An Effective Hybrid Heuristic Algorithm for the Hub Location Problems of Small-Scale Uncapacitated Hub-and-Spoke Network

The key factors for designing a successful hub-and-spoke network are to determine the optimal number of hubs, to properly locate hubs, and to allocate the non-hubs to the hubs.This paper proposed a hybrid heuristic based on genetic algorithm (GA) to resolve the uncapacitated single allocation hub location problem with fixed cost (USAHLPFC), according to the upper bound of the ratio of the number of hubs to the number of nodes.Computational experiments were conducted using the Civil Aeronautics Board (CAB) data set.Results indicate that, applying the proposed heuristic, one can obtain the optimal solutions for all small-scaled problems very efficiently.A comparison between the proposed hybrid heuristic method and the GA presented by Topcuoglu et al.shows that the former outperforms the latter when the discount factor α is less.     

基于遗传算法的近红外光谱建模样品集优化研究

探讨遗传算法优化方法在南丰蜜橘内部品质可溶性固形物的建模集样品选择中的应用,根据优化结果建立了南丰蜜橘可溶性固形物的快速无损检测数学模型。研究选择的波长范围为350—1800nm,应用遗传算法优化建模集样品,优化后建模集样品数量由89个减少到36个,累积变异系数达到99．99％,交互验证均方根误差达到最小。研究结果表明：优化后的建模样品数量大大减少,且保证了南丰蜜橘可溶性固形物的快速无损检测数学模型的稳定性。     

多类型任务复合作业的堆场龙门吊调度问题研究

在集装箱码头,龙门吊是负责在集装箱堆位和集卡之间进行集装箱搬运的主要设备,其作业效率对提升集装箱码头的服务水平至关重要.为了提升集装箱堆场龙门吊的作业效率,本文根据龙门吊实际作业情况,对集装箱堆场龙门吊的多类型任务复合作业问题进行研究.以优化龙门吊作业任务开工时间延迟的惩罚成本为目标,基于整数规划方法建立了多类型任务复合作业下的堆场龙门吊调度模型,并针对模型特点设计了基于GA的智能求解算法.最后,以某码头的实际作业数据为基础,进行了案例计算和对比,验证了该模型和求解算法的有效性.     

基于双层规划的危险品道路运输网络设计

分析了危险品道路运输网络设计问题的双层特性,建立了以政府期望的风险最小化为上层目标、运输者期望的成本最小化为下层目标的危险品运输网络双层规划模型。采用遗传算法,以Pydev为平台,运用Python编程以及TransCAD生成网络,实现了运算和结果可视化。实例验证结果表明,遗传算法能给出稳定的最优解,而且所得风险符合预期并接近于最低网络风险。     

Genetic Algorithm Based Tikhonov Regularization Method for Displacement Reconstruction

In this paper,a genetic algorithm based Tikhonov regularization method is proposed for determination of globally optimal regularization factor in displacement reconstruction.Optimization mathematic models are built by using the generalized cross-validation(GCV)criterion,L-curve criterion and Engl's error minimization(EEM)criterion as the objective functions to prevent the regularization factor sinking into the locally optimal solution.The validity of the proposed algorithm is demonstrated through a numerical study of the frame structure model.Additionally,the influence of the noise level and the number of sampling points on the optimal regularization factor is analyzed.The results show that the proposed algorithm improves the robustness of the algorithm effectively,and reconstructs the displacement accurately.     

U型不完全多目标拆卸线平衡问题建模与优化

针对U型布局所具有的生产柔性强、效率高等优点,结合仅需考虑需求零部件和危害性零部件的实际拆卸过程,提出U型不完全拆卸线平衡问题(U-shaped partial disassembly line balance problem,UPDLBP),以最小化工作站数量、空闲时间均衡指标、拆卸深度和拆卸成本为优化目标建立数学模...     

基于Fisher距离的新型脑机接口分类器

提出基于Fisher距离测度的线性分类器符合统计学习理论框架的观点,结合主分量分析和遗传算法提出一种基于结构风险最小化（Structural Risk Minimization,简称SRM）归纳原则的分类器设计方法．通过对比遗传算法和穷举法的运算量,阐明所提出的特征提取方法在采用Fisher线性分类器分类时的优势．最后采用所提出的基于SRM归纳原则的方法对一组人脑慢皮层电位数据进行了分类仿真实验,并将结果与该组数据竞赛优胜者的结果进行了对比,性能得到了明显提高．     

