GLOBAL CONVERGENCE RESULTS OF A THREE TERM MEMORY GRADIENT METHOD WITH A NON-MONOTONE LINE SEARCH TECHNIQUE

In this paper, a new class of three term memory gradient method with non-monotone line search technique for unconstrained optimization is presented. Global convergence properties of the new methods are discussed. Combining the quasi-Newton method with the new method, the former is modified to have global convergence property. Numerical results show that the new algorithm is efficient.

A New Genetic Algorithm Based on Niche Technique and Local Search Method

The genetic algorithm has been widely used in many fields as an easy robust global search and optimization method. In this paper, a new generic algorithm based on niche technique and local search method is presented under the consideration of inadequacies of the simple genetic algorithm. In order to prove the adaptability and validity of the improved genetic algorithm, optimization problems of multimodal functions with equal peaks, unequal peaks and complicated peak distribution are discussed. The simulation results show that compared to other niching methods, this improved genetic algorithm has obvious potential on many respects, such as convergence speed, solution accuracy, ability of global optimization, etc.

The global convergence of the non-quasi-Newton methods with non-monotone line search

The non-quasi-Newton methods for unconstrained optimization was investigated. Non-monotone line search procedure is introduced, which is combined with the non-quasi-Newton family. Under the uniform convexity assumption on objective function, the global convergence of the non-quasi-Newton family was proved. Numerical experiments showed that the non-monotone line search was more effective.

A Non-Monotone Trust Region Method with Non-Monotone Wolfe-Type Line Search Strategy for Unconstrained Optimization

In this paper, we propose and analyze a non-monotone trust region method with non-monotone line search strategy for unconstrained optimization problems. Unlike the traditional non-monotone trust region method, our algorithm utilizes non-monotone Wolfe line search to get the next point if a trial step is not adopted. Thus, it can reduce the number of solving sub-problems. Theoretical analysis shows that the new proposed method has a global convergence under some mild conditions.

Enhanced Cuckoo Search Optimization Technique for Skin Cancer Diagnosis Application

Skin cancer segmentation is a critical task in a clinical decision support system for skin cancer detection.The suggested enhanced cuckoo search based optimization model will be used to evaluate several metrics in the skin cancer pic-ture segmentation process.Because time and resources are always limited,the proposed enhanced cuckoo search optimization algorithm is one of the most effec-tive strategies for dealing with global optimization difficulties.One of the most significant requirements is to design optimal solutions to optimize their use.There is no particular technique that can answer all optimization issues.The proposed enhanced cuckoo search optimization method indicates a constructive precision for skin cancer over with all image segmentation in computerized diagnosis.The accuracy of the proposed enhanced cuckoo search based optimization for melanoma has increased with a 23%to 29%improvement than other optimization algorithm.The total sensitivity and specificity attained in the proposed system are 99.56%and 99.73%respectively.The proposed method outperforms by offering accuracy of 99.26%in comparisons to other conventional methods.The proposed enhanced optimization technique achieved 98.75%,98.96%for Dice and Jaccard coefficient.The model trained using the suggested measure outperforms those trained using the conventional method in the segmentation of skin cancer picture data.

Optimal Allocation of a Hybrid Wind Energy-Fuel Cell System Using Different Optimization Techniques in the Egyptian Distribution Network

This paper presents an optimal proposed allocating procedure for hybrid wind energy combined with proton exchange membrane fuel cell (WE/PEMFC) system to improve the operation performance of the electrical distribution system (EDS). Egypt has an excellent wind regime with wind speeds of about 10 m/s at many areas. The disadvantage of wind energy is its seasonal variations. So, if wind power is to supply a significant portion of the demand, either backup power or electrical energy storage (EES) system is needed to ensure that loads will be supplied in reliable way. So, the hybrid WE/PEMFC system is designed to completely supply a part of the Egyptian distribution system, in attempt to isolate it from the grid. However, the optimal allocation of the hybrid units is obtained, in order to enhance their benefits in the distribution networks. The critical buses that are necessary to install the hybrid WE/ PEMFC system, are chosen using sensitivity analysis. Then, the binary Crow search algorithm (BCSA), discrete Jaya algorithm (DJA) and binary particle swarm optimization (BPSO) techniques are proposed to determine the optimal operation of power systems using single and multi-objective functions (SOF/MOF). Then, the results of the three optimization techniques are compared with each other. Three sensitivity factors are employed in this paper, which are voltage sensitivity factor (VSF), active losses sensitivity factor (ALSF) and reactive losses sensitivity factor (RLSF). The effects of the sensitivity factors (SFs) on the SOF/MOF are studied. The improvement of voltage profile and minimizing active and reactive power losses of the EDS are considered as objective functions. Backward/forward sweep (BFS) method is used for the load flow calculations. The system load demand is predicted up to year 2022 for Mersi-Matrouh City as a part of Egyptian distribution network, and the design of the hybrid WE/PEMFC system is applied. The PEMFC system is designed considering simplified mathematical expressions. The economics of operation of both WE and PEMFC system are also presented. The results prove the capability of the proposed procedure to find the optimal allocation for the hybrid WE/PEMFC system to improve the system voltage profile and to minimize both active and reactive power losses for the EDS of Mersi-Matrough City.

