Do Search and Selection Operators Play Important Roles in Multi-Objective Evolutionary Algorithms:A Case Study

Multi-objective Evolutionary Algorithm (MOEA) is becoming a hot research area and quite a few aspects of MOEAs have been studied and discussed. However there are still few literatures discussing the roles of search and selection operators in MOEAs. This paper studied their roles by solving a case of discrete Multi-objective Optimization Problem (MOP): Multi-objective TSP with a new MOEA. In the new MOEA, We adopt an efficient search operator, which has the properties of both crossover and mutation, to generate the new individuals and chose two selection operators: Family Competition and Population Competition with probabilities to realize selection. The simulation experiments showed that this new MOEA could get good uniform solutions representing the Pareto Front and outperformed SPEA in almost every simulation run on this problem. Furthermore, we analyzed its convergence property using finite Markov chain and proved that it could converge to Pareto Front with probability 1. We also find that the convergence property of MOEAs has much relationship with search and selection operators.

NONFERROUS METALS BECOMES OBJECTS OF WORLD 6 MAJOR LITERATURE SEARCH SYSTEMS

Press & Publishing Journal (PPJ),March 6,1991:Upon PPJ edit-orial office's request,Institute of Scientific and Technological lnfor-mation of China (ISTTC) made a survey on the situation that how manytheses from China scientific and technological journals have been sele-cted by the 6 most important search systems (Science Literature Index,

基于Elasticsearch的挂号系统设计与实现

目的:设计并实现基于Elasticsearch的挂号系统,解决传统挂号系统预约挂号方式单一和搜索挂号平台信息片面等问题,以满足患者日益增长的多元化和智能化就诊需求。方法:采用浏览器与服务器(B/S)架构实现挂号系统设计,前端使用有机对象描述语言(NOODL)渲染界面,并通过Typescript实现函数方法和接口数据交互,Elasticsearch搜索引擎实现高效的搜索功能。后端通过基于边的云对象存储(ECOS)系统处理数据请求,并将业务数据存储在MySQL数据库中。系统包括应用层、服务层和存储层3层架构,可实现登录注册、搜索预约、链接预约、扫码预约、医生设置预约信息等功能。结果:基于Elasticsearch的挂号系统可实现线上搜索预约、医院专属链接预约、扫码预约等多种预约挂号方式,适用于Web和移动设备等多个平台。截止2023年6月,系统拥有医生219人,服务19903例患者,完成预约62737次,为患者就医节省大量时间,提高了医院运转效率。结论:基于Elasticsearch的挂号系统能够满足患者的多元化和智能化就诊需求,为患者提供全面、准确及智能的预约诊疗服务,有效提高诊疗效率。

Design of Railway Computer Interlocking Search Algorithm and Implementation of Interlocking Software

The computer interlocking system has a wide application in realizing interlocking control between the switch, the signal, and the track circuit in station. Due to the similarity between the binary tree and the station-type data structure, the actual station route search method and the recursive algorithm are combined inorder to realize the computer interlocking route search. On this basis, through the design of switch class, track circuit class and signal machine class, by using C++ object-oriented the management of station data structure and entity object are realized, and then the crowding alarm, switch operations and so on in computer interlocking software. Taking the implementation of 5# station computer interlocking software as an example and based on are realized C++ the object-oriented computer interlocking software is written to provide a reference for realizing the railway computer interlocking training system function.

Scheduling Optimization of Space Object Observations for Radar

An optimizing method of observation scheduling based on time-division multiplexing is proposed in this paper,and its efficiency is verified by outdoor experiments. The initial observation scheduling is first obtained by using a semi-random search algorithm,and secondly the connection time pair（ CTP） between adjacent objects is optimized by using a genetic algorithm. After obtaining these two parameters,the final observation scheduling can be obtained. According to pre-designed tracks between each adjacent objects in observation order,the seamless observation of neighboring targets is derived by automatically steering the antenna beam,so the observation efficiency is improved.

