调整,并在算法中引入最优保留策略和动态步长搜索方法,提出一种改进的遗传模拟退火算法,并将其应用于对超磁致伸缩执行器位移磁滞非线性模型参数辨识。该算法兼具遗传算法和模拟退火算法的优点,既有较快的收敛速度,又提高了辨识精度和最优解质量。通过试验验证,超磁致伸缩棒伸长量的模型计算结果与测量值符合程度较好,平均相对误差为3.85%,该方法能方便有效地辨识模型参数。     

基于优化模拟退火算法的航天薄壁结构件车间布局优化

针对某航天设备生产车间生产效率低,物流成本高等问题,首先通过相关算法的研究对比,提出算法混合的一般性原则,并得出:对于遗传算法和模拟退火算法,能够将两个算法的优缺点互补,即遗传算法易收敛特性,模拟退火算法能够较少受到初始解影响,接着基于该原理设计了混合算法。该算法能够继承两个算法的优点,同时避免了两个算法的缺点,即遗传算法容易局部收敛,模拟退火算法收敛时间较长、速度慢等缺陷,并在多行布局环境下对算法的收敛性、最优解进行验证。最后通过Plant Simulation仿真软件,以某航天零件加工车间为研究对象,进一步对算法的优越性进行仿真验证。仿真结果证明,优化后的车间布局能提升车间生产效率。     

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。     

机械手时间最优轨迹规划方法研究

提出一种基于模糊遗传算法的机械手时间最优轨迹规划方案。该方案对简单遗传算法进行了改进，将模糊原理应用于遗传算法，形成了模糊遗传算法，对遗传算法中的交叉概率及变异概率进行模糊控制，提高了算法的收敛速度，有效地避免了初期收敛的发生，在进行时间最优轨迹规划时，综合考虑了机械手的运动学与动力学特性，采用罚函数方法来处理力矩约束。经仿真研究表明，该方法简单实用，适用于大范围空间的轨迹规划，克服了传统的非线性规划方法容易陷入局部极小的不足。     

基于改进自适应遗传算法的柔性作业车间调度问题研究

为了克服遗传算法在解决柔性作业车间调度问题中收敛速度慢、易陷入局部最优的缺陷,根据柔性作业车间调度问题的特点,提出一种具有自适应交叉概率与变异概率的改进自适应遗传算法。将交叉概率与个体适应度值相关,将变异概率同时与个体适度值与进化代数相关,采用有效的编码、解码机制,引入精英保留策略。通过对两个基准问题进行仿真分析,验证了改进自适应遗传算法的有效性。     

基于自适应遗传算法的大型关重件车间布局优化

针对大型关重件生产车间的多行布局问题,建立了同时考虑物流费用与物流时间的多目标优化数学模型,运用了一种改进的自适应遗传算法,其交叉概率和变异概率可随群体的适应度自动改变,使算法避免陷入局部最优;加入精英保留策略,使每代中的最优个体都能够得到保留,避免交叉和变异操作遗失全局最优解。最后结合某船用曲轴车间实例,分别运用该算法和标准遗传算法对车间布局模型进行计算,通过数据和性能分析表明该算法有效的解决了遗传算法易陷入早熟及收敛速度慢的问题;优化后的车间物流费用及物流时间减少约35%,证明该方法在车间布局中的可行性和有效性。     

Optimisation of multi-pass milling using genetic algorithm and genetic simulated annealing

The selection of optimal machining parameters plays an important part in computer-aided manufacturing. The optimisation of machining parameters is still the subject of many studies. Genetic algorithm (GA) and simulated annealing (SA) have been applied to many difficult combinatorial optimisation problems with certain strengths and weaknesses. In this paper, genetic simulated annealing (GSA), which is a hybrid of GA and SA, is used to determine optimal machining parameters for milling operations. For comparison, basic GA is also chosen as another optimisation method. An application example that has previously been solved using geometric programming (GP) method is presented. The results indicate that GSA is more efficient than GA and GP in the application of optimisation.     

基于模拟退火遗传算法的拆卸路径规划

针对现存拆卸路径规划图论方法中的组合爆炸问题和遗传算法的早熟现象,给出模拟退火和遗传算法相结合的拆卸路径规划方法.在拆卸路径规划模型中,以拆卸效率最优作为优化目标,给出了算法流程.该算法对拆卸路径的全面寻优提供了一种新的思路.通过实例验证该算法的可行性,最后提出了进一步的研究方向.     

基于模拟退火遗传算法的PID控制器参数优化

针对遗传算法存在容易早熟的不足,将模拟退火算法融合到遗传算法中,建立了模拟退火遗传算法,并将其应用于PID控制器的参数优化.结果表明,将模拟退火算法融合到遗传算法中是有效的,基于模拟退火遗传算法的PID控制器参数优化是可行的.     