因特网多元搜索引擎Search X2000的研究

介绍了Internet网络多元搜索引擎SearchX2 0 0 0的基本情况 ,研究了该搜索引擎的使用方法与技巧 ,同时客观地评价了它的优劣得失 。

Optimal Coordinated Search for a Discrete Random Walker

This paper presents the search technique for a lost target. A lost target is random walker on one of two intersected real lines, and the purpose is to detect the target as fast as possible. We have four searchers start from the point of intersection, they follow the so called Quasi-Coordinated search plan. The expected value of the first meeting time between one of the searchers and the target is investigated, also we show the existence of the optimal search strategy which minimizes this first meeting time.

A Multilevel Tabu Search for the Maximum Satisfiability Problem

The maximum satisfiability problem (MAX-SAT) refers to the task of finding a variable assignment that satisfies the maximum number of clauses (or the sum of weight of satisfied clauses) in a Boolean Formula. Most local search algorithms including tabu search rely on the 1-flip neighbourhood structure. In this work, we introduce a tabu search algorithm that makes use of the multilevel paradigm for solving MAX-SAT problems. The multilevel paradigm refers to the process of dividing large and difficult problems into smaller ones, which are hopefully much easier to solve, and then work backward towards the solution of the original problem, using a solution from a previous level as a starting solution at the next level. This process aims at looking at the search as a multilevel process operating in a coarse-to-fine strategy evolving from k-flip neighbourhood to 1-flip neighbourhood-based structure. Experimental results comparing the multilevel tabu search against its single level variant are presented.

Towards More Efficient Image Web Search

With the flood of information on the Web, it has become increasingly necessary for users to utilize automated tools in order to find, extract, filter, and evaluate the desired information and knowledge discovery. In this research, we will present a preliminary discussion about using the dominant meaning technique to improve Google Image Web search engine. Google search engine analyzes the text on the page adjacent to the image, the image caption and dozens of other factors to determine the image content. To improve the results, we looked for building a dominant meaning classification model. This paper investigated the influence of using this model to retrieve more efficient images, through sequential procedures to formulate a suitable query. In order to build this model, the specific dataset related to an application domain was collected;K-means algorithm was used to cluster the dataset into K-clusters, and the dominant meaning technique is used to construct a hierarchy model of these clusters. This hierarchy model is used to reformulate a new query. We perform some experiments on Google and validate the effectiveness of the proposed approach. The proposed approach is improved for in precision, recall and F1-measure by 57%, 70%, and 61% respectively.

Fortified Financial Forecasting Models Based on Non-Linear Searching Approaches

The paper＇s aim is how to forecast data with variations involving at times series data to get the best forecasting model. When researchers are going to forecast data with variations involving at times series data （i.e., secular trends, cyclical variations, seasonal effects, and stochastic variations）, they believe the best forecasting model is the one which realistically considers the underlying causal factors in a situational relationship and therefore has the best ＂track records＂ in generating data. Paper＇s models can be adjusted for variations in related a time series which processes a great deal of randomness, to improve the accuracy of the financial forecasts. Because of Na＇fve forecasting models are based on an extrapolation of past values for future. These models may be adjusted for seasonal, secular, and cyclical trends in related data. When a data series processes a great deal of randomness, smoothing techniques, such as moving averages and exponential smoothing, may improve the accuracy of the financial forecasts. But neither Na＇fve models nor smoothing techniques are capable of identifying major future changes in the direction of a situational data series. Hereby, nonlinear techniques, like direct and sequential search approaches, overcome those shortcomings can be used. The methodology which we have used is based on inferential analysis. To build the models to identify the major future changes in the direction of a situational data series, a comparative model building is applied. Hereby, the paper suggests using some of the nonlinear techniques, like direct and sequential search approaches, to reduce the technical shortcomings. The final result of the paper is to manipulate, to prepare, and to integrate heuristic non-linear searching methods to serve calculating adjusted factors to produce the best forecast data.

