多目标U型拆卸线平衡问题的Pareto蚁群遗传算法

针对传统方法求解多目标U型拆卸线平衡问题的不足,提出了一种基于Pareto解集的多目标蚁群遗传算法.在构造初始解阶段,以协同考虑最大作业时间、最小拆卸成本差作为蚂蚁的启发式信息;通过蚁群算法搜索可行拆卸序列,并根据多目标之间的支配关系得到Pareto解集;将蚁群算法的Pareto非劣解作为遗传操作的个体,进而将遗传操作的结果正反馈于最优拆卸路径上信息素的积累,并采用拥挤距离作为蚂蚁全局信息素更新策略,可以平衡多目标对信息素的影响,使算法快速获得较优解.将所提算法应用于52项拆卸任务算例和某打印机拆卸线实例,在算例验证中,通过对比Pareto蚁群算法,所提算法求得的8个非劣解在3个评价指标上性能分别提高了50.43%、3.25%、14.10%,在实例应用中所提算法求得8种可选平衡方案,从而验证了所提算法的有效性、优越性和实用性.      

Fault reconfiguration of shipboard power system based on triple quantum differential evolution algorithm

Fault reconfiguration of shipboard power system is viewed as a typical nonlinear and multi-objective combinatorial optimization problem. A comprehensive reconfiguration model is presented in this paper, in which the restored loads, switch frequency and generator efficiency are taken into account. In this model, analytic hierarchy process(AHP) is proposed to determine the coefficients of these objective functions. Meanwhile, a quantum differential evolution algorithm with triple quantum bit code is proposed. This algorithm aiming at the characteristics of shipboard power system is different from the normal quantum bit representation. The individual polymorphic expression is realized, and the convergence performance can be further enhanced in combination with the global parallel search capacity of differential evolution algorithm and the superposition properties of quantum theory. The local optimum can be avoided by dynamic rotation gate. The validity of algorithm and model is verified by the simulation examples.     

Simplified Group Search Optimizer Algorithm for Large Scale Global Optimization

A simplified group search optimizer algorithm denoted as"SGSO"for large scale global optimization is presented in this paper to obtain a simple algorithm with superior performance on high-dimensional problems.The SGSO adopts an improved sharing strategy which shares information of not only the best member but also the other good members,and uses a simpler search method instead of searching by the head angle.Furthermore,the SGSO increases the percentage of scroungers to accelerate convergence speed.Compared with genetic algorithm(GA),particle swarm optimizer(PSO)and group search optimizer(GSO),SGSO is tested on seven benchmark functions with dimensions 30,100,500 and 1 000.It can be concluded that the SGSO has a remarkably superior performance to GA,PSO and GSO for large scale global optimization.     

基于微粒群算法的多目标列车运行过程优化

为客观地描述列车的运行过程,建立了列车运行过程的多目标优化模型,并用微粒群算法求解该模型.针对多目标微粒群优化(MOPSO)算法的不足,提出了相应的改进措施和解的多样性保持策略.仿真结果表明,提出的优化列车运行过程的改进MOPSO算法可以在一次运行过程中获得多组列车操纵控制策略,清晰地显示出各性能指标随控制策略变化的趋势,控制序列转换次数大大降低,每组控制策略都可以在能耗、运行时间和停靠准确性之间获得很好的折衷效果,可以根据列车运行状况选择恰当的策略控制列车,以获得预期的结果.     

考虑弹流润滑的圆柱滚子轴承多目标智能优化设计

基于弹性流体动力润滑理论,以额定动载荷最大和膜厚比最大为目标函数,通过对圆柱滚子轴承多目标优化数学模型的研究,应用退火罚函数方法将其转化为无约束单目标优化问题.采用遗传算法求解含连续及离散变量的优化设计问题,同时针对遗传算法的局限性,采用了实数编码,调整了适应函数,设计了基于确定性准则的杂交算子和变异算子,借鉴了优解保留策略,结合模拟退火算法,形成了遗传模拟退火算法,该算法发挥了模拟退火与遗传算法各自的特点.算例表明了该优化方法的有效性.     