A Hybrid Multi-Objective Evolutionary Algorithm for Optimal Groundwater Management under Variable Density Conditions

In this paper, a new hybrid multi-objective evolutionary algorithm （MOEA）, the niched Pareto tabu search combined with a genetic algorithm （NPTSGA）, is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability contenting with intensified search in a local area. Moreover, the overall searching ability of tabu search （TS） based MOEAs is very sensitive to the neighborhood step size. The NPTSGA is developed on the thought of integrating the genetic algorithm （GA） with a TS based MOEA, the niched Pareto tabu search （NPTS）, which helps to alleviate both of the above difficulties. Here, the global search ability of the NPTS is improved by the diversification of candidate solutions arising from the evolving genetic algorithm population. Furthermore, the proposed methodology coupled with a density-dependent groundwater flow and solute transport simulator, SEAWAT, is developed and its performance is evaluated through a synthetic seawater intrusion management problem. Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives. A key conclusion of this study is that the NPTSGA keeps the balance between the intensification of nondomination and the diversification of near Pareto-optimal solutions along the tradeoff curves and is a stable and robust method for implementing the multi-objective design of variable-density groundwater resources.

A GRASP Algorithm for Multi-objective Circuit Partitioning

Circuit partitioning plays a crucial role in very large-scale integrated circuit (VLSI) physical design automation. With current trends, partitioning with multiple objectives which includes cutsize, area, delay, and power obtains much concentration. In this paper, a multi-objective greedy randomized adaptive search procedure (GRASP) is presented for simultaneous cutsize and circuit delay minimization. Each objective is assigned a preference or weight to direct the search procedure and generate a variety of efficient solutions by changing the preference. To get a good initial partition with minimal cutsize and circuit delay, the gain of each module in a circuit is computed by considering both signal nets and circuit delay. The performance of the proposed algorithm is evaluated on a standard set of partitioning benchmark. The experimental results show that the proposed algorithm can generate a set of Pareto optimal solutions and is efficient for tackling multi-objective circuit partitioning.

考虑运输时间的混合流水车间绿色生产调度

针对运输时间对混合流水车间绿色生产调度的影响这一问题,以最大完工时间、生产能耗及生产成本为优化目标,提出一种改进的多目标麻雀搜索算法(Improved Multi-Objective Sparrow Search Algorithm,IMOSSA)进行求解,参考非支配排序将种群适应度值进行划分、引入正余弦策略提高解集质量、加入多项式变异算子和Levy飞行,提高解集的收敛速度和全局搜索能力,避免陷入局部最优。而后设计16种测试算例,将IMOSSA与其他多目标优化算法进行对比,验证了IMOSSA求解的优越性。最后,以某实际生产车间为例,将其生产调度划分为4种模式,证明算法求解的实用性。

非冷链商品配送路径优化研究--以京东配送为例

如何有效降低商品配送成本是物流企业的关注重点,学界已就带时间窗的商品配送路径优化算法展开了相关研究,但相关算法还存在着过早陷入局部最优或无法收敛的问题。由此提出一种改进的变邻域遗传搜索算法(VNS-GA),以非冷链商品配送为研究对象,构造起求解物流配送车辆路径规划的数学模型。首先,以配送成本和缺货惩罚成本的最小化作为实现目标,构建了包括车辆使用成本、配送运输成本和时间窗口惩罚成本的配送路径优化模型;其次,运用变邻域遗传优化算法来实现多目标物流配送路径的优化;最后,以京东某北京配送中心的物流配送为例,运用MATLAB软件对VNS-GA算法模型的科学性及有效性进行仿真验证。经实证,VNS-GA算法与传统算法相比具有更好的全局和局部搜索能力。研究期望为配送车辆调度与配送路径规划提供更优的路径选择模型,从而降低物流配送成本和减少便利店因缺货造成的损失。

基于低碳物流的危化品仓库堆垛布局优化研究

为保证危化品仓库安全的同时有效减少碳排放并提升经济效益,建立危险指数最小、物料搬运量最小和碳排放成本最小的危化品仓库堆垛布局多目标优化模型,采用改进的粒子群-禁忌搜索混合算法对模型进行求解。该算法在传统粒子群算法的基础上加入多点变异操作,并在粒子群算法得出解的基础上加入禁忌搜索算法,提高算法跳出局部最优解的能力。研究结果表明:利用本文建立的多目标优化模型及改进算法,危险指数、物料搬运量和碳排放成本均有所下降,解集质量较高,从而在保证危化品安全的情况下,有效降低物料搬运量及碳排放成本。研究结果可为危化品企业对仓库内部碳排放量的影响因素和数值计算以及危化品仓库安全性的界定提供参考与借鉴。

