基于改进遗传算法的赶流列车运行调整研究

开展技术站车流组织与区段列车运行调整的协同优化研究,利用在途列车的运行可调性实现运输区域的"线流配合",可优化运输生产指标。将前方技术站的车流接续需求作为列车运行调整的目标之一,并定义为赶流调整。在分析赶流调整策略及应用场景的基础上,建立了赶流调整模型,设计了基于遗传算法的模型求解算法。算法设计充分结合列车运行调整特点,有效避免了"早熟"及收敛速度慢等现象,适应性好,求解时间能很好地满足列车运行调整需求。可快速验证"线流配合"研究思路中调整目标的可行性,并给出具体的调整措施,为技术站车流组织与列车运行调整协同优化研究的深入奠定基础。     

Optimization of composite beam structures using a genetic algorithm

This work examines the possibility of using a to nature adopted, stochastic search method, called genetic algorithm (GA), for optimizing the damping ability of composites.The principles and properties of GAs are briefly described, and an approach to evaluating and optimizing the factor of dampness of composites, using a specific GA combined with an existing FEM program, is given.Some results of optimizing the stacking sequences and the thicknesses of the layers, consisting of different materials, are presented as examples of composite beams in order to demonstrate the efficiency of the method.     

Weighted edit distance optimized using genetic algorithm for SMILES-based compound similarity

Pattern Analysis and Applications - A method for developing new drugs is the ligand-based approach, which requires intermolecular similarity computation. The simplified molecular input line entry...     

Using weighted genetic programming to program squat wall strengths and tune associated formulas

This study developed a weighted genetic programming (WGP) approach to study the squat wall strength. The proposed WGP evolves on genetic programming (GP), an evolutionary algorithm-based methodology that employs a binary tree topology and optimized functional operators. Weight coefficients were introduced to each GP linkage in the tree in order to create a new weighted genetic programming (WGP) approach. The proposed WGP offers two distinct advantages, including: (1) a balance of influences is struck between the two front input branches and (2) weights are incorporated throughout generated formulas. Resulting formulas contain a certain quantity of optimized functions and weights. Genetic algorithms are employed to accomplish WGP optimization of function selection and proper weighting tasks. Case studies herein focused on a reference study of squat wall strength. Results demonstrated that the proposed WGP provides accurate results and formula outputs. This paper further utilized WGP to tune referenced formulas, which yielded a final formula that combined the positive attributes of both WGP and analytical models.     

Automatic test data generation using genetic algorithm and program dependence graphs

The complexity of software systems has been increasing dramatically in the past decade, and software testing as a labor-intensive component is becoming more and more expensive. Testing costs often account for up to 50% of the total expense of software development; hence any techniques leading to the automatic generation of test data will have great potential to considerably reduce costs. Existing approaches of automatic test data generation have achieved some success by using evolutionary computation algorithms, but they are unable to deal with Boolean variables or enumerated types and they need to be improved in many other aspects. This paper presents a new approach utilizing program dependence analysis techniques and genetic algorithms (GAs) to generate test data. A set of experiments using the new approach is reported to show its effectiveness and efficiency based upon established criterion.     

基于改进遗传算法的加权模糊C均值聚类算法

针对模糊C均值(FCM)聚类算法具有初始聚类中心敏感和容易陷入局部最优的问题,提出了一种基于改进遗传算法(GA)的加权模糊c均值聚类算法,采用高斯变异算子,提高了遗传算法在每个峰值附近的局部搜索能力,用基于复相关系数的加权欧式距离代替欧式距离,改进了FCM算法的聚类目标函数.用改进的算法对国际标准测试数据Iris进行测试,实验结果表明改进后的算法具有更好的稳定性和健壮性,提高了聚类的效果.     

Optimum design of structures by an improved genetic algorithm using neural networks

Optimum design of large-scale structures by standard genetic algorithm (GA) makes the computational burden of the process very high. To reduce the computational cost of standard GA, two different strategies are used. The first strategy is by modifying the standard GA, called virtual sub-population method (VSP). The second strategy is by using artificial neural networks for approximating the structural analysis. In this study, radial basis function (RBF), counter propagation (CP) and generalized regression (GR) neural networks are used. Using neural networks within the framework of VSP creates a robust tool for optimum design of structures.     