Improved Fuzzification Method for Multi-Objective Decision-Making and Its Application in Evaluation of Highway Planning

IntroductionFuzzysettheoryhasbeenstudiedextensivelyoverthepast 30 years,andhasfoundsuccessfulapplicationsandimplementationsinvariousareas .Mostoftheearlyinterestsinfuzzysettheorywerefocusedonrepresentingtheuncertaintyinhumancognitiveprocesses.Nowfuzzysett…     

云遗传算法

为了克服传统遗传算法搜索速度慢、易陷入局部最优解的缺陷，借鉴遗传算法的思想，利用云模型云滴的随机性和稳定倾向性的特点，提出了一种新的遗传算法——云遗传算法（CGA）．该算法由正态云模型的Y条件云发生器实现交叉操作，由基本云发生器实现变异操作．最后，进行了函数优化实验，并与标准遗传算法（SGA）和自适应遗传算法（AGA）进行了比较，以证明其有效性．     

公交线网优化的算法研究

为缩短居民乘车出行时间以及减少公交运营投入,在公交线网优化过程中通常采用具有全局最优的遗传算法。针对基本遗传算法自身的一些缺点,提出了一种改进的遗传算法。该算法在公共交通路线优选过程中运用减少搜索空间、加入最佳保留策略和修正策略的方法,对基本遗传算法中的选择、交叉、变异算子及中止条件进行改进。改进遗传算法能够在保证种群多样性的同时加快收敛速度,较好地避免了基本遗传算法前期容易早熟和后期进化缓慢等现象。实例仿真计算表明,该算法具有比基本遗传算法更高的效率,是优化公交线网的一个有效途径。     

Research on Multi-Objective Real-Time Optimization of Automatic Train Operation (ATO) in Urban Rail Transit

The determination and optimization of Automatic Train Operation (ATO) control strategy is one of the most critical technologies for urban rail train operation. The practical ATO optimal control strategy must consider many goals of the train operation, such as safety, accuracy, comfort, energy saving and so on. This paper designs a set of efficient and universal multi-objective control strategy. Firstly, based on the analysis of urban rail transit and its operating environment, the multi-objective optimization model considering all the indexes of train operation is established by using multi-objective optimization theory. Secondly, Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve the model, and the optimal speed curve of train running is generated. Finally, the intelligent controller is designed by the combination of fuzzy controller algorithm and the predictive control algorithm, which can control and optimize the train operation in real time. Then the robustness of the control system can ensure and the requirements for multi-objective in train operation can be satisfied.     

AN EFFICIENT BTC IMAGE COMPRESSION ALGORITHM

IntroductionBlock truncation coding( BTC) [1] is a simple andfast lossy image coding technique,which has theadvantage of being easy to implement comparedto transform coding and vector quantization[2 ] .Its simplicity,performance and channel error re-sisting capability make it attractive in the real-time image transmission.BTC is a one- bit adap-tive moment- preserving quantizer that preservescertain statistical moments of small blocks of theinput image in the quantized output.The origi-nal …     

一种改进的遗传算法在动态交通分配中的应用

以遗传思想为基础设计了新的启发式优化算法,针对动态交通分配问题,对遗传操作中的选择、交叉、变异算子进行修改,同时采用了模拟退火收敛准则,克服了传统遗传算法早熟收敛的弊端,并提高了全局寻优能力。仿真实验表明,该算法有快速高效的特点,提高了该动态交通分配模型的实用价值。     

求解具有恶化工件单机调度问题的改进遗传算法

为克服现有算法求解工件数较多的单机调度问题计算量大的缺点,分析了加工时间为阶梯函数的工件排序规则,以极小化最大完工时间为目标,提出了基于局部搜索的改进遗传算法,对基于工序编码方式的染色体设计了线性顺序交叉算子和融合工件排序性质的局部变异算子,并引入局部搜索策略,提高了算法局部搜索能力和收敛速度.算例测试结果表明:工件数为40件时,与模拟退火算法相比,本文算法求得的最大完工时间平均减少了56.6%,显著缩短了制造周期,并有效地避免了局部最优解,收敛速度显著提高.     

