基于遗传算法的码垛机器人路径规划应用

目的为了改进传统遗传算法在码垛机器人路径规划中可能出现的局部陷阱和过早收敛问题,以及机器人的能耗和路线平滑性问题,提出一种改进的遗传算法机器人路径规划方法。方法针对传统遗传算法存在的问题,分别对种群初始化、适应度函数、选择算子、交叉算子、变异算子的算法和方式进行调整和改进,对优秀算法进行融合。针对基本遗传算法主要着重于路径最短,从而忽视了机器人的能耗及路径平滑性等问题,设计一种综合考虑距离和转弯次数控制的适应度函数,最后将改进的算法应用于码垛机器人的路径规划中。结果仿真结果表明,相较于基本遗传算法,提出的算法搜索到的路径质量更高,不仅距离更短,同时转弯次数远远小于其他算法,路径更为平滑,验证了该算法的有效性。结论基于该算法的码垛机器人路径在兼顾距离最优的同时,路线更加平滑。由于减少了转向次数,机器人的能耗更低,同时仿真结果表明,该算法的实时性也较好。     

An adaptive penalty scheme for genetic algorithms in structural optimization

A parameter‐less adaptive penalty scheme for genetic algorithms applied to constrained optimization problems is proposed. Using feedback from the evolutionary process the procedure automatically defines a penalty parameter for each constraint. The user is thus relieved from the burden of having to determine sensitive parameter(s) when dealing with every new constrained optimization problem. The procedure is shown to be effective and robust when applied to test problems from the evolutionary computation literature as well as several optimization problems from the structural engineering literature. Copyright © 2003 John Wiley & Sons, Ltd.     

A robust Island Parallel Genetic Algorithm for the Quadratic Assignment Problem

The Quadratic Assignment Problem (QAP) is a difficult and important problem studied in the domain of combinatorial optimisation. It is possible to solve QAP instances with 10--20 facilities using exhaustive parallel algorithms within a few days on a cluster machine. However, large QAP instances with more than 100 facilities are not solvable using exhaustive techniques. We have explored a variety of Genetic Algorithm crossover operators for this problem and verified its performance experimentally using well-known instances from the QAPLIB library. By increasing the number of processors, generations and population sizes we have been able to find solutions that are the same as (or very close to) the best reported solutions for large QAP instances in QAPLIB. In order to parallelise the Genetic Algorithm we generate and evolve separate solution pools on each cluster processor, using an island model. This model exchanges 10% of each processor’s solutions at the initial stages of optimisation. We show experimentally that both execution times and solution qualities are improved for large QAP instances by using our Island Parallel Genetic Algorithm.     

遗传算法用于衍射光学元件的优化设计

提出了一种基于遗传算法的衍射光学元件优化设计方法;在衍射光学元件设计中遗传算法运行参数对遗传算法性能有一定的影响:采用较大的群体规模,遗传算法越容易获得最优解;交叉算子越大,遗传算法全局搜索能力越强;选择算子对遗传算法的影响不是太大;如果要进一步提高解的精度,可选取较大的终止代数。数值计算结果表明,用遗传算法优化设计的衍射光学元件,其误差小于 5.2%,衍射效率达到 91.2%。遗传算法很适合衍射光学元件的优化设计。     

一种改进的k-means文档聚类初值选择算法

提出了一种改进的基于最小最大原则的k-means文档聚类初始值选择算法.该方法首先构造相似度矩阵,然后利用最小最大原则对相似度矩阵进行分析,从而选择初始聚点并自动确定聚类k值.实验结果表明利用该方法找到的k值比较接近真实值.     

Petri nets and genetic algorithms for complex manufacturing systems scheduling

In this paper we propose the GAPN (genetic algorithms and Petri nets) approach, which combines the modelling power of Petri nets with the optimisation capability of genetic algorithms (GAs) for manufacturing systems scheduling. This approach uses both Petri nets to formulate the scheduling problem and GAs for scheduling. Its primary advantage is its ability to model a wide variety of manufacturing systems with no modifications either in the net structure or in the chromosomal representation. In this paper we tested the performance on both classical scheduling problems and on a real life setting of a manufacturer of car seat covers. In particular, such a manufacturing system involves features such as complex project-like routings, assembly operations, and workstations with unrelated parallel machines. The implementation of the algorithm at the company is also discussed. Experiments show the validity of the proposed approach.     

Genetic algorithms and simulated annealing for scheduling in agile manufacturing

In this paper, genetic algorithms and simulated annealing are applied to scheduling in agile manufacturing. The system addressed consists of a single flexible machine followed by multiple identical assembly stations, and the scheduling objective is to minimize the makespan. Both genetic algorithms and simulated annealing are investigated based on random starting solutions and based on starting solutions obtained from existing heuristics in the literature. Overall, four new algorithms are developed and their performance is compared to the existing heuristics. A 2³ factorial experiment, replicated twice, is used to compare the performance of the various approaches, and identify the significant factors that affect the frequency of resulting in the best solution and the average percentage deviation from a lower bound. The results show that both genetic algorithms and simulated annealing outperform the existing heuristics in many instances. In addition, simulated annealing outperforms genetic algorithms with a more robust performance. In some instances, existing heuristics provide comparable results to those of genetic algorithms and simulated annealing with the added advantage of being simpler. Significant factors and interactions affecting the performance of the various approaches are also investigated.     

