Differential Evolution Algorithm Based on Ensemble of Constraint Handling Techniques and Multi-Population Framework

Aimed at improving the insufficient search ability of constraint differential evolution with single constraint handling technique when solving complex optimization problem, this paper proposes a constraint differential evolution algorithm?based on ensemble of constraint handling techniques and multi-population?framework, called ECMPDE. First, handling three improved variants of differential evolution algorithms are dynamically matched with two constraint handling techniques through the constraint allocation mechanism. Each combination includes three variants with corresponding constraint handling technique?and these combinations are in the set. Second, the population is divided into three smaller subpopulations and one larger reward subpopulation. Then a combination with three constraint algorithms is randomly selected from the set, and the three constraint algorithms are run in three sub-populations respectively. According to the improvement of fitness value, the optimal constraint?algorithm is selected to run on the reward sub-population, which can share?information and close cooperation among populations. In order to verify the effectiveness of the proposed algorithm, 12 standard constraint optimization problems?and 10 engineering constraint optimization problems are tested. The experimental results show that ECMPDE is an effective algorithm for solving constraint optimization problems.

Composition of Web Services of Multi-Population Adaptive Genetic Algorithm Based on Cosine Improvement

Web quality of service (QoS) awareness requires not only the selection of specific services to complete specific tasks, but also the comprehensive quality of service of the whole web service composition. How to select the web service composition with the highest comprehensive QoS is a NP hard problem. In this paper, an improved multi population genetic algorithm is proposed. Cosine adaptive operator is added to the algorithm to avoid premature algorithm caused by improper genetic operator and the disadvantage of destroying excellent individuals in later period. Experimental results show that compared with the common genetic algorithm and multi population genetic algorithm, this algorithm has the advantages of shorter time consumption and higher accuracy, and effectively avoids the loss of effective genes in the population.

HELMHOLTZ FREE ENERGY IN A SYSTEM OF ANISOTROPIC CHAINLIKE MOLECULES AND ITS APPLICATIONS FOR NORMAL MONOHYDRIC ALCOHOLS AND NORMAL ALKANES

1 INTRODUCTIONAfter Prausnitz et al.published the perturbed hard-sphere chain theory(PHCT),manymodified theories have been put forward,such as the perturbed soft-sphere chain theory(PSCT),the perturbed anisotropic chain theory(PACT)and the associated perturbedanisotropic chain theory(APACT).These theories are all concerned with the parameter3c.But,Wertheim in his work proved that the free energy of hard-sphere chain couldbe calculated without 3c.Induced by the work done by Wertheim,Chapman

链状MFI型分子筛的合成与应用

链状MFI型分子筛是由大小均一的晶粒沿b轴方向通过表面羟基缩合而成,在保留传统分子筛独特的三维孔道结构、耐酸性、水热稳定性、高催化活性的同时,具有较高的孔道择形性,在吸附、分离、催化等领域展现出独特的性能。本文系统介绍了链状MFI型分子筛合成及其应用领域的最新研究进展,重点综述了常规水热合成与微波辅助水热合成两种方法,深入分析了链状MFI型分子筛的形成机理,对诱导表面羟基缩合的各种因素进行了详细阐述,总结了其在物理吸附及催化方面的优异性能。最后对链状MFI型分子筛的绿色合成及潜在应用进行了展望,提出硅配位环境的动态变化过程分析对于揭示其合成机理至关重要,将为拓展链状MFI型分子筛的应用奠定重要理论基础。

Multi-population particle swarm optimization algorithm for automatic design of steel frames

Steel structures are widely used;however,their traditional design method is a trial-and-error procedure which is neither efficient nor cost effective.Therefore,a multi-population particle swarm optimization(MPPSO)algorithm is developed to optimize the weight of steel frames according to standard design codes.Modifications are made to improve the algorithm performances including the constraint-based strategy,piecewise mean learning strategy and multi-population cooperative strategy.The proposed method is tested against the representative frame taken from American standards and against other steel frames matching Chinese design codes.The related parameter influences on optimization results are discussed.For the representative frame,MPPSO can achieve greater efficiency through reduction of the number of analyses by more than 65% and can obtain frame with the weight for at least 2.4%lighter.A similar trend can also be observed in cases subjected to Chinese design codes.In addition,a migration interval of 1 and the number of populations as 5 are recommended to obtain better MPPSO results.The purpose of the study is to propose a method with high efficiency and robustness that is not confined to structural scales and design codes.It aims to provide a reference for automatic structural optimization design problems even with dimensional complexity.The proposed method can be easily generalized to the optimization problem of other structural systems.