Applications of genetic algorithm to aesthetic design of dam structures

In this paper, an attempt is made to develop a decision-making supporting system for the aesthetic design of dam structures. The present system is based on genetic algorithm and computer graphics. Genetic algorithm is able to produce many design alternatives, whereas computer graphics is useful to prepare their view simulations. In order to evaluate the alternatives, the analytical hierarchy process method is used to reflect the preference of designers. A numerical example of coloring a dam structure is presented to demonstrate the applicability of the system developed here.     

Optimum design of structures against earthquake by adaptive genetic algorithm using wavelet networks

Optimum design of structures against earthquake is achieved by a modified genetic algorithm. Some features of the simulated annealing are used to control various parameters of the genetic algorithm. To reduce the computational work, a fast wavelet transform is used by which the number of points in the earthquake record is decreased. For this purpose, the record is decomposed into two parts. One part contains the low frequency and the other possesses the high frequency of the record. The low-frequency part is used for dynamic analysis. Then, by using a wavelet network, the dynamic responses of the structures are approximated. By such approximation, the dynamic analysis of the structure is not necessary during the optimisation process. Thus, wavelet neural networks have been employed as a general approximation tool for the time-history dynamic analysis and estimation of the dynamic responses in the process of optimisation. A number of structures are designed for optimal weight against El Centro earthquake and the results are compared with those of the exact approach.     

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.     

Integrated machine tool selection and operation sequencing with capacity and precedence constraints using genetic algorithm

In this paper, an integrated machine tool selection and sequencing model is proposed. The model determines machine visiting sequences for all part types, such that the total production time for the production order is minimized and workloads among machine tools are balanced. The model is formulated as a 0–1 integer programming. To solve the model, a genetic algorithm approach based on a topological sort technique is developed. To demonstrate the efficiency of the proposed GA approach on the integrated machine tool selection and sequencing problem, a number of numerical experiments using various size problems are carried out. The numerical experiments show that the proposed GA approach is efficient to this problems.     

应用分段遗传算法的视频纹理合成算法

通过对视频纹理定义的分析,将视频纹理合成转化为一个优化组合问题。提出一种应用分段遗传算法的视频纹理合成算法,采用分段遗传算法,对有限长度的源视频进行加工,得到可无限播放的连续视频序列。算法采用更适当的相似性尺度和测量准则,省去了大量复杂的对源视频的预处理,分段的搜索策略只需要用很少的遗传代数即可快速合成出质量很高的视频纹理。与现有的视频纹理合成方法比较,该算法具有较小的计算复杂度,在合成的速度和质量上都有所提高。另外,实验结果给出了种群大小以及最大遗传代数对合成质量和速度的影响。     

Assignment of collaborators to multiple business problems using genetic algorithm

As firms encounter new problems in the fast-changing business environment, they have to find collaborators with problem-solving expertise. Since this optimization problem takes place in a firm as the business environment changes, genetic algorithm (GA), which has shown outstanding performance in obtaining a sub-optimal solution relatively quickly, seems to be the right solution, one that is superior to goal-programming, multi-attribute decision making, and branch and bound. We therefore propose a GA-based approach to solving the problem of assigning collaborators to multiple business problems. Our solution worked well in several experiments.     

改进遗传算法的并行任务调度

并行任务调度是一个NP完全问题,它关注资源的分配和并行任务调度,要求具有高性能的调度算法,且能求解出高质量的解。提出了一种基于改进遗传算法的并行任务调度算法,在算法初始化种群产生时引入任务向量矩阵来表示任务、资源以及调度的关系,并采用启发式方法得到初始化种群,提高种群质量;采用规则约束的交叉和变异操作,提高个体的质量;提出了加速进化策略,有效地避免了早熟。仿真实验结果表明,该改进算法能更有效地求解并行任务调度问题。     

