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Congress on Evolutionary Computation

作者： Junqi Zhang Xiong Zhu Wei Wang Jing Yao Department of Computer Science and Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Particle swarm optimization (PSO) is a swarm intelligence technique that optimizes a problem by iterative exploration and exploitation in the search space. However, PSO cannot achieve the preservation of population diversity on solving multimodal optimization problems, and once the swarm falls into local convergence, it cannot jump out of the local trap. In order to solve this problem, this paper presents a fast restarting particle swarm optimization (FRPSO), which uses a novel restarting strategy based on a discrete finite-time particle swarm optimization (DFPSO). Taking advantage of frequently speeding up the swarm to converge along with a greater exploitation capability and then jumping out of the trap, this algorithm can preserve population diversity and provide a superior solution. The experiment performs on twenty-five benchmark functions which consists of single-model, multimodal and hybrid composition problems, the experimental result demonstrates that the performance of the proposed FRPSO algorithm is better than the other three representatives of the advanced PSO algorithm on most of these functions.

关键词： Particle swarm optimization Sociology Statistics Convergence Oscillators Algorithm design and analysis Benchmark testing

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A Method to Check Liveness of WS3PR 33

A Method to Check Liveness of WS3PR

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第三十三届中国控制会议

作者： WANG ShouGuang LIU Miao ZHOU MengChu WU WenHui IEEE School of Information & Electronic Engineering Zhejiang Gongshang University State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University School of Electro-Mechanical Engineering Xidian University The Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102USA

ISBN: (纸本)9781479947249

Petri nets are widely used to model flexible manufacturing systems(FMSs) because they can help analyze the properties and synthesize deadlock-free supervisory controllers of *** system of Simple Sequential Processes with Resources(WS3PR) is an important subclass of Petri nets that can well model many *** work first gives new algorithms to check liveness for a WS3 PR net via its subnet trees and *** the computation complexity for the proposed method is shown in this paper,to be polynomial under certain ***,sufficient conditions for deciding liveness of a WS3 PR are *** example is used to illustrate the results.

关键词： Liveness,flexible manufacturing system,deadlock,Petri net.

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A Self-Learning Clustering Algorithm Based on Clustering Coefficient

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8786卷 79-94页

作者： Zhong, Mingjie Ding, Zhijun Sun, Haichun Wang, Pengwei The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China Department of Computer Science University of Pisa Pisa56127 Italy

This paper presents a novel clustering algorithm based on clustering coefficient. It includes two steps: First, k-nearest-neighbor method and correlation convergence are employed for a preliminary clustering. Then, the results are further split and merged according to intra-class and inter-class concentration degree based on clustering coefficient. The proposed method takes correlation between each other in a cluster into account, thereby improving the weakness existed in previous methods that consider only the correlation with center or core data element. Experiments show that our algorithm performs better in clustering compact data elements as well as forming some irregular shape clusters. It is more suitable for applications with little prior knowledge, e.g. hotspots discovery. © Springer International Publishing Switzerland 2014.

关键词： Clustering algorithms

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An optimized Apriori algorithm based on sparse matrix for intrusion detection

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Open Cybernetics and systemics Journal 2014年第1期8卷 8-11页

作者： Zeng, Xiaohui Lv, Jingxiang Li, Jingzhong Luo, Wenlang Dept. of Computer Science and Technology Jinggangshan University Ji’an China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

The original Apriori algorithm is widely used in the intrusion detection field, but it may consume incredible computing resources in the process of handling network packets. We propose our optimized-Apriori algorithm which can greatly improves the algorithm efficiency by means of reducing the data storage space and the number of frequent item sets. We take full advantage of the characteristics of sparse matrix to save storage space and shorten the running time. The experimental results show that our optimized-Apriori algorithm can make a larger progress in saving the storage space of network data to be processed, even shortening nearly half of the data mining time in the process of intrusion detection. © Zeng et al.