Distribution Network Optimization Model of Industrial Park with Distributed Energy Resources under the Carbon Neutral Targets

Taking an industrial park as an example,this study aims to analyze the characteristics of a distribution network that incorporates distributed energy resources(DERs).The study begins by summarizing the key features of a distribution network with DERs based on recent power usage data.To predict and analyze the load growth of the industrial park,an improved back-propagation algorithm is employed.Furthermore,the study classifies users within the industrial park according to their specific power consumption and supply requirements.This user segmentation allows for the introduction of three constraints:node voltage,wire current,and capacity of DERs.By incorporating these constraints,the study constructs an optimization model for the distribution network in the industrial park,with the objective of minimizing the total operation and maintenance cost.The primary goal of these optimizations is to address the needs of DERs connected to the distribution network,while simultaneously mitigating their potential adverse impact on the network.Additionally,the study aims to enhance the overall energy efficiency of the industrial park through more efficient utilization of resources.

电流变液及其性能研究

研制了两类不同重量比的电流变液，然后对基于硅油的几种配比的电流变液进行了微观结构研究，发现这一类电流变液的确存在链状结构，链的多少和粗细与液体的重量比和外加电场强度的大小有关。最后对两类电流变液分别进行了静态和动态特性实验，发现液体的重量比和外加电场强度对性能影响很大，所制得的电流变液可以认为是一种宾汉流体。

基于实时信息的区域备自投研究与实现

针对常规备自投装置不能实现链式结构串供的多个110 k V变电站中非开环点变电站在失电时的快速恢复供电问题,提出了一种适用于常规和智能化变电站,基于区域电网实时全景信息的区域备自投新方案。具体介绍了区域备自投原理和实现方案,以及其与站域备自投、常规备自投、区域保护、区域稳控的关系等。RTDS试验及现场工程应用表明,所提出的区域备自投性能优越,能够实现区域电网发生故障时的快速恢复供电。

磁流变智能液场致微结构变化的Monte Carlo模拟

从磁流变液中存在的各种相互作用势出发，对Ｎ＝１００个球形羰基铁颗粒均匀分布于一正方形二维体系之中的物理模型，利用ＭｏｎｔｅＣａｒｌｏ方法和Ｍｅｔｒｏｐｌｉｓ算法着重计算了磁流变智能液的场致微结构变化。计算结果表明，磁流变液（ＭＲＦ）的流变效应在外磁场中的变化是由于其ＭＲＦ中磁性颗粒的成链团簇有序化所致。这与磁流变液在外磁场中熵的下降和对ＭＲＦ的光学显微镜观察结果是一致的。

非均匀链状分子系统的密度泛函理论

提出了一个基于空穴相关函数的非均匀链状分子系统的密度泛函理论，其中，链分子是相切连接的柔性硬球链，并处于两块平行的无限大平板之间。其亥氏函数由理想和剩余两部分组成，它们都是分子密度分布的泛函，其中剩余部分由链的空穴相关函数计算。在数值计算中应用了嵌入计算机分子模拟的方程组求解方法。计算了三个链节组成的分子的密度分布，与ＭＣ数据吻合良好。

由双核铜簇与柔性双三氮唑基配体构筑的一维链状铜配合物的合成、结构与光学性质表征(英文)