遗传算法在终端区飞机排序中的应用

研究了遗传算法在终端区跑道分配以及飞机排序中的应用，建立了多条跑道多架飞机排序的数学模型，并进行了算例仿真分析。仿真结果表明，遗传算法与先到先服务排序相比较，适应度增加了80％，延时减小了40％，说明遗传算法的排序结果优于先到先服务的排序结果。     

IMPROVED GENETIC ALGORITHM TO OPTIMAL PORTFOLIO WITH RISK CONTROL

1IntroductionAlog-optimalinvestimentportfoliowithoutriskconstraintshasbeenintroducedandsystem-aticallystudiedinChapter15ofCoverandl'homas[11.Themodelcanbediscribedasfol-.lows:Astockmarketisrepresentedasavectorofstocksx=(X,,X,,..',X.)',X,>o,i=1,2,.-',m,wheremisthenumberofstocksandthepricerelativeXirepresentstheratiooftheclos-ingpriceofthecurrentbusinessdaytotheprevi-ousday.LetF(x),x=(x,,x2,..',x-)'eR"bethejointdistributionofvectorX.Aportfoliob=(b,,bs,..',b,)',b,>o,i=l,2,..',m,Zb,=1istheall…     

遗传算法在城市单交叉路口信号动态控制中的研究及应用

提出一种实时遗传算法优化配时控制方法,对单交叉路口多相位交通流建立一种实时动态模型,根据不同时刻的不同车流量,选择最优相位配时和相位顺序,通过对本周期及前一周期的车流量进行实时测量,采用线性预估方法,对下一周期的车流量进行预估,以最大通行能力为路口模型控制性能指标,从而确定下一周期的相位配时方案。最后在原模型的基础上作为改进模型的例子融入了延误时间,得到了新的目标函数。采用C语言编程进行实时仿真试验,结果表明,算法实时性好,运行稳定。     

遗传算法在逆向物流网络规划设计中的应用

考虑到客户、初始回收点和回收中心3个层次的逆向物流网络,选择合适的回收中心,确定初始回收点个数和回收周期,基于一些基本假设,以最小化总的相关成本建立了1个内嵌两个指派模型的非线性混合整数规划模型,对符号体系和求解思路进行了分析.借助遗传算法,结合具体实例,给出了求解步骤和求解结果分析.     

SPSA算法在微观交通仿真模型VISSIM参数标定中的应用

微观交通仿真模型在交通系统管理、控制和优化中得到了广泛的应用. 然而微观交通仿真模型参数标定是一项复杂且系统的工作,特别是对于较复杂网络,其参数标定耗时长,且不容易找到最优解. 本文选取了应用较为广泛的VISSIM仿真模型作为基础平台,针对遗传算法（GA）的不足,建立了基于同步扰动随机逼近（SPSA）算法的微观仿真模型参数标定方法,并实现了程序的自动化标定;最后将该方法应用于北京市快速路仿真模型的驾驶员行为参数标定中,以速度的相对误差平方和作为收敛函数,通过对比GA算法,SPSA算法收敛速度快1.7倍,且在标定后的流量检验中相对误差的平方和小0.16,验证了SPSA算法在VISSIM参数标定上的优越性.     

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微观交通仿真模型在交通系统管理、控制和优化中得到了广泛的应用. 然而微观交通仿真模型参数标定是一项复杂且系统的工作,特别是对于较复杂网络,其参数标定耗时长,且不容易找到最优解. 本文选取了应用较为广泛的VISSIM仿真模型作为基础平台,针对遗传算法（GA）的不足,建立了基于同步扰动随机逼近（SPSA）算法的微观仿真模型参数标定方法,并实现了程序的自动化标定;最后将该方法应用于北京市快速路仿真模型的驾驶员行为参数标定中,以速度的相对误差平方和作为收敛函数,通过对比GA算法,SPSA算法收敛速度快1.7倍,且在标定后的流量检验中相对误差的平方和小0.16,验证了SPSA算法在VISSIM参数标定上的优越性.     

An efficient approach for solving yard crane scheduling in a container terminal

An efcient approach for yard crane（YC）scheduling is proposed in this paper.The definition of task group for YC scheduling is proposed.A mixed integer programming（MIP）model is developed.In the model,objective functions are subject to the minimization of the total delay of complete time for all task groups and the minimization of block-to-block movements of YCs.Due to the computational scale of the non-deterministic polynomial（NP）complete problem regarding YC scheduling,a rolling-horizon decision-making strategy is employed to solve this problem,by converting the MIP model into another MIP model in the scheduling of each rolling period.Afterwards,a heuristic algorithm based on modified A＊search is developed to solve the converted model and obtain near optimal solution.Finally,the computational experiments are used to examine the performance of the proposed approach for YC scheduling.     