基于混合遗传算法的桥梁颤振导数识别

提出一种基于混合遗传算法识别桥梁颤振导数的方法,该混合遗传算法将模拟退火算法与遗传算法相结合,充分利用遗传算法的并行运算机制以及模拟退火算法的强局部搜索机制,具有较强的鲁棒性和全局收敛性,从而保证识别的精度。该方法通过对自由振动时程曲线进行时域拟合,识别出自由振动表达式中的各参数,进而确定系统的等效刚度矩阵和等效阻尼矩阵,并同时得到8个颤振导数。数值仿真算例表明该方法的可靠性,风洞试验表明该方法有效、可行。     

Machine-Cell Grouping in Cellular Manufacturing Systems Using Non-traditional Optimisation Techniques - A Comparative Study

In this paper, the machine-cell grouping problem is considered with the objective of minimising the total moves and minimising the cell load variation. We first review the literature on machine-cell grouping involving meta-heuristics. Then we integrate the most powerful non-traditional algorithms, genetic algorithm (GA) and simulated annealing (SA) with the most robust computer programming language "C", for cell grouping. The computational results obtained by applying the genetic algorithm and simulated annealing are compared for their efficiency in solving the machine-cell grouping problems.     

Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm

The increased use of flexible manufacturing systems (FMS) to efficiently provide customers with diversified products has created a significant set of operational challenges. Although extensive research has been conducted on design and operational problems of automated manufacturing systems, many problems remain unsolved. In particular, the scheduling task, the control problem during the operation, is of importance owing to the dynamic nature of the FMS such as flexible parts, tools and automated guided vehicle (AGV) routings. The FMS scheduling problem has been tackled by various traditional optimisation techniques. While these methods can give an optimal solution to small-scale problems, they are often inefficient when applied to larger-scale problems. In this work, different scheduling mechanisms are designed to generate optimum scheduling; these include non-traditional approaches such as genetic algorithm (GA), simulated annealing (SA) algorithm, memetic algorithm (MA) and particle swarm algorithm (PSA) by considering multiple objectives, i.e., minimising the idle time of the machine and minimising the total penalty cost for not meeting the deadline concurrently. The memetic algorithm presented here is essentially a genetic algorithm with an element of simulated annealing. The results of the different optimisation algorithms (memetic algorithm, genetic algorithm, simulated annealing, and particle swarm algorithm) are compared and conclusions are presented .     

柔性设计任务协同调度算法

指出柔性多任务协同调度是一个NP难题,并分析了协同任务调度在协同设计系统中的重要性,提出一种基于遗传算法和模拟退火算法的混合算法,利用该算法实现设计任务的选择。设计二维结构的矩阵编码,并基于这种编码方式,提出行算子与列算子,融入约束条件,采用列交叉算子与列变异算子;为了加快群体的收敛性,采用精英保留策略;此外引入灾变算子,以保证群体的多样性;在个体生成过程中,考虑能力等相关因素对设计效果的影响,在解码过程中实现任务的时间调度与优化,并设计解码算法。通过实例仿真分析,所提出的混合遗传算法收敛速度快,寻优能力强。     

Heuristic search algorithms for lot streaming in a two-machine flowshop

The objective of this paper is to propose and evaluate heuristic search algorithms for a two-machine flowshop problem with multiple jobs requiring lot streaming that minimizes makespan. A job here implies many identical items. Lot streaming creates sublots to move the completed portion of a production lot to second machine. The three heuristic search algorithms evaluated in this paper are Baker’s approach (Baker), genetic algorithm (GA) and simulated annealing (SA) algorithm. To create neighborhoods for SA, three perturbation schemes, viz., pair-wise exchange, insertion and random insertion are used, and the performance of these on the final schedule is also compared. A wide variety of data sets is randomly generated for comparative evaluation. The parameters for GA and SA are obtained after conducting sensitivity analysis. The genetic algorithm is found to perform well for lot streaming in the two-machine flowshop scheduling.     

Optimal design of nonlinear squeeze film damper using hybrid global optimization technique

The optimal design of the squeeze film damper (SFD) for rotor system has been studied in previous researches. However, these researches have not been considering jumping or nonlinear phenomena of a rotor system with SFD. This paper represents an optimization technique for linear and nonlinear response of a simple rotor system with SFDs by using a hybrid GA-SA algorithm which combined enhanced genetic algorithm (GA) with simulated annealing algorithm (SA). The damper design parameters are the radius, length and radial clearance of the damper. The objective function is to minimize the transmitted load between SFD and foundation at the operating and critical speeds of the rotor system with SFD which has linear and nonlinear unbalance responses. The numerical results show that the transmitted load of the SFD is greatly reduced in linear and nonlinear responses for the rotor system.     

An intelligent hybrid meta-heuristic for solving a case of no-wait two-stage flexible flow shop scheduling problem with unrelated parallel machines

This paper addresses the problem of no-wait two-stage flexible flow shop scheduling problem (NWTSFFSSP) considering unrelated parallel machines, sequence-dependent setup times, probable reworks and different ready times to actualize the problem. The performance measure used in this study is minimizing maximum completion time (makespan). Because of the complexity of addressed problem, we propose a novel intelligent hybrid algorithm [called hybrid algorithm (HA)] based on imperialist competitive algorithm (ICA) which are combined with simulated annealing (SA), variable neighborhood search (VNS) and genetic algorithm (GA) for solving the mentioned problem. The hybridization is carried out to overcome some existing drawbacks of each of these three algorithms and also for increasing the capability of ICA. To achieve reliable results, Taguchi approach is used to define robust parameters' values for our proposed algorithm. A simulation model is developed to study the performance of our proposed algorithm against ICA, SA, VNS, GA and ant colony optimization (ACO). The results of the study reveal the relative superiority of HA studied. In addition, potential areas for further researches are highlighted.