A FUZZY CLOPE ALGORITHM AND ITS OPTIMAL PARAMETER CHOICE

Among the available clustering algorithms in data mining, the CLOPE algorithm attracts much more attention with its high speed and good performance. However, the proper choice of some parameters in the CLOPE algorithm directly affects the validity of the clustering results, which is still an open issue. For this purpose, this paper proposes a fuzzy CLOPE algorithm, and presents a method for the optimal parameter choice by defining a modified partition fuzzy degree as a clustering validity function. The experimental results with real data set illustrate the effectiveness of the proposed fuzzy CLOPE algorithm and optimal parameter choice method based on the modified partition fuzzy degree.

An exact algorithm for optimal redundancy in a series system with multiple component choices

In this paper, an exact algorithm was proposed for optimal redundancy in a series system with multiple component choices. A reformulation of the nonseparable reliability function was approximated by a separable integer programming problem. The resulting separable nonlinear integer programming problem is used to compute upper bounds by Lagrangian relaxation and dual search. A special partition scheme was derived to reduce the duality gap in a branch-and-bound process, thus ensure the convergence of the algorithm. Computational results show that the algorithm is efficient for solving this class of reliability optimization problems.

The Optimal Choice of in Rural Highway Pavement

It indicate that block pavement has virtue of being satisfied with driving demands,longer cycle,simple technology,less cost and so on,reaching purpose to improve road grades and service quality by putting into less investment,can quickly advance the building step of rural road,at the same time relieve the financial strain.So the block pavement is a optimal pavement in rural road except for heavy trafics,special geography,for tourism and high grade highway.

Optimal Solution of Multi-Choice Mathematical Programming Problem Using a New Technique

The study deals with the multi-choice mathematical programming problem, where the right hand side of the constraints is multi-choice in nature. However, the problem of multi-choice linear programming cannot be solved directly by standard linear or nonlinear programming techniques. The aim of this paper is to transform such problems to a standard mathematical linear programming problem. For each constraint, exactly one parameter value is selected out of a multiple number of parameter values. This process of selection can be established in different ways. In this paper, we present a new simple technique enabling us to handle such problem as a mixed integer linear programming problem and consequently solve them by using standard linear programming software. Our main aim depends on inserting a specific number of binary variables and using them to construct a linear combination which gives just one parameter among the multiple choice values for each choice of the values of the binary variables. A numerical example is presented to illustrate our analysis.

Internet-Based Optimization and Choice of Insert Grooves for Face-Milling

Manufacture is facing more furiously competition in the new century. It tends to be globalized. Rapid response and technology innovation have become the key factor to the success of manufacture enterprise. At present, internet-based manufacturing develops rapidly. With the development of engineering material and machining techniques, better cutting properties of metal cutting tools are required. The world is paying more attention to the study of indexable carbide inserts with three-dimensional complex grooves which can affect cutting properties directly and chip-controlling. So the variety and specification of indexable insert develop quickly and the application of insert with three-dimensional complex chip-former becomes wider and wider. Because milling is an interrupted cutting, the process of milling is complicated and the failure of the milling inserts is severe. It is important to choose suitable milling inserts groove for particular machining conditions before actual machining, it will extend tool life and raise productivity. Optimization and choice of inserts with complex groove via internet may serve for manufacture enterprises all over the world. The milling inserts with three-dimensional complex grooves are optimized and chosen via Internet in this paper. The face-milling process is studied, with the mathematics models of cutting force for different shape of cutting edges established, the cutting forces predicted and stress-fields of different insert grooves analyzed by FEM, according to the predictable results and the most suitable insert groove for particular machining conditions are optimized and chosen. The complex groove optimization and choice are based on a client/server model, with the client and server being on different machines across the internet. Communication between the client and server uses the TCP/IP. The result of optimization and choice will be sent to the users via Internet. Manufacturers all over the world can get the desired insert groove without paying for expensive experiments. Thus, the cost and lead time of products are reduced.