关键词： Data mining

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Robust control reconfiguration of resource allocation systems with Petri nets and integer programming

Robust control reconfiguration of resource allocation system...

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作者： Li, Jun Zhou, Mengchu Guo, Tao Gan, Yahui Dai, Xianzhong Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education China School of Automation Southeast University Nanjing 210096 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102 United States

Supervisory control reconfiguration can handle the uncertainties including resource failures and task changes in discrete event systems. It was not addressed to exploit the robustness of closed-loop systems to accommodate some uncertainties in the prior studies. Such exploitation can cost-efficiently achieve reconfigurability and flexibility for real systems. This paper presents a robust reconfiguration method based on Petri nets (PNs) and integer programming for supervisory control of resource allocation systems (RASs) subject to varying resource allocation relationships. An allocation relationship is seen as a control specification while the execution processes requiring resources as an uncontrolled plant. First, a robust reconfiguration mechanism is proposed. It includes updating the P-invariant-based supervisor and evolving the state of the closed-loop system. The latter adapts to the control specification changes by the self-regulation of the closed-loop system's state. Next, two novel integer programming models for control reconfiguration are proposed, called a reconfiguration model with acceptability and reconfiguration one with specification correction. Since both models integrate the firability condition of transitions, no additional efforts are required for the state reachability analysis. Finally, a hospital emergency service system is used as an example to illustrate them. © 2014 Elsevier Ltd. All rights reserved.

关键词： Integer programming

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A comparative study of redundant feature detection based feature selection methods

A comparative study of redundant feature detection based fea...

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International Conference on computer, Information and Telecommunication systems (CITS)

作者： Xue-Qiang Zeng Qian-Sheng Chen Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Computer Center Nanchang University Nanchang China

As a high dimensional problem, analysis of largescale data sets is a challenging task, where many weakly relevant or redundant features hurt generalization performance of classification models. In order to solve this problem, many effective feature selection methods have proposed to eliminate redundant features in recent years. However, the comparative performances of these redundant feature detection based methods have not been reported yet, which makes the choice of feature selection method relatively difficult for many real applications. The paper presents a novel comparative study of redundant feature detection based feature selection methods. Experiments on several benchmark data sets demonstrate the comparative performances of some state-of-the-arts methods. Based on the extensive empirical results, the minimum Redundancy-Maximum Relevance (mRMR) method has been found to be the best one among all compared feature selection models.

关键词： Breast Colon Lungs Redundancy

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Construction algorithms for index model based on Web page classification

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Journal of Computational Information systems 2014年第2期10卷 655-664页

作者： Zhang, Yangjie Yan, Chungang Wang, Pengwei Sun, Haichun Department of Computer Science and Technology Tongji University Shanghai 201804 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China

Web pages on the Internet are massive, diverse, heterogeneous and redundant. How to organize and manage them effectively is an urgent problem. In this paper, we propose a method to index web pages and build an index model based on web page classification and hyperlink analysis. First, an initialization algorithm is given to construct an index model for an initial set of web pages. Then, considering the dynamics of web pages on the Internet, we propose an incremental updating algorithm which can update an index model incrementally. Through theoretical analysis, the complexity of proposed algorithms shows linear relationships with the scale of web pages and their growth. The experimental results show that initialization algorithm can construct an index model for a fixed number of web pages relatively fast, while the incremental updating algorithm can satisfy the update speed of web pages on the Internet. Thus, the proposed algorithms are feasible and effective. The constructed index model can provide supports for diversified information service systems to enable them to make better use of web resources and provide more valuable services to users. Copyright © 2014 Binary Information Press.

