遗传算法在快速成形轮廓路径规划中的应用

以减少层片扫描中的空程运行时间为轮廓路径优化的目标,以轮廓路径与经典旅行商问题之优化的共性和不同为比对,在采用遗传算法实现快速成形轮廓路径的优化中,将轮廓路径的特点灵活应用到该算法的各个步骤中,有效地缩短了扫描路径中的空程距离,从而有利于提高成形速度和成形质量．     

约束优化问题的混合遗传算法研究

如何处理约束条件与增强局部搜索能力是遗传算法用于非线性约束优化问题的线性约束优化问题的不足,提出了一种基于模拟退火算法与外点法的混合遗传算法,对于不满足约束条件的解用外点罚函数法来修正,同时把退火选择算子作为一个与选择、交叉和变异平行的算子,嵌入到实数编码的遗传算法中,来增强其的局部搜索能力.算法兼顾了遗传算法、模拟退火算法和外点法三者的长处,既有较快的收敛速度,又能以较大的概率求得非线性约束优化问题的全局最优解.最后以两个测试函数为算例对算法进行测试,验证了该算法搜索能力强、稳健性好,能获得更好的优化结果.实验结果表明引入外点法处理约束条件是可行的.     

基于平面不封闭图形切割的路径规划

在平面切割环节中，如何确定更短切割路径以减少加工成本、降低设备损耗和提升切割质量是工业应用和学术研究的重点。目前国内外对平面切割路径的研究主要集中于封闭图形，为此，针对在激光刀模行业中不封闭图形的问题，提出一种基于禁忌搜索的贪婪算法和贪婪准则的局部优化。首先使用构建型的贪婪算法和改进型的禁忌搜索算法相结合的方式对加工过程中的图元路径进行优化排序，随后提出贪婪准则的局部优化系数，削弱贪婪算法的“贪心”思想，解决加工路径的规划和优化问题。实验数据表明，禁忌搜索的贪婪算法和局部优化在对切割路径的规划和空行程优化上有显著效果，空行程减少50%以上，并且其优化性能和图元数量成正比，能有效地解决刀模行业以及其他激光雕刻行业中图形不封闭的切割难题。     

基于遗传算法求解约束优化问题的一种算法

在用遗传算法求解约束优化问题时,处理好约束条件是取得好的优化效果的关键.通过考虑遗传算法和约束优化问题的某些特点,提出将直接比较方法和在进化群体中自适应地保持不可行解比例的策略相结合来处理约束条件的一种新方法,并将该方法结合到通用的遗传算法中.数值实验显示了这种方法的有效性.     

包含多重嵌套封闭环的平面切割建模与优化EI北大核心CSCD

为解决在平面图形切割领域中采用不同的切割顺序以及切割起止点导致切割路径空行程相差甚远的问题,提出一种包含多重嵌套封闭环的平面切割路径优化算法.首先提出一种多重嵌套封闭环识别子算法,并以四向链表节点为基础构建包含多重嵌套封闭环的平面切割路径模型;然后考虑各封闭环的几何特征以及彼此之间的关系,将切割路径优化问题转化为多个关联的动态旅行商问题组合;最后通过逐层多次调用禁忌贪婪算法对切割路径进行优化求解,在优化中同时考虑封闭环之间的切割顺序及各封闭环切割起止点的选择.仿真实验结果表明,该算法对包含多重嵌套封闭环的平面切割路径建模和优化是可行和有效的.     

密集型多轮廓裁片的刀具空行程路径寻优

服装行业中缩短刀具裁剪空行程对于高效裁剪布料具有重要意义。结合服装裁片排列具有轮廓形状复杂、分布密集的特点,将问题转化成广义旅行商问题。 基于最大最小蚁群(MMAS)算法提出了一种新的用于裁片刀具空行程路径寻优的算法--密集多轮廓蚁群算法,该算法包括4步:1)用MMAS算法确定初步裁片顺序;2)由裁片顺序寻找各裁片入刀节点;3)将各裁片的入刀节点再次用MMAS进行顺序优化重组得到初步裁剪路径;4)反复迭代第2)步和第3)步以求得最优路径。实验验证了所提算法的有效性,对比现有的扫描算法以及双信息素蚁群(NACS)算法其结果分别提升了60.15%和22.44%,该算法在刀具空行程优化上具有明显优势。     

密集型多轮廓裁片的刀具空行程路径寻优

服装行业中缩短刀具裁剪空行程对于高效裁剪布料具有重要意义。结合服装裁片排列具有轮廓形状复杂、分布密集的特点,将问题转化成广义旅行商问题。基于最大最小蚁群(MMAS)算法提出了一种新的用于裁片刀具空行程路径寻优的算法——密集多轮廓蚁群算法,该算法包括4步:1)用MMAS算法确定初步裁片顺序;2)由裁片顺序寻找各裁片入刀节点;3)将各裁片的入刀节点再次用MMAS进行顺序优化重组得到初步裁剪路径;4)反复迭代第2)步和第3)步以求得最优路径。实验验证了所提算法的有效性,对比现有的扫描算法以及双信息素蚁群(NACS)算法其结果分别提升了60.15%和22.44%,该算法在刀具空行程优化上具有明显优势。     