改进布谷鸟算法在装配序列规划中的应用研究

针对装配序列规划问题,建立考虑装配序列的几何可行性、稳定性、聚合性、重定向性的装配关系模型以及基于适应度函数的装配序列优化数学模型。提出一种改进布谷鸟算法对装配序列规划问题进行求解,采用随机键和最小位置规则的方法设计基于零件编号、装配方向、装配工具的3层编码方案;设计基于最小装配成本的初始化策略与随机初始化策略相结合的混合种群初始化策略,提高种群质量;改进种群进化和搜索方式,将种群分为3个子群,并分别采用自适应步长飞行、标准步长飞行和交叉、变异的方式进行种群更新,提高算法的收敛速度和求解精度。最后通过实例应用及与其他算法的比较,验证了所提出的改进布谷鸟算法在求解装配序列规划问题上的有效性和优越性。

An Effective Algorithm for Tank Objects Identification in a Complex Background

An effective algorithm for tank objects identification in a complex background that fea-tures the perceptual organization is proposed in this paper.With multi-window architecture,the algorithm consists of two parts:coarse recognition and detailed recognition.Based onprior knowledge,coarse recognition scans the entire image data,then gets the target-kerneland its interesting window to direct detailed processing.Moreover,the detailed recognitionexecutes a depth-first search which retrieves locally around the target-kernel in the windowaccording to the rule of similarity measure.Experimental results show that the algorithm canidentify tank objects in a complicated scene effectively.

面向高维投资组合的多目标优化算法

针对高维投资组合优化问题,提出了一种基于非支配排序和混合搜索的多目标优化算法。考虑到现有进化算法在大规模问题处理上受限于其广泛的搜索空间,引入了基于分解的策略。该策略通过分析个体与参考点的距离,有效地将种群划分为三个子群体。为提升种群多样性并避免局部最优,算法结合了个体的位置特征,并采用了混合局部和全局搜索策略。此外,通过基于分解的双重环境选择机制,有效生成优质解。在包含100、500和1000个决策变量的LSMOP实验中,该算法展现出超越多个先进进化算法的性能。最后,应用该算法于包含交易成本的CVaR模型,并与其他三种多目标进化算法进行比较,进一步证实了其在实际应用中的优势。

海浪上下文信息补偿小目标检测算法

针对海上搜救图像中遇难人员在水面露出的面积小并且容易受到海浪反光、雨雾天气等恶劣环境影响,导致特征提取困难的问题,提出一种海浪上下文信息补偿小目标检测算法。首先,通过基于滑动窗口的图像预处理模块将图像进行裁剪,把关注点集中在目标物体周围,并减少图像中的无关区域,降低了计算量并提高了准确率;其次,提出一种海浪上下文模块,首次通过分析海浪的运动方向和强度,提取海浪上下文信息来辅助海上搜救小目标检测,提高检测精度。在SeaDronesSeev1和SeaDronesSeev2数据集上的实验结果表明,所提算法平均精度分别达到了73.29%和87.81%,相比YOLOv7-tiny算法,平均精度分别提高了21.84%和6.5%。所提算法提高了海上搜救小目标的检测精度,提高了海上搜救的效率。

城市轨道交通乘务交路方案编制的禁忌搜索优化方法

针对城市轨道交通普遍采用轮乘制、日班和两头班混合等特点,对城市轨道交通乘务交路方案编制问题进行研究,旨在降低乘务成本并提高乘务效率.首先,通过分析城市轨道交通乘务交路方案的构成要素,考虑值乘时长、值乘片段接续时间和接续地点等要求,构建值乘片段、值乘任务构成和乘务规则约束,由此建立以乘务组数量和总接续时间等综合指标最小化为目标函数的多目标0-1整数规划模型.然后,针对该模型设计禁忌搜索(Tabu Search,TS)算法进行求解,以先到先走(First-In-First-Out,FIFO)的就近指派原则得到初始解,并设计4种邻域变换策略,以提高邻域解的多样性,并构建基于多邻域结构的禁忌搜索求解算法,实现对乘务交路方案的优化.最后,以广州地铁7号线的乘务交路方案为例进行实例验证.研究结果表明:优化方案较实际运营方案的乘务作业段数和总接续时间分别降低了20%和4.94%,每个乘务作业段值乘列车数量从8.2列增加到10.3列,有效驾驶时间从5.4 h增加到5.8 h,优化方案的各项指标均得到显著提升.研究成果可以为城市轨道交通乘务计划编制提供理论和方法支持.