基于柔性的双三氮唑基配体和刚性的芳香羧酸,合成得到了一个一维链状铜配位聚合物[Cu2(bza)4(btx)]n(1)(btx=1,4-二(1,2,4-三唑基-1-基甲基)苯,Hbza=苯甲酸),并通过元素分析、红外光谱、X-射线单晶衍射、热分析、荧光光谱等测试对其进行了表征。晶体数据表明该化合物属于三斜晶系,P1空间群。在化合物1中,羧酸根均采取双齿桥联的模式,4个苯甲酸配体螯合2个铜离子构成1个双核铜单元(Cu-Cu键的距离为0.26 nm),这些双核铜单元又通过柔性双三氮唑基配体桥联形成一维链状的结构;热分析表明该化合物具有相对较高的热稳定性,室温下的固态荧光测试发现,在398 nm激发下,化合物1发射出绿色光。

立方扰动硬链方程

本文通过在SPHC方程中引入拟合Carnahan-Starling硬球方程所得的斥力项,建立了立方型扰动硬链状态方程.由拟合正构烷烃的饱和蒸汽压和液相密度所确定的3个纯组分参数与碳原子数呈良好线性关系.对19种纯物质饱和蒸汽压和液相密度数据计算的平均相对误差分别为1.52%和2.97%;对混合物提出了一种新的混合规则,应用于预测含短链和长链烃类及二氧化碳、硫化氢混合物的高压汽液平衡,优于采用SPHC、SRK和PT方程所得的计算结果.

链状纳米碳酸钙的制备

用间歇鼓泡碳化法制备了链状纳米CaCO3,用pH计和电导仪对碳化反应跟踪检测 ,对其形成机理进行了分析 ,实现了对链状CaCO3的粒度控制。考察了碳化反应过程中pH值、电导率和反应物质量分数、反应温度、CO2 气体流等因素对产品粒径和形态的影响。结果表明 ,碳化反应过程中 pH值和电导率都有一个快速下降阶段。其中电导率有两个下降回复过程。Ca(OH) 2 质量分数、CO2 流量的增大、反应温度升高都会使反应速率和晶核生长速率变大。反应温度控制在 30℃左右 ,Ca(OH) 2 质量分数为 1 0 %左右 ,在一定范围内CO2

基于MPHC活度系数模型关联烃－水体系液－液平衡

基于普遍化范德华配分函数理论，应用由局部组成概念导出的方阱流体配位数模型导出了链状分子混合物的配位数模型，并由此建立了一个新的活度系数模型；将其应用于高度非理想体系汽－液平衡活度系数的关联，结果与Ｗｉｌｓｏｎ方程相当，但方程参数随温度变化较小；其于Ｎｅｗｔｏｎ－Ｒａｐｈｓｏｎ－Ｇａｕｓｓ液－液平衡算法，应用新模型对烃－水体系的液－液平衡进行了关联计算。

含临界点的统计缔合流体理论研究(Ⅰ)非极性流体

从适合于临界点的计算出发 ,对统计缔合流体理论方程进行改进 :(1)对链状分子的每个链节考虑其非球形度 ;(2 )提出必须考虑链节成链时对色散作用的影响 .建立了扩展的统计缔合流体理论状态方程 ,对于正构烷烃和异构烷烃的计算表明 ,该方程计算温度范围大 ,从低温区到临界区 ,特别是在临界点附近精度有明显提高 ,临界点的计算值和实验值基本符合 .扩展了理论状态方程的适用性 。