Mate choice for major histocompatibility complex(MHC)complementarity in the Yellow-rumped Flycatcher(Ficedula zanthopygia)

Background:Genes of the major histocompatibility complex(MHC)are an important component of the vertebrate immune system and play a significant role in mate choice in animal populations.However,the MHC genetic targets of female mate choice have not been clearly identified,and whether female mate choice is based on neutral genetic characteristics remains an open question.Here,we focus on the effects of morphological traits and genetic similarity among individuals in MHC class IIB(MHC IIB)exon 2 on mating in a sexually dimorphic songbird that exhibits social monogamy with extra-pair paternity(EPP).Methods:We sequenced 64 parent-offspring triads sampled over a 3-year period using two MHC class II loci to detect disassortative mating in the Yellow-rumped Flycatcher(Ficedula zanthopygia).Results:We found that MHC similarity in social pairs was lower than that in random pairs.Extra-pair mate choice according to MHC IIB was observed,in which females’extra-pair mates had fewer MHC alleles than their within-pair mates,but there was no significant band-sharing between extra-pair sires and potential extra-pair mates.However,the interaction between the MHC diversity of females and that of the social males affected the occurrence of EPP.Conclusions:Our results support the“optimality hypothesis”of MHC-based social and extra-pair choice.Female choice probably maintains a certain level of MHC diversity in offspring in the Yellow-rumped Flycatcher.

Optimal Consumption, Leisure and Job Choice under Inflationary Environment

The optimal job choice,consumption and portfolio decision-making of economic agents under inflationary environment for a continuous infinite time are studied.The agent’s preference is characterized by the Cobb-Douglas utility function with two variables of consumption and leisure.The economic agent invests in three kinds of assets:risk-free bonds,inflation index bonds and risky assets.The agent has two kinds of working conditions:One is the work with high income and little leisure time,and the other is the work with low income and much leisure time.Firstly,the real wealth process after inflation discount is derived by using Itôformula.Then,based on the expected utility maximization standard under any working state,martingale method is adopted to obtain the closed form solution of optimal job choice,consumption and portfolio decision-making.Finally,the effects of wealth and inflation volatility on the optimal consumption and portfolio strategies are quantitatively analyzed by numerical simulation with given parameters.

基于机器学习的中型城市居民出行方式选择行为研究

为探索中型城市居民出行特征以及不同因素对出行方式选择行为的影响机制,本文以中国某中型城市居民出行数据为例,综合考虑传统离散选择模型和机器学习模型在预测精度和建模合理性上的优劣,以及机器学习模型超参数求解算法的特点和效率,引入变异程序,提出粒子群优化随机森林的中型城市居民出行方式选择预测模型,采用预测准确率、出行方式比例预测绝对误差和期望模拟误差这3项性能指标,量化对比粒子群优化随机森林模型与多种机器学习模型和多项Logit模型统计学上的预测性能差异,利用SHAP(SHapley Additive exPlanation)模型深入分析个人社会经济属性、出行属性及出行方式属性等相关因素与居民出行方式选择之间的非线性关系。结果表明:粒子群优化随机森林模型整体平均预测准确率最高,为0.856,出行方式比例预测平均绝对误差和期望模拟平均误差最低,分别为0.062和0.306,模型间指标差异在统计学检验下显著;距离对不同出行方式选择的影响最显著,步行和私家车出行对距离敏感性更高,不同距离下,两者选择概率变化超过35%;30岁以下群体不同出行方式选择概率差距大于其他年龄段;性别、是否拥有私家车或公交IC卡等因素显著改变公交车和私家车的选择概率。

考虑旅客选择行为的收益管理和机型指派联合模型

传统收益管理假设旅客需求是独立的,未能充分考虑到旅客的选择行为,本文讨论了考虑旅客选择行为的收益管理问题和机型指派联合优化问题。根据对历史销售数据的观察,发现旅客对最低价舱位具有特殊偏好,因此本文利用尖峰多项Logit (spiked multinomial logit, Spiked-MNL)选择模型来预测旅客潜在真实需求,并以航空公司利润最大化为目标,构建考虑旅客选择行为的收益管理和机型指派联合模型。本研究利用某航空公司的实际销售交易数据进行算例分析,验证所提出的模型的可行性,并通过在不同场景下的比较来评估模型的性能。结果显示,在比较联合模型和独立模型的利润时,联合模型的平均利润高出5%;在不同机队规模的比较中,5机型联合模型产生的利润最高,其平均利润比3机型高出6.6%,比9机型高出7.3%。此外,与以往关于利用旅客选择模型预测旅客需求的方法相比,Spiked-MNL模型能够更准确地反映旅客的实际购买行为,其平均利润比广义吸引力模型(generalized attraction model, GAM)高出0.7%,比多项Logit (multinomial logit, MNL)模型高出1.2%。

