The repulsion algorithm,a new multistart method for global optimization

This paper proposes a new multistart algorithm to find the global minimum of constrained problems. This algorithm, which in this paper is called the repulsion algorithm, efficiently selects initial design points for local searches. A Bayesian approach provides the stopping rules. The method uses information from the previous sampling points and the corresponding sequences generated by local searches to select new initial points. This approach increases the probability of finding all local minima with fewer local searches. Numerical example problems show that compared with traditional multistart methods, the repulsion algorithm reduces significantly the number of local searches required to find the global minimum.     

A novel hybrid adaptive collaborative approach based on particle swarm optimization and local search for dynamic optimization problems

This paper proposes a novel hybrid approach based on particle swarm optimization and local search, named PSOLS, for dynamic optimization problems. In the proposed approach, a swarm of particles with fuzzy social-only model is frequently applied to estimate the location of the peaks in the problem landscape. Upon convergence of the swarm to previously undetected positions in the search space, a local search agent (LSA) is created to exploit the respective region. Moreover, a density control mechanism is introduced to prevent too many LSAs crowding in the search space. Three adaptations to the basic approach are then proposed to manage the function evaluations in the way that are mostly allocated to the most promising areas of the search space. The first adapted algorithm, called HPSOLS, is aimed at improving PSOLS by stopping the local search in LSAs that are not contributing much to the search process. The second adapted, algorithm called CPSOLS, is a competitive algorithm which allocates extra function evaluations to the best performing LSA. The third adapted algorithm, called CHPSOLS, combines the fundamental ideas of HPSOLS and CPSOLS in a single algorithm. An extensive set of experiments is conducted on a variety of dynamic environments, generated by the moving peaks benchmark, to evaluate the performance of the proposed approach. Results are also compared with those of other state-of-the-art algorithms from the literature. The experimental results indicate the superiority of the proposed approach.     

Improving collision avoidance for mobile robots in partially known environments: the beam curvature method

This paper presents a new approach to obstacle avoidance for mobile robots in cluttered and unknown or partially unknown environments. The method combines a new directional method, called beam method (BM), to improve the performance of a local obstacle avoidance approach called curvature velocity method (CVM). BM calculates the best one-step heading which is used by CVM to obtain the optimal linear and angular velocities. The resulting combined technique is called beam curvature method (BCM).

Different experiments in populated and dynamic environments have proved to be very successful. The method is able to guide the robot safely and efficiently during long time periods. We present some of these results compared with other methods.     

一种基于本地网络的蠕虫协同检测方法

目前已有一些全球化的网络蠕虫监测方法,但这些方法并不能很好地适用于局域网.为此,提出一种使用本地网协同检测蠕虫的方法CWDMLN(coordinated worm detection method based on local nets).CWDMLN注重分析扫描蠕虫在本地网的行为,针对不同的行为特性使用不同的处理方法,如蜜罐诱捕.通过协同这些方法给出预警信息,以揭示蠕虫在本地网络中的活动情况.预警信息的级别反映报警信息可信度的高低.实验结果表明,该方法可以准确、快速地检测出入侵本地网络的扫描蠕虫,其抽取出的蠕虫行为模式可以为协同防御提供未知蠕虫特征.通过规模扩展,能够实施全球网络的蠕虫监控.     

基于智能Agent的网络安全监控系统的研究

提出了一种称为IA－NSM（网络安全监控的智能Agent)的全新方案，它基于智能Agent技术，用于监控和管理Intranet环境下内部结点。IA－NSM是在传统的网络环境中集成了一个灵活的多Agent系统，从而加强了对受控结点非法行为的防范能力，介绍了基于智能Agent技术的网络安全监控系统的应用背景，系统功能，Agent分类和系统模型以及安全策略的制定原则。     

Experimental evaluation of two complementary decentralized event-based control methods

Event-based control aims at reducing the feedback communication effort among the sensors, controllers and actuators in control loops to time instants at which the feedback of information is necessary to meet a desired control performance. This paper presents a new method for the decentralized event-based control of physically interconnected systems and shows its experimental evaluation. The novel method is based on two complementary approaches, called the global and the local approach, which jointly ensure the ultimate boundedness of the closed-loop system. The global approach steers the state of each subsystem into a target region, whereas the local approach keeps the state in this set in spite of exogenous disturbances and the effect of the interconnections to other subsystems. This event-based control method is applied to a continuous flow process to show its practical implementation and to evaluate the analytical results on the basis of experiments.     

Fuzzy weighted support vector regression with a fuzzy partition.