Immune clonal selection optimization method with combining mutation strategies

In artificial immune optimization algorithm, the mutation of immune cells has been considered as the key operator that determines the algorithm performance. Traditional immune optimization algorithms have used a single mutation operator, typically a Gaussian. Using a variety of mutation operators that can be combined during evolution to generate different probability density function could hold the potential for producing better solutions with less computational effort. In view of this, a linear combination mutation operator of Gaussian and Cauchy mutation is presented in this paper, and a novel clonal selection optimization method based on clonal selection principle is proposed also. The simulation results show the combining mutation strategy can obtain the same performance as the best of pure strategies or even better in some cases.     

基于改进灰狼算法优化BP 神经网络的短时交通流预测模型

准确的短时交通流预测是交通控制和交通诱导的依据. 提出一种基于改进灰狼算法(TGWO)优化BP 神经网络的短时交通流预测模型(TGWO-BP),有效提高短时交通流预测精度. 针对标准灰狼算法(GWO)收敛速度慢,容易陷入局部极值的问题,提出一种自适应递减的收敛因子,使灰狼算法区分全局搜索和局部搜索;改进灰狼个体的位置更新公式,引入惯性权重,调节惯性权重大小使灰狼算法具有跳出局部极值的能力;对比分析TGWO-BP、GWOBP 、PSO-BP、BP这4 种短时交通流预测模型,结果显示,TGWO-BP的短时交通流预测模型误差为10.03%,达到较好的预测精度.     

MRT列车运行模拟模型的多目标改进遗传算法

为了求解城市快速交通（MRT）列车运行模拟模型，寻找最优的列车运行控制曲线，构造了多目标改进遗传算法．以列车运行过程中工况转换点为基因编码依据，以多个基因构成一个染色体代表一个控制方案，从而形成初始种群；根据列车运行控制的停站误差、时分误差和能耗等目标要求设计适应值函数；通过个体有效性检查保证选择、交叉和变异过程中新个体的有效性，并在各算子中加入保优算子，使新种群不淘汰上一代最优个体．实例计算表明，与多质点优化模型相比，在一定的误差范围内，遗传算法能够减少能耗10％以上，并能提供大量次优解，具有明显的优化效果．     

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…     

基于联合时频分析的设备状态评估方法

在机电设备状态评估过程中,提出了一种基于联合时频分析的振动、噪声信号测量与分析方法.该方法可以通过综合利用机电设备运行过程中的振动与噪声信号中的有用信息,完成对整机运行状态评估.最后,以某舰船的电机作为实验对象,构建一套振动和噪声测量系统,并运用戈勃变换对所测得的振动和噪声信号进行联合时频分析,从而得到电机真实的运行状态.实验结果表明所提出的测量与分析方法是正确有效的.     

物体表面重建轮廓拼接的改进遗传算法

为解决物体表面重建中的轮廓拼接问题，将其转化为在有向图中寻求最优路径问题．提出了基于遗传算法的适用各种目标函数的轮廓拼接算法，其中对初始种群的产生、交叉算子和变异算子等做了改进，以确保产生的个体均能代表有效解．算例模拟结果表明，该算法简单可行，在优化性能、收敛速度及鲁棒性等方面优于模拟退火算法．     

基于遗传算法的追踪列车节能优化

为了研究追踪列车的节能优化操纵策略,提出了四显示固定闭塞系统下的列车静态速度约束条件和追踪列车动态速度约束条件.在此基础上,建立了以列车操纵手柄级位和工况转换点为控制变最的追踪列车节能优化模型.采用染色体长度可变多目标遗传算法,结合外部惩罚函数对该模型进行了求解,并利用遗传算法中的染色体变长算子对列车操纵手柄变换策略进行了优化.在四显示固定闭塞平台上的仿真结果表明,该方法可在安全、准点的前提下,使追踪列车的能耗下降4.3％,运行时间误差减小1.7％.     

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针对有时间窗的车辆调度问题，在标准遗传算法的基础上，加入爬山算法增强算法的局部搜索能力，通过保护全局最优基因的方法提高了算法的收敛能力，并辅以自适应变异算子，构造了一种改进的混合遗传算法。实验结果表明，改进后的算法具有抗“早熟”能力强、收敛速度快和局部搜索能力强的特点。