量子遗传算法在TSP中的应用

本文提出了一种改进的量子遗传算法,其核心是对量子遗传算法中的量子旋转门的调整策略进行改进。在现有的静态、指数型动态调整策略的基础上提出了基于正弦函数的动态调整策略。文中对旅行商问题（TSP）的仿真实验结果表明：改进后的算法的优化质量和效率都优于遗传算法和一般量子遗传算法。     

LoG边缘算子改进的加权引导滤波算法

针对原始全局的引导滤波算法对整幅图像各个区域使用统一的线性模型与相同的规整化因子,从而未能适应图像本身不同区域的纹理特性,提出了基于LoG边缘检测算子改进的加权自适应规整因子。通过在局部窗口内计算LoG幅值响应,对原有的规整化因子进行惩罚来取得对图像平滑区域与边缘区域的自适应,使得在保证降噪效果的前提下进一步突出边缘像素和平坦区域像素之间的差异。对开源医学图像库BrainWeb中不同断层的T1、T2与PD加权图像,共18张图像,添加9%的莱斯噪声作为测试库,并采用结构相似性因子(SSIM)与无参考图像锐化因子(CPBD)作为算法的定量评估指标。实验结果表明,与原始的引导滤波算法相比,所提方法的SSIM指标获得了最高5%左右的提升,CPBD指标获得了最高6%左右的提升。在引导滤波不同规整化因子的条件下,所提算法均优于原始的引导滤波算法和现有的基于方差图像加权改进的引导滤波算法,并保留了原始引导滤波O(N)的复杂度。与现存的主流滤波算法比较,所提算法能够兼顾SSIM与CPBD指标,具有最高的综合性能,且具有最低的算法复杂度,能够用于医学图像和彩色图像的快速滤波降噪。     

基于嵌套遗传算法的拣货作业联合优化

针对物流配送中心拣货作业过程中传统订单分批和拣货路径分步优化难以获得整体最优解的问题，为了提高拣货作业效率，提出了一种基于嵌套遗传算法的订单分批和路径优化的联合拣货策略。首先，建立了以拣货总时间最短为目标函数的订单分批与拣货路径联合优化模型；然后，考虑双重优化的复杂性，设计了一种嵌套遗传算法对模型进行求解，外层不断优化订单分批结果，内层根据外层订单分批结果优化拣货路径。算例结果表明，与传统的订单分步优化、分批分步优化策略相比，所提策略的拣货时间分别减少了45.6%、6%，基于嵌套遗传算法的联合优化模型得出的拣货路径更短、拣货时间更少。为验证该算法对不同规模订单均有较优性能，分别对10、20、50张订单规模的算例进行仿真实验，结果表明，随着订单量的增加，整体拣货距离和时间进一步减少，拣货时间的减少从6%增加到7.2%。基于嵌套遗传算法的拣货作业联合优化模型和其求解算法可以有效解决订单分批与拣货路径联合优化问题，为配送中心拣选系统的优化提供依据。     

基于嵌套遗传算法的拣货作业联合优化

针对物流配送中心拣货作业过程中传统订单分批和拣货路径分步优化难以获得整体最优解的问题,为了提高拣货作业效率,提出了一种基于嵌套遗传算法的订单分批和路径优化的联合拣货策略。首先,建立了以拣货总时间最短为目标函数的订单分批与拣货路径联合优化模型;然后,考虑双重优化的复杂性,设计了一种嵌套遗传算法对模型进行求解,外层不断优化订单分批结果,内层根据外层订单分批结果优化拣货路径。算例结果表明,与传统的订单分步优化、分批分步优化策略相比,所提策略的拣货时间分别减少了45.6%、6%,基于嵌套遗传算法的联合优化模型得出的拣货路径更短、拣货时间更少。为验证该算法对不同规模订单均有较优性能,分别对10、20、50张订单规模的算例进行仿真实验,结果表明,随着订单量的增加,整体拣货距离和时间进一步减少,拣货时间的减少从6%增加到7.2%。基于嵌套遗传算法的拣货作业联合优化模型和其求解算法可以有效解决订单分批与拣货路径联合优化问题,为配送中心拣选系统的优化提供依据。