含双馈机组风电场备用电缆位置和容量优化方法

链状结构风电场中电缆故障后,故障串部分风电机组将被迫停运。在两串末端增加备用电缆,可以为故障下游风电机组提供输电通道,但对非故障串电缆容量要求较大,利用率低。文章提出一种备用电缆位置和容量优化方法。首先定义最小路矩阵,评估随机风速/电缆故障时风电场输出。根据备用电缆位置对风电场分区。提出有限容量模型解决故障串输电优先性顺序,以确定备用电缆最佳位置。然后引入双馈感应发电机(doubly-fed induction generator,DFIG)稳态约束,采用主从分裂算法,求解风电场潮流。以利用率和成本为目标,提出备用电缆容量的优化模型。最后给出算例分析结果,验证所提方法的有效性。

改进量子位初始映射的综合SWAP优化策略

在嘈杂的中尺度量子时代,传统的初始映射策略忽略了后续操作中可能出现的邻接性的问题。针对这一挑战,综合考虑量子比特间的距离、交互时间和门操作的错误率,设计了一个多因素交互成本函数,并提出综合SWAP优化策略(comprehensive SWAP optimization strategy,CSOS)。该策略包括最佳SWAP选择和基于SWAP的批量更新策略,用于优化量子电路的局部量子位映射。最佳SWAP选择通过对比SWAP操作的效益,选择最佳收益的SWAP门;批量更新策略在映射阶段考虑即将执行的量子操作序列,预先执行批量的SWAP操作。二者综合可以减少整个电路执行过程中的SWAP数量,以最大程度减少映射开销。实验结果显示,CSOS优化方式可以平均减少38.1%的插入SWAP门数量,并降低约12%的硬件门计数开销。

不确定环境下收费与信息对路径选择行为影响的实验研究

在不确定环境下考虑信息发布与拥挤收费两种措施,基于交通网络均衡,推导了两路径简单路网的最优收费模型,并推广至一般路网。基于两路径路网设计逐日路径选择行为实验,分析了两种措施对路径选择行为的影响。结果表明:完美信息与收费的实施均能减少流量波动,单独实施收费的效果最佳,但只有两种措施相结合时,路网流量趋于用户均衡的趋势最稳定;完美信息会增加路径切换行为,而收费可有效抑制大幅的路径切换;无收费时路径切换往往会造成出行成本增加,而收费时则相反;收费与信息结合时的出行时间比两种措施单独实施时小。

政策工具视角下我国美育教育的价值追求与实现

美育政策对学校美育实践具有指导作用。政策工具是政策文本、目标和行动之间的连接机制。文章对1985年-2022年间美育政策及相关文献进行梳理,通过工具分类、文本界定和政策编码,遵循“政策工具——政策内容”二维矩阵分析框架,以33份美育政策文本进行分析和探究,我国美育政策工具选择存在环境型政策工具影响作用明显,供给性政策工具的推动作用不够显著,需求型政策工具拉动作用欠缺等。据此,可通过提高美育政策实施路径中的融合度,全面细化美育政策中有关法制管理与措施,优化政策工具的精准度,增强美育政策的灵活性等路径优化我国美育政策工具。

一种螺旋更新的正弦余弦算法

针对基本正弦余弦算法在处理函数优化问题时优化精度低、易陷入局部最优、收敛速度慢等问题,提出一种螺旋更新的正弦余弦算法。首先,非线性调整参数,平衡全局勘探和局部开发能力;其次,利用螺旋更新策略,改进原算法搜索方式,提高优化精度,增强全局搜索能力,防止陷入局部最优;最后,采用贪婪选择与高斯变异策略,提高收敛速度和优化精度。采用23个基本测试函数进行仿真实验,并与其它几种算法进行比较,实验结果表明改进算法具有较高的优化性能。

计及多方利益的“车-路-网”三层充电优化方法

随着电动汽车(EVs)保有量逐年增加,充电需求日益增大,短时间内大规模EVs集中充电不仅会给电网稳定运行带来风险和负担,还会造成路网交通拥堵。为了缓解上述问题,提出一种兼顾配电网、路网和车主三方利益的“车-路-网”三层充电优化方法。通过三层优化模型的相互传递与配合,对配电网的等效负荷方差和有功网损、路网的通畅情况、车主的充电总成本进行协调优化。以杭州市某区域路网为例,结合IEEE-33节点配电网系统进行仿真。通过三层充电优化,系统等效负荷方差降低72.82%,有功网损降低83.41%,车主充电总成本减少8.89%。仿真结果表明,所提优化方法能够合理安排EVs充电,可以在平抑负荷波动和降低有功网损的同时,缓解路网拥堵和降低车主充电总成本。