大型机场进场航空器联合调度模型

为减少进场航空器总延误与总滑行时间,研究了大型机场进场航空器联合调度问题;分别以跑道排序时间跨度和总延误加权和最小、被分配至远机位航班数量最少、进场航空器总滑行时间最短为目标函数,构建了跑道、停机位、滑行道三大系统的正向联合调度模型;在此基础上引入停机位再调整模型,通过调整额外滑行时间较大的航空器的停机位指派方案对滑行...     

Ship appearance optimal design on RCS reduction using response surface method and genetic algorithms

Radar cross section (RCS) reduction technologies are very important in survivability of the militarynaval vessels. Ship appearance shaping as an effective countermeasure of RCS reduction redirects the scatteredenergy from one angular region of interest in space to another region of little interest. To decrease the scatteringelectromagnetic signals from ship scientifically, optimization methods should be introduced in shaping design.Based on the assumption of the characteristic section design method, mathematical formulations for optimalshaping design were established. Because of the computation-intensive analysis and singularity in shapingoptimization, the response surface method (RSM) combined genetic algorithm (GA) was proposed. The poly-nomial response surface method was adopted in model approximation. Then genetic algorithms were employedto solve the surrogate optimization problem. By comparison RCS of the conventional and the optimal design,the superiority and effectiveness of proposed design methodology were verified.Ky words: radar cross section (RCS); characteristic section design method; response surface method; genetic algorithm (GA) was proposed. The polynomial response surface method was adopted in model approximation. Then genetic algorithms were employed to solve the surrogate optimization problem. By comparison RCS of the conventional and the optimal design, the superiority and effectiveness of proposed design methodology were verified.     

考虑二阶效应的空间刚架结构布局优化

针对三维空间刚架布局优化问题,以七自由度节点梁柱单元二阶弹性理论推导考虑构件几何非线性和截面翘曲变形的非线性刚度矩阵,通过整合梁柱单元非线性刚度矩阵对刚架结构进行整体的二阶弹性分析,建立满足刚架结构强度、刚度和稳定性要求的布局优化数值模型;并针对复杂刚架结构布局优化数值求解问题,改进遗传搜索算法（GA）,提出可靠拓扑和引导型遗传算法双向控制方法（KLGA）. 该方法一方面将拓扑变量从布局设计变量中分离,以构件重要度评定结构可靠拓扑变量组合,再与设计变量整合;另一方面将结构特有的引导信息加入算法中,为GA提供全局最优解的指引路径. 通过两种典型的刚架算例表明二阶效应模型和KLGA算法的可行性和有效性,例如算例2中基于二阶效应模型的KLGA得到的最优结构质量比GA减轻了24.5%,波动幅度从9.61%提升到1.39%,算法更加稳定.      

Optimal linear phase finite impulse response band pass filter design using craziness based particle swarm optimization algorithm

An efficient method is proposed for the design of finite impulse response (FIR) filter with arbitrary pass band edge, stop band edge frequencies and transition width. The proposed FIR band stop filter is designed using craziness based particle swarm optimization (CRPSO) approach. Given the filter specifications to be realized, the CRPSO algorithm generates a set of optimal filter coefficients and tries to meet the ideal frequency response characteristics. In this paper, for the given problem, the realizations of the optimal FIR band pass filters of different orders have been performed. The simulation results have been compared with those obtained by the well accepted evolutionary algorithms, such as Parks and McClellan algorithm (PMA), genetic algorithm (GA) and classical particle swarm optimization (PSO). Several numerical design examples justify that the proposed optimal filter design approach using CRPSO outperforms PMA and PSO, not only in the accuracy of the designed filter but also in the convergence speed and solution quality.     

基于改进遗传算法的递归神经网络非线性系统辨识

将递归内时延神经网络应用于非线性动力学系统辨识中，描述了其动力学方程，并引入改进遗传算法作为其学习算法，通过非线性动力学SISO和MIMO系统的辨识仿真研究，验证了内时延递归网络结构和改进遗传算法的有效性。     

COLOR IMAGE QUANTIZATION WITH EVOLUTIONARY PROGRAMMING

Introduction　　 The need for quantization of color images isarised because of the limitations of image displayand hardcopy,data storage and data transmissiondevices.The color image quantization is a complexdata clustering problem due to the broad distribu-tion of local optima in the three- dimensional colorspace. Many of the present algorithms for colorquantization find non- optimal solutions,giving riseto visible shifts in color and false contours whenthe number of quantization colors is sma…