地震灾害现场搜救方法与策略研究——以中国救援队土耳其地震现场救援为例

总结中国救援队在土耳其地震现场开展的多场救援行动中常用的搜救技术遇到的困难和挑战,中国救援队针对所遇到的困难和问题采取的搜索与营救技术和方法等案列,开展了案列分析,分析了各项搜救技术及其综合运用的优缺点,并对救援队在搜救技术方面提出了进一步的改进建议。

基于预训练模型的漏洞信息检索系统研究

[研究目的]威胁情报中漏洞信息是指有关网络、系统、应用程序或供应链中存在的漏洞的信息。目前搜索引擎在漏洞信息检索上存在短板,利用预训练模型来构建漏洞检索系统可以提高检索效率。[研究方法]以公开的漏洞信息作为数据来源,构建了一个问答数据集,对Tiny Bert进行增量预训练。使用模型对于每个查询向量化,并把漏洞信息构建成faiss向量数据库,利用HNSW索引进行多通道和单通道召回检索。然后对模型进行对比学习微调生成双塔和单塔模型,利用双塔召回和单塔精排构建了一个简易的知识检索系统。[研究结论]实验结果表明,预训练模型可以显著地提升检索性能,对比学习微调的双塔模型在构建的漏洞信息测试集中TOP1召回率为92.17%。通过漏洞信息领域的检索实践,对构建威胁情报的检索系统提供了参考。

Big graph search： challenges and techniques

On one hand, compared with traditional rela- tional and XML models, graphs have more expressive power and are widely used today. On the other hand, various ap- plications of social computing trigger the pressing need of a new search paradigm. In this article, we argue that big graph search is the one filling this gap. We first introduce the ap- plication of graph search in various scenarios. We then for- malize the graph search problem, and give an analysis of graph search from an evolutionary point of view, followed by the evidences from both the industry and academia. After that, we analyze the difficulties and challenges of big graph search. Finally, we present three classes of techniques to- wards big graph search： query techniques, data techniques and distributed computing techniques.

求解加权偏MaxSAT问题的通用子句加权方法

最大可满足性问题(Maximum Satisfiability Problem,MaxSAT)是著名的可满足性问题(Satisfiability Problem,SAT)的优化形式,也是一个经典的NP难组合优化问题.加权偏MaxSAT(Weighted Partial MaxSAT,WPMS)是最一般的一类MaxSAT问题,其中包含了必须要满足的硬子句,对应了优化问题中的约束条件,以及带权重的软子句,对应了优化问题中的优化目标.WPMS旨在满足所有硬子句的同时最大化被满足软子句的权重之和.工业场景中和学术领域中的许多优化问题都能够转化成WPMS问题进行求解,因此WPMS具有广泛的应用领域和重要的研究意义.局部搜索方法是求解WPMS问题的一种著名且被广泛研究的非完备方法.子句加权技术是WPMS局部搜索算法中常用的一种有效且关键的技术,通过为子句赋予动态权重并在搜索过程中更新它们以引导搜索方向,帮助算法逃离局部最优.最先进的WPMS局部搜索算法都提出或采用了有效的子句加权技术,以帮助它们在不同的解空间中搜索.然而,现有的子句加权技术仅根据当前局部最优解更新子句动态权重,而未考虑任何历史信息,可能导致子句加权的视野局限,对搜索方向的引导不够准确.为了解决这一问题,提出了一种新的子句加权技术,称为Hist-Weighting(Clause Weighting with Historical Information),同时考虑了当前及历史信息来更新子句的动态权重,以改进子句加权机制和局部搜索算法的搜索精度和效率.具体而言,Hist-Weighting为那些同时被当前和历史局部最优解所不满足的子句赋予更大的动态权重增量,使算法更倾向于满足那些久未被满足且难以被满足的子句,提高子句加权的准确度.此外,在Hist-Weighting中,子句动态权重的增量能够根据子句中的变元得分自适应地调整,使子句加权更具有灵活性.Hist-Weighting还为子句动态权重的增量设置了上下限,保证了子句加权的稳定性.为了评估所提出的Hist-Weighting子句加权技术的性能,将其应用于三种最先进的WPMS局部搜索算法,即BandMaxSAT、SATLike3.0和CCEHC.在近五届 MaxSAT国际算法竞赛 MaxSAT Evaluation非完备组的所有WPMS算例上的实验结果表明,应用Hist-Weighting技术的改进算法相比于原算法在获胜算例数上能够提升约10%至60%,体现了所提出的Hist-Weighting子句加权技术在求解WPMS问题时的有效性.此外,通过将应用了 Hist-Weighting的改进局部搜索算法与其变体算法对比以进行消融实验,表明了 Hist-Weighting中限制动态权重增量上下限,以及使动态权重增量根据变元得分自适应调整的机制的有效性.