自适应多智能体算法优化深度网络的列车智能驾驶

在利用深度学习实现列车智能驾驶时,神经网络架构和参数的选择过于依赖人工经验,现有梯度下降法在参数优化时易陷入局部最优,且学习任务单一。针对上述问题,提出一种自适应多种群链式多智能体算法(AMPCMA)优化LSTM网络的列车智能驾驶新方法,该方法有机地将计算智能与深度学习结合,能充分挖掘优秀驾驶员数据。其具体实现过程为:首先,从自动化深度学习角度出发,采用遗传算法(GA)优化深度网络结构,克服了其结构难以确定的问题,并在此基础上分粗、细学习2个阶段对整个网络的参数进行优化。在粗学习阶段,采用AMPCMA算法对LSTM参数预置初值,有效地学习多个任务的共性。该算法能在进化过程中动态调整小种群链表规模,具有较好的灵活性和自适应性。接着在细学习阶段,基于上述多任务共性学习所得到的LSTM参数,再用Adam算法分别对单个任务上的参数精细优化,以实现任务的个性学习;其次,有效地设计了多任务之间的信息共享机制,且任务共性和个性学习有机结合,使得整个网络泛化能力强,较好地改善了列车档位、档位操纵时间和列车速度的多任务决策效果;最后,通过仿真实验验证了所提出的AMPCMA-LSTM模型较传统机器学习方法更优越,提高了列车操控与预测精度,并能在多种操控序列下表现出较强的鲁棒性。

求解动态优化问题的多种群热力学遗传算法

多种群方法已被证明是提高演化算法动态优化性能的重要方法之一。提出了多种群热力学遗传算法(multi-population based thermodynamic genetic algorithm,MPTDGA)。该算法使用一个概率向量在热力学遗传算法迭代过程中不断演化优化与竞争学习,环境变化时分化成三个概率向量,并分别抽样产生原对偶和随机迁入三个子种群,依据这三个种群和记忆种群最好解的情况,选择新的工作概率向量进入新环境进行学习。在动态背包问题上的实验结果表明,MPTDGA比原对偶遗传算法跟踪最优解的能力更强,有很好的多样性,非常适合求解0-1动态优化问题。

链状结构IVB和VB金属硫属化合物的结构化学

总结了我们此前开展链状结构前过渡金属硫属化合物结构研究的结果。按照结构特征 ,分类讨论了 1 2个链状化合物的晶体结构、电子结构和物性。化合物M4O (Te2 ) 4I4Te(M =Ti,Ta)具有绝缘体性质 ,其结构由M4OTe10 I4氧心四核簇通过共用Te原子连接而成的 [M4OTe9I4]链组成 ;化合物 (MQ4) nG(M =Nb ,Ta;Q =Se,Te ;G =I,In ,Sb)具有半导体性质 ,其结构由 [M (Q2 ) 2 ]1∞ 链组成 ,链间插入G原子 ;化合物 (MTe4) nIn -2 (TaI6 ) (M =Nb ,Ta;n =4、6 )的结构由 [M (Te2 ) 2 ]1∞ 链、分立的TaI6基团和I原子组成 ,它们具有良好的导电性。

离子型γ-Fe_2O_3/Ni_2O_3磁性液体的制备、磁化性质和微结构特征

用Massart法合成了水基γ-Fe_2O_3/Ni_2O_3复合纳米微粒多分散离子型磁性液体。该磁性液体的磁化强度和光透射驰豫现象表明磁性液体中除了存在无环的微团聚外,还有链状团聚。由于在多分散磁性液体中,磁性微粒之间的磁相互作用很弱,仅靠磁相互作用不能形成链状团聚体,链状团聚体的形成是受到了"排空力"的作用。在γ-Fe_2O_3/Ni_2O_3离子型磁性液体中,"排空力"促使液体中的较大微粒在无外磁场作用下形成短链,磁性液体的宏观特性如磁化强度和光弛豫现象都是由这些短链造成的。同时,只具有链状团聚体的磁性液体具有优良的磁光效应。

链状杆菌制作方法的改良

探讨不同的前期处理方法对链状杆菌制作效果的影响,寻找一种最适合链状杆菌制片效果的方法。以在细菌培养基28℃培养7～8小时的枯草芽孢杆菌为材料,分别用斜面固体培养悬浮菌体制片、斜面固体培养直接制片和液体震荡培养制片3种前期处理方法染色制成的标本片,经形态观察发现:用斜面固体培养悬浮菌体制片效果最佳,具有典型标准的长链状排列;其次是液体震荡培养制片的有较长的链状排列;而斜面固体培养的制片效果最差,几乎不能表达出长链排列形态。