Genetic algorithms and Monte Carlo simulation for optimal plant design

We present an approach to the optimal plant design (choice of system layout and components) under conflicting safety and economic constraints, based upon the coupling of a Monte Carlo evaluation of plant operation with a Genetic Algorithms-maximization procedure. The Monte Carlo simulation model provides a flexible tool, which enables one to describe relevant aspects of plant design and operation, such as standby modes and deteriorating repairs, not easily captured by analytical models. The effects of deteriorating repairs are described by means of a modified Brown–Proschan model of imperfect repair which accounts for the possibility of an increased proneness to failure of a component after a repair. The transitions of a component from standby to active, and vice versa, are simulated using a multiplicative correlation model. The genetic algorithms procedure is demanded to optimize a profit function which accounts for the plant safety and economic performance and which is evaluated, for each possible design, by the above Monte Carlo simulation.In order to avoid an overwhelming use of computer time, for each potential solution proposed by the genetic algorithm, we perform only few hundreds Monte Carlo histories and, then, exploit the fact that during the genetic algorithm population evolution, the fit chromosomes appear repeatedly many times, so that the results for the solutions of interest (i.e. the best ones) attain statistical significance.     

基于遗传粒子群动态聚类算法的物流柔性分拣系统品规分配

目的 针对目前烟草物流配送中心条烟分拣量大,不同条烟品规的分配对订单的总处理时间影响较大的问题,研究平衡各个分拣区品规的分配,提高分拣效率。方法 建立以各分区品规相似系数和最小为目标函数的数学模型,并采用改进的遗传粒子群动态聚类（GAPSO-K）算法进行求解。首先,结合各品规分拣量对品规相似系数进行改进,并将其作为适应度函数;然后在粒子群算法中对惯性权重因子进行改进,使其值可以进行自适应改变;最后,在粒子群动态聚类算法中引入遗传算法中的交叉变异扩大解的搜索范围,基于Matlab对文中的其他算法进行求解对比,求得结果在EM-plant中进行仿真验证。结果 结合某烟草物流配送中心数据仿真验证,利用GAPSO-K算法处理订单的时间为234.5s,较传统时间大幅度较少,有效提升了柔性物流分拣效率。结论 采用该算法可充分发挥2种算法的优良性,具有更好的收敛性及寻优性,为柔性物流品规分配提供了新思路。     

Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)—the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets.     

大规模物流网络的组合遗传算法研究

在对遗传算法、生成树遗传算法和混合进化方法进行比较的基础上,提出组合遗传算法来解决大规模基本物流网络设计问题.该问题抽象为"选址-分派问题",并进一步分解,且将"分派"镶嵌在"选址"中."选址"和"分派"染色体分别使用二进制编码和矩阵编码、适值采用物流费用.运算结果显示该方法比混合进化方法结果更精确,且在大规模问题求解方面速度优于通常的遗传算法,因此是一种设计大规模物流网络的较好方法.     

Optimal packetization scheme for embedded multimedia bitstreams using genetic algorithms

The problem of packetizing embedded multimedia bitstreams into fixed‐size packets is investigated and an optimal packetization scheme using a genetic algorithm is presented. In the proposed method, each individual packetization instance is represented by a decision sequence and mapped to a chromosome. A steady‐state genetic algorithm is applied to search for the optimal chromosome that minimizes the distortion between the original and the reconstructed media. In addition, we propose a fast method to calculate the fitness value based on the distortion associated with each chromosome to speed up the evolution process. The computer simulation results show that our proposed packetization scheme has high compression efficiency and provides error resiliency to packet losses with a relatively fast speed. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 77–84, 2006     

An MILP model and clustering heuristics for LED assembly optimisation on high-speed hybrid pick-and-place machines

This paper deals with a scheduling optimisation problem arising in printed circuit board (PCB) assembly. In one class of PCB assembly, light-emitting diodes are to be assembled into the placement locations on PCBs by a machine with multiple pick-and-place heads. The scheduling optimisation problem is to determine the assembly sequence of placement locations and the assignment of pick-and-place heads for locations so as to minimise the assembly time. We formulate it as a mixed integer linear programming model. To solve the problem efficiently, we classify the PCBs into two types. For the first type of PCBs, on which the locations are linearly arranged, a constructive heuristic is proposed based on the analysis of the best next location after a location is assembled. For the second type of PCBs, on which the locations are circularly arranged, a heuristic based on clustering strategy and path relinking method is proposed. Computational experiments show that the solutions obtained by the two heuristics make 2.32 and 6.82% improvements averagely for the PCBs with linearly and circularly arranged locations, respectively, as compared to the solutions used in real production, and they are also better than those obtained by a hybrid genetic algorithm.     