Compressive strength prediction and optimization design of sustainable concrete based on squirrel search algorithm-extreme gradient boosting technique

Concrete is the most commonly used construction material.However,its production leads to high carbon dioxide(CO_(2))emissions and energy consumption.Therefore,developing waste-substitutable concrete components is necessary.Improving the sustainability and greenness of concrete is the focus of this research.In this regard,899 data points were collected from existing studies where cement,slag,fly ash,superplasticizer,coarse aggregate,and fine aggregate were considered potential influential factors.The complex relationship between influential factors and concrete compressive strength makes the prediction and estimation of compressive strength difficult.Instead of the traditional compressive strength test,this study combines five novel metaheuristic algorithms with extreme gradient boosting(XGB)to predict the compressive strength of green concrete based on fly ash and blast furnace slag.The intelligent prediction models were assessed using the root mean square error(RMSE),coefficient of determination(R^(2)),mean absolute error(MAE),and variance accounted for(VAF).The results indicated that the squirrel search algorithm-extreme gradient boosting(SSA-XGB)yielded the best overall prediction performance with R^(2) values of 0.9930 and 0.9576,VAF values of 99.30 and 95.79,MAE values of 0.52 and 2.50,RMSE of 1.34 and 3.31 for the training and testing sets,respectively.The remaining five prediction methods yield promising results.Therefore,the developed hybrid XGB model can be introduced as an accurate and fast technique for the performance prediction of green concrete.Finally,the developed SSA-XGB considered the effects of all the input factors on the compressive strength.The ability of the model to predict the performance of concrete with unknown proportions can play a significant role in accelerating the development and application of sustainable concrete and furthering a sustainable economy.

A Regularization Semismooth Newton Method for P_(0)-NCPs with a Non-monotone Line Search

In this paper,we propose a regularized version of the generalized NCPfunction proposed by Hu,Huang and Chen[J.Comput.Appl.Math.,230(2009),pp.69–82].Based on this regularized function,we propose a semismooth Newton method for solving nonlinear complementarity problems,where a non-monotone line search scheme is used.In particular,we show that the proposed non-monotone method is globally and locally superlinearly convergent under suitable assumptions.We test the proposed method by solving the test problems from MCPLIB.Numerical experiments indicate that this algorithm has better numerical performance in the case of p=5 andθ∈[0.25,075]than other cases.

基于SMOTE-SSA-CNN的开关柜故障诊断方法

开关柜多源监测数据包含丰富的设备运行状态信息,对其进行分析可实现开关柜故障诊断。提出一种基于SMOTE-SSA-CNN的开关柜故障诊断方法。首先,以开关柜电压、电流和温湿度等监测数据为基础,采用合成少数类样本过采样技术(SMOTE)算法对原始数据集进行样本扩充,解决原始数据集中正负样本严重失衡的问题;然后引入麻雀搜索算法(SSA)对卷积神经网络(CNN)的卷积核大小与数量、全连接层神经元数量、学习率等超参数进行优化,提高模型故障诊断结果的准确率;最后,通过算例分析对建立的SMOTE-SSA-CNN模型性能进行评估,验证了所提方法对开关柜故障诊断的有效性,且与传统故障诊断方法相比,所提方法的收敛性较好,精度较高。

一种基于P码的频域直接捕获算法的研究

GPS(Global Positioning System)系统采用典型的CDMA(Code Division Mutiple Access)体制,目前扩频PRN码主要是用于标准定位服务的C/A码和用于精确定位服务的P码.P码信号具有很强的抗干扰和保密能力,而P码的捕获通常是利用C/A码来完成的,因此,实现P码的直接捕获对我国未来发展自己的卫星定位系统具有重要意义.本文从研究信号直接捕获算法角度出发,提出了一种新的P码快速直接捕获算法,并通过仿真实验验证了算法的有效性.仿真结果表明,该算法有效地提高了捕获效率.