The problem of the traditional support vector regression (SVR) approach, referred to as the global SVR approach, is the incapability of interpreting local behavior of the estimated models. An approach called the local SVR approach was proposed in the literature to cope with this problem. Although the local SVR approach can indeed model local behavior of models better than the global SVR approach does, the local SVR approach still has the problem of boundary effects, which may generate a large bias at the boundary and also need more time to calculate. In this paper, the fuzzy weighted SVR with a fuzzy partition is proposed. Because the concept of locally weighted regression is not used in the proposed approach, the boundary effects will not appear. The proposed method first employs the fuzzy c-mean clustering algorithm to split training data into several training subsets. Then, the local-regression models (LRMs) are independently obtained by the SVR approach for each training subset. Finally, those LRMs are combined by a fuzzy weighted mechanism to form the output. Experimental results show that the proposed approach needs less computational time than the local SVR approach and can have more accurate results than the local/global SVR approaches does.     

Multi-modal function optimization with the ZEDS (zoomed evolutionary dual strategy) algorithmzoomed evolutionary algorithm

This article introduces a new evolutionary algorithm for multi-modal function optimization called ZEDS (zoomed evolutionary dual strategy). ZEDS employs a two-step, zoomed (global to local), evolutionary approach. In the first (global) step, an improved ‘GT algorithm’ is employed to perform a global recombinatory search that divides the search space into niches according to the positions of its approximate solutions. In the second (local) step, a ‘niche evolutionary strategy’ performs a local search in the niches obtained from the first step, which is repeated until acceptable solutions are found. The ZEDS algorithm was applied to some challenging problems with good results, as shown in this article.     

Generation of candidates' tree for the fault diagnosis of discrete event systems

This paper presents a discrete event model-based approach for Fault Detection and Isolation of manufacturing systems. This approach considers a system as a set of independent Plant Elements (PEs). Each PE is composed of a set of interrelated Parts of Plant (PoPs) modeled by a Moore automaton. Each PoP model is only aware of its local behavior. The degraded and faulty behaviors are added to each PoP model in order to obtain extended PoP ones. An extrapolation of Gaussian learning is realized to obtain acceptable temporal intervals between the time occurrences of correlated events. Finally based on the PoP extended models and the links between them, a fault candidates' tree is established for each plant element. This candidates' tree corresponds to a local on-line fault event occurrence observer, called diagnoser. Thus, the diagnosis decision is distributed on each plant element. An application example is used to illustrate the approach.     

Interactive Case-Based Planning for Forest Fire Management

This paper describes an AI system for planning the first attack on a forest fire. This planning system is based on two major techniques, case-based reasoning, and constraint reasoning, and is part of a decision support system called CHARADE. CHARADE is aimed at supporting the user in the whole process of forest fire management. The novelty of the proposed approach is mainly due to the use of a local similarity metric for case-based reasoning and the integration with a constraint solver in charge of temporal reasoning.     

Maximal local interclass embedding with application to face recognition

Dimensionality reduction of high dimensional data is involved in many problems in information processing. A new dimensionality reduction approach called maximal local interclass embedding (MLIE) is developed in this paper. MLIE can be viewed as a linear approach of a multimanifolds-based learning framework, in which the information of neighborhood is integrated with the local interclass relationships. In MLIE, the local interclass graph and the intrinsic graph are constructed to find a set of projections that maximize the local interclass scatter and the local intraclass compactness simultaneously. This characteristic makes MLIE more powerful than marginal Fisher analysis (MFA). MLIE maintains all the advantages of MFA. Moreover, the computational complexity of MLIE is less than that of MFA. The proposed algorithm is applied to face recognition. Experiments have been performed on the Yale, AR and ORL face image databases. The experimental results show that owing to the locally discriminating property, MLIE consistently outperforms up-to-date MFA, Smooth MFA, neighborhood preserving embedding and locality preserving projection in face recognition.     

Triple RRTs: An Effective Method for Path Planning in Narrow Passages

Although Rapidly-exploring Random Trees (RRTs) have been successfully applied in path planning of robots with many degrees of freedom under non-holonomic and differential constraints, rapidly identifying and passing through narrow passages in a robot's configuration space remains a challenge for RRTs-based planners. This paper presents a novel two-stage approach to address the problem of multi-d.o.f. robot path planning in high-dimensional configuration space with narrow corridors. The first stage introduces an efficient sampling algorithm called Bridge Test to find a global roadmap that identifies the critical region. The second stage presents two varieties of RRTs, called Triple-RRTs, to search for a local connection under the guidance of the global landmark. The two-stage strategy keeps a fine balance between global heuristics and local connection, resulting in high performance over the previous RRTs-based path planning methods. We have implemented the Triple-RRTs planners for both rigid and articulated robots in two- and three-dimensional environments. Experimental results demonstrate the effectiveness of the proposed method.     