利用遗传算法优化透明质酸发酵培养基

运用遗传算法对透明质酸(HA)产生菌--马链球菌兽瘟亚种ATCC 39920发酵培养基的6种组份进行了优化研究.每个长度为36位的染色体编码一种培养基配方,以HA产量为适应度函数值对其进行评价.经过4代的进化,各参数的取值范围收敛于最优区域.最终以40个实验样本完成了6种培养基成分、64个浓度水平的优化选择.优化后的培养基的构成为:葡萄糖44.0g/L,酵母膏5.2g/L,蛋白胨8.4g/L,牛肉膏9.8g/L,KH2PO41.45g/L,MgSO42.8g/L.采用优化培养基的HA产量达0.395g/L,较原培养基提高了31.2%,生产成本也大幅度降低.     

Genetic algorithms for layout optimization in crossdocking operations of a manufacturing plant

In this study, we use genetic algorithms to optimize the lane layout associated with the crossdocking operation at the Toyota Motor Manufacturing plant in Georgetown, Kentucky, USA. A genetic algorithm solution can be obtained within seconds, whereas an exhaustive search would require a computing time of over five days on a 1?GHz Intel Pentium III. The results of this study show that a simple rearrangement of the lanes will lead to a decrease in workload of nearly 34% in the crossdocking area and ultimately result in an overall reduced lead time.     

Hybrid evolutionary multi-objective algorithms for integrating assembly sequence planning and assembly line balancing

Assembly sequence planning (ASP) and assembly line balancing (ALB) play critical roles in designing product assembly systems. In view of the trend of concurrent engineering, pondering simultaneously over these two problems in the development of assembly systems is significant for establishing a manufacturing system. This paper contemplates the assembly tool change and the assembly direction as measurements in ASP; and further, Equal Piles assembly line strategy is adopted and the imbalanced status of the system employed as criteria for the evaluation concerning ALB. Focus of the paper is principally on proposing hybrid evolutionary multiple-objective algorithms (HEMOAs) for solutions with regard to integrate the evolutionary multi-objective optimization and grouping genetic algorithms. The results provide a set of objectives and amend Pareto-optimal solutions to benefit decision makers in the assembly plan. In addition, an implemented decision analytic model supports the preference selection from the Pareto-optimal ones. Finally, the exemplifications demonstrate the effectiveness and performance of the proposed algorithm. The consequences definitely illustrate that HEMOAs search out Pareto-optimal solutions effectively and contribute to references for the flexible change of assembly system design.     

Scalability of a Hybrid Extended Compact Genetic Algorithm for Ground State Optimization of Clusters

We analyze the utility and scalability of extended compact genetic algorithm (eCGA)—a genetic algorithm (GA) that automatically and adaptively mines the regularities of the fitness landscape using machine learning methods and information theoretic measures—for ground state optimization of clusters. In order to reduce the computational time requirements while retaining the high reliability of predicting near-optimal structures, we employ two efficiency-enhancement techniques: (1) hybridizing eCGA with a local search method, and (2) seeding the initial population with lowest energy structures of a smaller cluster. The proposed method is exemplified by optimizing silicon clusters with 4-20 atoms. The results indicate that the population size required to obtain near-optimal solutions with 98% probability scales sub linearly (as Θ(n^0.83)) with the cluster size. The total number of function evaluations (cluster energy calculations) scales sub-cubically (as Θ(n^2.45)), which is a significant improvement over exponential scaling of poorly designed evolutionary algorithms.     

Application of genetic algorithms for the identification of elastic constants of composite materials from dynamic tests

This work has been placed within the framework of the identification of stiffness properties of composite materials from dynamic tests. More precisely, the used approach has been inserted in the general context of model updating. The genetic algorithms method has been used as a complementary technique allowing a first estimation of the elastic coefficients. In other words, the initial finite element model is estimated. The refinement of solutions can thus be made by a classical updating method, such as the sensitivity method. The procedure allows the simultaneous estimation of several properties from a single test. Properties of extension, bending, twisting and transverse shear effects can be identified. Results obtained by numerical simulation show the efficiency and robustness of the genetic algorithms. Copyright © 1999 John Wiley & Sons, Ltd.     

基于遗传算法的回转体头部线型低噪优化设计

基于边界层理论和转捩区声辐射理论,利用Krane偶极子声源模型对Liepmann单极子声源模型进行改进,结合回转体头部线型设计理论,采用标准遗传算法,建立了一套完整的回转体头部线型低噪优化设计的方法和模型。优化结果表明：找到了全局最优解,最大降噪量约为11.8%。     

基于遗传模糊C-均值聚类算法的除湿机故障诊断

针对除湿机系统的故障诊断问题及其特点,以CFTZ21型除湿机为对象,应用模糊C-均值聚类（FCM）算法进行了研究;引入遗传算法对传统模糊C-均值聚类算法进行了改进,克服了传统算法的不足;结合实验采集到的数据样本,对改进后的遗传模糊C-均值聚类算法进行检验,结果达到预期效果,由此说明,将改进的FCM应用于除湿机故障诊断是可行的。