包含多重嵌套封闭环的平面切割建模与优化

为解决在平面图形切割领域中采用不同的切割顺序以及切割起止点导致切割路径空行程相差甚远的问题,提出一种包含多重嵌套封闭环的平面切割路径优化算法.首先提出一种多重嵌套封闭环识别子算法,并以四向链表节点为基础构建包含多重嵌套封闭环的平面切割路径模型;然后考虑各封闭环的几何特征以及彼此之间的关系,将切割路径优化问题转化为多个关联的动态旅行商问题组合;最后通过逐层多次调用禁忌贪婪算法对切割路径进行优化求解,在优化中同时考虑封闭环之间的切割顺序及各封闭环切割起止点的选择.仿真实验结果表明,该算法对包含多重嵌套封闭环的平面切割路径建模和优化是可行和有效的.     

求解约束优化问题的一种复合形遗传算法

研究约束优化问题是科学和工程应用领域经常会遇到的一类数学规划问题.现有的约束优化进化算法,通常的解决办法是将等式约束条件转化为成对的不等式约束条件来处理,转换会使得可行域的拓扑结构变化显著,直接影响了算法性能和解的精度.为解决上述问题,提出了一种改进的处理约束优化问题的新算法.新算法将约束优化问题转化为多目标优化问题,把复合形法嵌入到遗传算法中,通过将全局搜索和局部搜索机制有机地结合,利用遗传算法全局性好和复合形法快速高效的特点,以加快最优解的搜索进程.仿真结果表明,方法既有复合形法快速高效的特点,又有遗传算法全局性好的特点.与标准遗传算法相比,方法具有良好的求解约束优化性能和精度效果.     

基于遗传算法的集合划分问题求解

集合划分问题是组合优化领域中有着广泛应用基础的著名问题,属于NP难问题.通过引入精英策略提出对遗传算法的改进,并为了能把遗传算法应用到集合划分问题,对数学模型进行了等价变换.针对集合划分问题,设计出一种高效的基因表示,避免了组合优化中处理约束条件的麻烦.解决了传统二进制基因编码无法精确适应离散优化问题,首次提出一种离散编码解决方案.最后,使用Visual C 6编程实现,取得较好的结果.     

考虑缓冲区的自动生产单元的无死锁调度策略

在制造系统中,必须防止死锁的发生.本文提出了一种在制造系统(带有有限缓冲区)中搜索最优的无死锁调度的算法.为此首先介绍了死锁问题及其图论表示方法,然后在遗传算法的基础上,运用图论算法来保证无死锁的调度结果.为了保证遗传算法生成的调度策略能够满足所要求的约束,运用图论方法选择无死锁个体,或添加缓冲区,从而在基本保证了系统的主要性能指标的同时,得到系统可行的无死锁调度结果.最后给出了一个运用此方法解决死锁问题的实例.     

Deadlock-free scheduling strategy for automated production cell

Deadlock must be avoided in a manufacturing system. In this paper, an efficient algorithm for finding a good deadlock-free schedule in a manufacturing system with enough (sufficient) or limited buffer is presented. This algorithm is based on the effective genetic algorithm (GA) search method. A formal Petri net structure is introduced, and the token player is used to assure deadlock freeness. In order to make the scheduling strategy generated by GA meet the required deadlock-free constraint, a Petri net is involved in checking the implementation of a manufacturing system during the job-scheduling process. The effectiveness and efficiency of the proposed approach is illustrated by several examples.     

基于并行遗传算法的智能公交排班研究

智能公交排班问题是公交车辆智能调度的一个典型问题之一。它可以描述为：利用某种智能化算法,在有限的步骤内,找出所有满足约束条件的最优或者接近最优的排班方案。由于排班问题搜索规模巨大,传统算法在短时间内难以获得高质量可行解。文章引入并行遗传算法,对三种主流并行模型进行评价分析,并设计了求解车辆排班问题的粗粒度并行遗传算法,编制了算法实现程序。     

基于GA改进DHMM和KPCA-RS的滚动轴承智能诊断方法研究

为实现滚动轴承故障智能诊断,提出了一种基于核主元分析法(KPCA)、粗糙集(RS)和遗传算法(GA)改进离散隐马尔科夫模型(DHMM)的智能诊断方法。通过使用混合核函数的KPCA和RS对时域、频域参数进行约简,构造敏感性高、稳定性强,并能准确表征轴承状态的特征参数矩阵。应用GA优化了DHMM,克服了DHMM训练算法容易陷入局部极小的缺点。最后应用GA优化的DHMM训练算法得到的滚动轴承各状态下的DHMM,并通过比较测试样本在各DHMM下的对数似然概率,实现了轴承故障类型的有效识别。实验结果表明,该方法可以有效地识别滚动轴承的状态,具有较强的适用性。     

Using characteristics of the optimisation problem to determine the Genetic Algorithm population size when the number of evaluations is limited

The Genetic Algorithm (GA) parameter values that result in the best possible solutions being found are generally problem specific, and therefore expected to be related to the characteristics of the fitness function. In this work, statistics that characterise the fitness function have been related to the convergence of a GA population due to the repetitive application of tournament selection. Assuming that this operator has the dominant influence on the variance of the population, and that the computational time available is limited, the result can be used to determine a suitable population size. The methodology developed has been compared to other GA calibration methodologies, and was found to be the best of the different methods considered across a range of stopping criteria and problem formulations. This result demonstrates the potential usefulness of fitness function characteristics to inform the configuration of GAs, and in turn find the best possible solutions.     