关键词： Websites

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Automated web service composition supporting conditional branch structures

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Enterprise Information systems 2014年第1期8卷 121-146页

作者： Wang, Pengwei Ding, Zhijun zhijun_ding@*** Jiang, Changjun Zhou, Mengchu Department of Computer Science and Technology Tongji University Shanghai 201804 China Key Laboratory of “Embedded System and Service Computing” Ministry of Education Shanghai 201804 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102 USA

The creation of value-added services by automatic composition of existing ones is gaining a significant momentum as the potential silver bullet in service-oriented architecture. However, service composition faces two aspects of difficulties. First, users' needs present such characteristics as diversity, uncertainty and personalisation; second, the existing services run in a real-world environment that is highly complex and dynamically changing. These difficulties may cause the emergence of nondeterministic choices in the process of service composition, which has gone beyond what the existing automated service composition techniques can handle. According to most of the existing methods, the process model of composite service includes sequence constructs only. This article presents a method to introduce conditional branch structures into the process model of composite service when needed, in order to satisfy users' diverse and personalised needs and adapt to the dynamic changes of real-world environment. UML activity diagrams are used to represent dependencies in composite service. Two types of user preferences are considered in this article, which have been ignored by the previous work and a simple programming language style expression is adopted to describe them. Two different algorithms are presented to deal with different situations. A real-life case is provided to illustrate the proposed concepts and methods. [ABSTRACT FROM AUTHOR]

关键词： conditional branch structure control construct service composition user preference web service

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A New Class of Learning Automata for Selecting an Optimal Subset

A New Class of Learning Automata for Selecting an Optimal Su...

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IEEE International Conference on systems, Man, and Cybernetics

作者： JunQi Zhang ZeZhou Li Qi Kang MengChu Zhou Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science and Technology Tongji University Department of Control Science and Engineering Tongji University Department of Electrical and Computer Engineering New Jersy Institute of Technology

ISBN: (纸本)9781479938414

Interacting with a random environment, Learning Automata (LAs) are automata that, generally, have the task of learning the optimal action based on responses from the environment. Distinct from the traditional goal of Learning Automata to select only the optimal action out of a set of actions, this paper considers a multiple-action selection problem and proposes a novel class of Learning Automata for selecting an optimal subset of actions. Their objective is to identify the optimal subset: the top k out of r actions. Based on conventional continuous pursuit and discretized pursuit learning schemes, this paper introduces four pursuit learning schemes for selecting the optimal subset, called continuous equal pursuit, discretized equal pursuit, continuous unequal pursuit and discretized unequal pursuit learning schemes, respectively. In conjunction with a reward-inaction learning paradigm, the above four schemes lead to four versions of pursuit Learning Automata for selecting the optimal subset. The simulation results present a quantitative comparison between them.

关键词： Learning Automata Optimal subset Pursuit algorithm

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Team of Bayesian optimization algorithms to solve task assignment problems in heterogeneous computing systems

Team of Bayesian optimization algorithms to solve task assig...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jie Li JunQi Zhang Qi Kang ChangJun Jiang Ministry of Education China Key Laboratory of Embedded System and Service Computing Department of Computer Science and Technology Tongji University Shanghai Department of Control Science and Engineering Tongji University Shanghai China

ISBN: (纸本)9781479938414

A Bayesian optimization algorithm (BOA) belongs to estimation of distribution algorithms (EDAs). It is characterized by combining a Bayesian network and evolutionary algorithms to solve nearly decomposable optimization problems. BOA is less popularly applied to solve high dimensionality complex optimization problems. A key reason is that the cost of training all dimensions by BOA becomes expensive with the increase of problem dimensionality. Since data are relatively sparse in a high dimensional space, even though BOA can train all dimensions simultaneously, the interdependent relations between different dimensions are difficult to learn. Its search ability is thus significantly reduced. In this paper, we propose a team of Bayesian optimization algorithms (TBOA) to search and learn dimensionality. TBOA consists of multiple BOAs, in which each BOA corresponds to a dimension of the solution domain and it is responsible for the search of this dimension's value region. The proposed TBOA is used to solve the real problem of task assignment in heterogeneous computing systems. Extensive experiments demonstrate that the computational cost of the overall training in TBOA is decreased very significantly while keeping high solution accuracy.

关键词： Bayes methods Sociology Statistics Optimization Computational efficiency Joints Probability distribution

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