一种动态环境下移动机器人的路径规划方法

本文提出了在动态环境中，移动机器人的一种路径规划方法，适用于环境中存 在已知和未知、静止和运动障碍物的复杂情况．采用链接图法建立了机器人工作空间模型， 整个系统由全局路径规划器和局部路径规划器两部分组成．在全局路径规划器中，应用遗传 算法规划出初步全局优化路径．在局部路径规划器中，设计了三种基本行为：跟踪全局路径 的行为、避碰的行为和目标制导的行为，采用基于行为的方法进一步优化路径．其中，避碰 的行为是通过强化学习得到的．仿真和实验结果表明所提方法简便可行，能够满足移动 机器人导航的高实时性要求．     

使用均值距离与关联性标记的并行OPTICS算法

针对大数据环境下传统并行密度聚类算法中存在的数据划分不合理,聚类结果准确度不高,结果受参数影响较大以及并行效率低等问题,提出一种MapReduce下使用均值距离与关联性标记的并行OPTICS算法——POMDRM-MR。算法使用一种基于维度稀疏度的减少边界点划分策略（DS-PRBP）,划分数据集;针对各个分区,提出标记点排序识别簇算法（MOPTICS）,构建数据点与核心点之间的关联性,并标记数据点迭代次数,在距离度量中,使用领域均值距离策略（FMD）,计算数据点的领域均值距离,代替可达距离排序,输出关联性标记序列;最后结合重排序序列提取簇算法（REC）,对输出序列进行二次排序并提取簇,提高算法局部聚类的准确性和稳定性;在合并全局簇时,算法提出边界密度筛选策略（BD-FLC）,计算筛选密度相近局部簇;又基于n叉树的并集型合并与MapReduce模型,提出并行局部簇合并算法（MCNT-MR）,加快局部簇收敛,并行合并局部簇,提升全局簇合并效率。对照实验表明,POMDRM-MR算法聚类效果更佳,且在大规模数据集下算法的并行化性能更好。     

一种基于Z曲线的新离群点挖掘算法*

提出一种基于密度的快速查找离群点的算法——基于Z曲线的离群点查找算法(ZOD), 依据Z曲线的构造过程将空间分割成大小相等的网格,沿着曲线延伸方向对网格进行排序,将落在网格中的点映射到一维空间,从而克服了基于网格算法的“维灾”缺点;同时用局部偏离指数指示离群点的偏离程度,又具有识别精度高和偏离程度可度量的优点。理论分析表明,该算法性能优于著名的基于密度的算法;实验结果表明,该算法与其他高维离群点挖掘算法相比,在效率及有效处理的维数方面均有显著提高。     

分块类增广PCA及其在人脸识别中的应用

提出一种分块类增广PCA方法并应用于人脸识别中。对原始图像矩阵进行分块，对各个分块子图像施行自适应的CAPCA方法进行特征提取，将提取到的特征依次整合，从而达到降维的目的。该方法不仅能有效提取图像的局部特征，而且能适应不同的光照条件。实验结果表明，该方法在识别性能上优于CAPCA方法和分块PCA方法。     

Particle swarm optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows

Today, companies need to collect and to deliver goods from and to their depots and their customers. This problem is described as a Vehicle Routing Problem with Mixed Linehaul and Backhaul customers (VRPMB). The goods delivered from the depot to the customers can be alternated with the goods picked up. Other variants of VRP added to VRPMB are Heterogeneous fleet and Time Windows. This paper studies a complex VRP called HVRPMBTW which concerns a logistic/transport society, a problem rarely studied in literature. In this paper, we propose a Particle Swarm Optimization (PSO) with a local search. This approach has shown its effectiveness on several combinatorial problems. The adaptation of this approach to the problem studied is explained and tested on the benchmarks. The results are compared with our previous methods and they show that in several cases PSO improves the results.     

HED-FL: A hierarchical,energy efficient,and dynamic approach for edge Federated Learning

The increasing data produced by IoT devices and the need to harness intelligence in our environments impose the shift of computing and intelligence at the edge, leading to a novel computing paradigm called Edge Intelligence/Edge AI. This paradigm combines Artificial Intelligence and Edge Computing, enables the deployment of machine learning algorithms to the edge, where data is generated, and is able to overcome the drawbacks of a centralized approach based on the cloud (e.g., performance bottleneck, poor scalability, and single point of failure). Edge AI supports the distributed Federated Learning (FL) model that maintains local training data at the end devices and shares only globally learned model parameters in the cloud. This paper proposes a novel, energy-efficient, and dynamic FL-based approach considering a hierarchical edge FL architecture called HED-FL, which supports a sustainable learning paradigm using model parameters aggregation at different layers and considering adaptive learning rounds at the edge to save energy but still preserving the learning model’s accuracy. Performance evaluations of the proposed approach have also been led out considering model accuracy, loss, and energy consumption.