基于改进跳点搜索和蚁群算法的机器人多目标点巡检规划

针对移动机器人的多目标点巡检规划问题,本文提出了一种融合改进跳点搜索算法(JPS)与蚁群算法(ACO)的路径规划算法.首先,在JPS算法的评估函数中引入角度引导因子,使路径具有更强的导向性;然后,综合考虑路径距离、平滑度、安全性对评估函数的影响,以获得综合性能更优的路径;其次,提出了双向的逆向跳点剔除规则,筛除了多余节点,从而进一步降低路径长度并提高路径平滑度;最后,将多目标优化得到的路径综合性能替代传统旅行商问题(TSP)中的距离因子,并使用自适应蚁群算法来实现多巡检点的路径规划问题.仿真结果表明,改进JPS算法与传统JPS算法相比,具有更好的综合性能;同时应用于多巡检点规划时,具有更强的有效性和实用性.

考虑碳排放的分布式电源优化配置

对分布式电源接入配电网进行合理的优化配置,能在兼顾运营商和用户利益的同时,改善系统整体电压分布。建立了综合考虑分布式电源投资成本、用户购电成本、网损费用和碳排放费用的多目标优化模型。利用改进层次分析法确定各目标的权重,进而转化为单目标函数规划问题。针对天牛须算法个体单一性在解决高维复杂问题时精度低,优化效果不佳的问题,提出了一种改进天牛须粒子群算法,利用混沌映射对参数进行调整,引入动态惯性权重、莱维飞行机制,提高了收敛速度。以IEEE33节点系统为例,将改进天牛须粒子群算法与粒子群算法及天牛须粒子群算法的效果对比,验证改进算法对分布式电源优化配置问题的可行性,有效降低了碳排放费用、用户购电费用,减少了系统网损,改善了系统整体电压分布。

基于声纳搜索累积探测概率的平台路径优化方法

针对面向移动目标的移动搜索平台最优路径研究不足问题,提出一种基于累积搜索概率理论的移动搜索平台路径优化方法。基于传感器性能评价的重要标准之一的累积探测概率(cumulative detection probability,CDP),利用时序相关性模型,即(λ,σ)过程模型,构造单峰CDP计算公式。构建一组目标运动想定,利用贝叶斯后验概率修正目标想定轨迹概率和不同时刻下的CDP。以搜索完成时CDP最大以及CDP达到目标水平时间最短为多目标,在连续时间与连续空间中实现高效搜索,构建面向声纳搜索移动目标的路径优化模型,通过多目标遗传算法给出优化解。与随机搜索模式下的CDP结果比对可以发现,本文方法可以获得更高的CDP,比单目标优化结果所得到的搜索方案具有更高的效率。

引江济淮工程(河南段)多目标水量优化调度

引江济淮工程(河南段)涉及河道、闸泵、管道和调蓄水库,约束条件复杂,常规的优化调度算法难以搜索可行解,求解效率低。选用受水区缺水率平均值最小、泵站总抽水量最小和受水区缺水率标准差最小作为目标函数,从供水保障、供水成本和公平性角度构建多目标水量优化调度模型。基于可行搜索思路,结合逆序演算和顺序演算过程对约束条件进行处理,引入决策系数,通过映射关系使搜索空间保持在可行域中,结合多目标非支配排序遗传算法(non-dominated sorting genetic algorithms,NSGA-II)进行求解,得到Pareto最优解集,并采用熵权法进行方案优选。结果表明,基于可行搜索的NSGA-II算法能够有效求解复杂调度系统的多目标优化问题,综合考虑多个目标的最优方案相对单目标方案更加合理,结果可为引江济淮工程(河南段)运行管理提供决策支撑。

基于机器学习的工业机器人多目标轨迹规划

为满足机器人工作轨迹的多样化需求,以时间、能量及冲击为优化目标提出了一种新的多目标麻雀搜索算法,用于寻找机器人的最优轨迹。首先,通过7次B样条插值方法构造关节空间轨迹,以此确立多目标综合最优轨迹规划模型。其次,采用违反约束度计算、非支配排序以及精英保留来改进麻雀搜索算法,使其能够处理机器人多目标轨迹规划问题。最后,用袋装树分类算法对随机种群内数据进行了筛选,并搭建5层BP神经网络来替代改进多目标麻雀搜索算法中适应度值的数值计算部分,从而提高算法求解效率。通过MATLAB仿真与实验证明了该算法优化所得轨迹的可行性及有效性。