考虑个体异质性的大型活动参与意向选择行为

为研究个体异质性对于大型活动参与意向选择的影响,构建了基于潜在类别选择模型的大型活动参与意向选择模型。使用D-optimal设计法生成了大型活动参与意向调查中的选择情景,并假设以天津市奥体中心为大型活动举办地,对天津市居民开展了大型活动参与意向调查。根据调查获取的数据,使用EM算法对模型进行了参数估计。模型结果表明:天气、活动时间、票价、活动类型、政策复杂度等是影响大型活动参与意向选择的主要因素。模型将潜在的大型活动参与者划分为天气敏感型群体、活动时间敏感型群体、票价敏感型群体3类,分别占样本总体的30%、15%、55%,区分度为0.81,其中类别1对天气类型最为敏感,对活动举办时间和票价不敏感;类别2对活动举办时间最为敏感,对天气的敏感程度较低;类别3对票价最为敏感,对活动时间和政策复杂度不敏感。

机会约束的多选择背包问题的遗传算法求解

机会约束的多选择背包问题(CCMCKP)是一类具有重要应用价值的NP难组合优化问题,但目前还缺乏关于该问题求解方法的专门研究。为此,提出首个CCMCKP的求解框架,并基于该框架构建了两种求解方法:基于动态规划的RA-DP和基于遗传算法的RA-IGA。RA-DP是精确求解方法,具有最优性保证,但是在可接受的时间(1 h)内仅能求解小规模问题样例;相较而言,RA-IGA是近似求解方法,具有更好的可扩放性。仿真实验结果验证了所提求解方法的性能:在小规模问题样例上,RA-DP和RA-IGA都可以找到最优解;在中大规模问题样例上,RA-IGA表现出了比RA-DP显著更高的求解效率,它总是可以在给定时间(1 h)内快速获得可行解。在CCMCKP的后续研究中,RA-DP和RA-IGA可作为基准对比方法,而实验工作中所构建的测试样例集可作为该问题的标准测试集。

基于多重选择目标规划(MCGP)模型的商业银行资产负债管理优化

在大数据时代,多目标决策模型不仅有助于金融行业的数字化转型,也能提高行业风险控制能力。商业银行资产负债管理以安全性、流动性和盈利性的均衡为主要目标,是银行风险管理的重要方面和银行经营的灵魂。针对资产负债管理中存在的多个相互冲突的目标以及各目标值的不确定性,建立基于多重选择目标规划的商业银行资产负债管理优化模型进行实证研究。研究结果表明,相较于传统的目标规划方法,多重选择目标规划模型具有更高的灵活性,可以避免目标值的低估,实现更理想的结果;运用多重选择目标规划模型可有效优化商业银行资产负债结构,更好地平衡风险与收益,同时满足管理层的期望。

Simulation-Based Optimization for the Fast Fashion Replenishment

Fast fashion is a commercial pattern which provides fashionable clothes at affordable price.This mode needs rapid response supply chain to respond to varying fashion trends.New styles are introduced in every sale period to cover fashion trends.In order to maximize profits,replenishment quantity of each style should be decided in every period.The purchasing and replenishing process over multiple periods based on uncertainty customer demand is modeled,which is formulated by a stochastic choice process.Heterogeneous consumers visit a store in a stochastic sequence and choosing dynamically from the available fashion styles(buy or not buy) according to a utility maximization criterion.The purchase process in a retail shop for multi-period is simulated.An algorithm which combines simulated anneal(SA) with gradient estimation is proposed to find the optimal replenishing strategy from the simulation program.

基于多源数据的电动汽车充电设施布局优化方法研究

针对电动汽车充电设施布局与电动汽车用户需求平衡性问题,提出了一种基于多源数据的电动汽车充电设施布局优化方法。首先,基于兴趣点位置、用户产生充电需求时的地点、需求偏好等多源数据构建电动汽车充电站双层布局优化模型。上层模型以社会总成本为目标函数对电动汽车充电站进行选址定容优化;下层模型以寻址距离、寻址时间、拥挤程度3个指标搭建用户最佳选择模型;然后,利用遗传算法对双层规划模型进行求解,获得电动汽车充电站布局优化方案。最后,通过实例验证了所提方法的可行性。