遗传算法中基于规则的分类器编码长度研究

遗传学为基础的机器学习使用遗传算法作为学习机制,设计以规则为基础的分类系统,通过训练数据集来实现类别的精确描述。针对遗传算法编码没有统一标准的问题,研究基于规则的分类器个体特征编码长度与分类准确率以及效率之间的关系,通过概率逼近分析个体特征编码长度对分类准确率的影响,利用迭代步骤数的数学期望计算方法,计算遗传算法分类器的分类效率。实验结果证明,遗传算法在密西根编码条件下,个体特征编码长度越长,分类器的分类准确率越高、收敛速度越慢。     

Multi-class fault diagnosis of induction motor using Hilbert and Wavelet Transform

The information extraction capability of two widely used signal processing tools, Hilbert Transform (HT) and Wavelet Transform (WT), is investigated to develop a multi-class fault diagnosis scheme for induction motor using radial vibration signals. The vibration signals are associated with unique predominant frequency components and instantaneous amplitudes depending on the motor condition. Using good systematic and analytical approach this fault frequencies can be identified. However, some faults either electrical or mechanical in nature are associated with same or similar vibration frequencies leading to erroneous conclusions. Genetic Algorithm (GA) is proposed and used successfully to find the most relevant fault frequencies in radial (vertical) frame vibration signal which can be used to diagnose the induction motor faults very effectively even in the presence of noise. The information obtained by Continuous Wavelet Transform (CWT) was found to be highly redundant compared to HT and thus by selecting the most relevant features using GA, the fault classification accuracy has considerably improved especially for CWT. Almost similar fault frequencies were found using CWT + GA and HT + GA for radial vibration signal.     

An innovative framework for designing genetic algorithm structures

Genetic Algorithms are popular optimization algorithms, often used to solve complex large scale optimization problems in many fields. Like other meta-heuristic algorithms, Genetic Algorithms can only provide a probabilistic guarantee of the global optimal solution. Having a Genetic Algorithm (GA) capable of finding the global optimal solution with high success probability is always desirable. In this article, an innovative framework for designing an effective GA structure that can enhance the GA's success probability of finding the global optimal solution is proposed. The GA designed with the proposed framework has three innovations. First, the GA is capable of restarting its search process, based on adaptive condition, to jump out of local optima, if being trapped, to enhance the GA's exploration. Second, the GA has a local solution generation module which is integrated in the GA loop to enhance the GA's exploitation. Third, a systematic method based on Taguchi Experimental Design is proposed to tune the GA parameter set to balance the exploration and exploitation to enhance the GA capability of finding the global optimal solution. Effectiveness of the proposed framework is validated in 20 large-scale case study problems in which the GA designed by the proposed framework always outperforms five other algorithms available in the global optimization literature.     

Efficient resource allocation for genetic algorithm based multi-objective optimization with 1,000 simulations

This study pertains to practical use of the GA for industrial applications where only a limited number of simulations can be afforded. Specifically, an attempt is made to find an efficient allocation of the total simulation budget (population size and number of generations) for constrained multi-objective optimization. A study is conducted to seek improvements while restricting the number of simulations to 1,000. Parallelization is exploited using concurrent simulations for each GA generation on a HP quad-core cluster, and resulted in a significant time savings. Furthermore, the efficient distribution of computational effort to achieve the greatest improvement in performance was explored. Two analytical examples as well as an automotive crashworthiness simulation of a finite element model with 58,000 elements were used as test examples. Various population sizes and numbers of generations were tried while limiting the total number of simulations to 1,000. The optimization performance was compared with Monte-Carlo and space filling sampling methods. It was observed that using the GA, many feasible and trade-off solutions could be found. It is shown that allowing a large number of generations is beneficial to get good trade-off solutions. For the vehicle design, significant improvements in the performance were observed. This example also suggests that, for problems with a small feasible region, the number of feasible solutions can be significantly increased in the first few generations involving about 200 simulations.     

变焦遗传算法及其并行实现研究

遗传算法(Genetic Alogrithm,GA)以其简洁及适应性而获得广泛应用.通常遗传算 法由于串格式及串长的限制,搜索空间及分辨率是有限的,因而往往收敛于局部最优.文中 提出了适宜串、并行计算的变焦(Zooming)遗传算法及多任务并行策略,采用了解码因子、搜 索中心、快速变异等策略来解决搜索空间与分辨率的矛盾,并在工作站及transputer上分别 以串行、并行方式实现了变焦遗传算法.