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Computational Intelligence and Security (CIS), 2013 9th International Conference on

Date 14-15 Dec. 2013

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Displaying Results 1 - 25 of 187
  • [Front cover]

    Publication Year: 2013 , Page(s): C4
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  • [Title page i]

    Publication Year: 2013 , Page(s): i
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  • [Title page iii]

    Publication Year: 2013 , Page(s): iii
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  • [Copyright notice]

    Publication Year: 2013 , Page(s): iv
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  • Table of contents

    Publication Year: 2013 , Page(s): v - xvii
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  • Organization

    Publication Year: 2013 , Page(s): xviii
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  • Program Committee

    Publication Year: 2013 , Page(s): xix - xx
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  • Additional reviewers

    Publication Year: 2013 , Page(s): xxi - xxii
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  • The Finite Element Analysis on the Multi-bubble Model in the Ship Wake

    Publication Year: 2013 , Page(s): 1 - 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (494 KB) |  | HTML iconHTML  

    The double-bubble model is formed in the spherical coordinates, which is based on the distribution of bubbles in the ship wake. The multi-bubble models are created and the finite element structure models of the multi-bubble are formed and studied by then. A sound source is located in the field point models of the multi-bubble, and the active acoustic characteristic of the models are obtained by the finite element analysis. The experiment of multi-bubble in the ship wake is simulated, which could be used to direct the practical experiment and the application of the theory. View full abstract»

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  • An Approximation Scheme for RNA Folding Structure Prediction Including Pseudoknots

    Publication Year: 2013 , Page(s): 6 - 10
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (593 KB) |  | HTML iconHTML  

    The paper further investigates the computational problem and complexity of predicting Ribonucleic Acid structure. In order to find a way to optimize the Ribonucleic Acid pseudoknotted structure, we investigate the Ribonucleic Acid pseudoknotted structure based on thermal dynamic model, computational methods, minimum free energy are adopted to predict Ribonucleic Acid structure. The contribution of this paper is to obtain an efficient Approximation algorithm for finding RNA pseudoknotted structure, compared with other algorithms, the algorithm takes O(n3) time and O(n2) space. The experimental test in PseudoBase shows that the algorithm is more effective and exact than other algorithms, and the algorithm can predict arbitrary pseudoknots. And we also give a proof of existing 1+e (e>0) Polynomial Time Approximation Scheme(PTAS) in Searching Maximum Number of Stackings. View full abstract»

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  • Glowworm Swarm Optimization Algorithm for Solving Multi-objective Optimization Problem

    Publication Year: 2013 , Page(s): 11 - 15
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (494 KB) |  | HTML iconHTML  

    The glowworm swarm optimization algorithm is used to solve multi-objective optimization problem (MOP-GSO). It is shown by simulation that, MOP-GSO algorithm is effective to solve multi-objective optimization. Compared with NSGA2, it is better in term of the spread of the solutions. View full abstract»

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  • Artificial Bee Colony Algorithm with Two-Stage Eagle Strategy

    Publication Year: 2013 , Page(s): 16 - 20
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (605 KB) |  | HTML iconHTML  

    Pointing at that Artificial Bee Colony Algorithm (ABC) has the defect of slow search speed and low precision, the article proposed an Improved Artificial Bee Colony Algorithm with Two-Eagle Strategy (ETABC) through using a kind of optimization method-Eagle Strategy, and proved the convergence of ETABC. The simulation results show that ETABC is more effective in solving optimization problems. View full abstract»

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  • Optimizing Vaccine Distribution for Different Age Groups of Population Using DE Algorithm

    Publication Year: 2013 , Page(s): 21 - 25
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (648 KB) |  | HTML iconHTML  

    Vaccination is one of the most promising methods to control the epidemic spread by protecting the most vulnerable population and reducing the number of susceptible population who are exposed to the virus. However, the vaccine doses are usually limited and can only be supplied during the epidemic outbreak. How to distribute the vaccines to different populations to reduce the total number of infectious people is important to the public health. In this paper, a DE algorithm is proposed to solve the problem by searching for the optimal strategy to distribute the limited vaccines to different age groups of population. The performance of the algorithm is compared with three strategies in the literature. The results show that the proposed algorithm can offer more effective vaccine distributions significantly. View full abstract»

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  • Dynamic Resource Allocation of TD-LTE System Based on Improved Quantum Evolutionary Algorithm

    Publication Year: 2013 , Page(s): 26 - 30
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (516 KB) |  | HTML iconHTML  

    A new model of time division duplex long term evolution (TD-LTE) system considering crossed time slot (CTS) interference is proposed in this paper because CTS interference may greatly affect system performance. An improved quantum evolutionary algorithm (QEA) is put forward to solve the model efficiently. The proposed QEA enhances the global search capability and has a good convergence due to the increase of a mutation operator based on controlled-not gate. Simulation experiment shows that the proposed QEA is effective to solve the problem. View full abstract»

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  • An Improved Differential Evolution Algorithm for Mixed Integer Programming Problems

    Publication Year: 2013 , Page(s): 31 - 35
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (428 KB) |  | HTML iconHTML  

    This paper proposes an improved differential evolution algorithm, named I-DE, for constrained nonlinear mixed integer programming problems. The new population initialization technology and dynamic non-linear scaling factor are applied to enhance optimization capability of algorithm. We strengthen influence of constraint matrix to deal with constraint of problems. Introduction of special truncation procedure to handle integer restrictions and selection operator based on Deb constraint rules update the population. The test results show that the I-DE algorithm possess higher success rate and precision than MI-LXPM algorithm and has found solutions which are better than the known optimal solution in five problems. View full abstract»

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  • A Middleware-Based Model for Redundant Reader Elimination Using Plant Growth Simulation Algorithm

    Publication Year: 2013 , Page(s): 36 - 40
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (450 KB) |  | HTML iconHTML  

    Eliminating redundant readers is a significant way to improve the performance of the radio frequency identification (RFID) networks. This paper proposes a middleware-based model for redundant reader elimination, which is formulated as a multi-dimensional optimization problem. It uses the data in RFID middleware to distinguish redundant readers and no "write-to-tag" operations are required. The Plant Growth Simulation Algorithm (PGSA) is employed to search the optimal adjustable parameters. The simulation results reveal that the proposed approach outperforms other algorithms in terms of optimization precision and computing time. View full abstract»

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  • An Efficient Genetic Algorithm for Interval Linear Bilevel Programming Problems

    Publication Year: 2013 , Page(s): 41 - 44
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (417 KB) |  | HTML iconHTML  

    This paper deals with a class of interval linear bilevel programming problems, in which some or all of the leader's and follower's objective function coefficients are specified in terms of intervals. The focus of solving this class of problems is on determining the optimal value range when different coefficients of objectives are taken in intervals given. In order to obtain the best and the worst optimal solutions to this class of problems, an efficient genetic algorithm is developed. Firstly, the objective coefficients of the lower level are encoded as individuals using real coding scheme, and the relative intervals are taken as the search space of the genetic algorithm. Secondly, for each encoded individual, a simplified interval linear bilevel program is obtained, in which interval coefficients are simply in the upper level objective function. Finally, the simplified problem is further divided into two linear bilevel programs without interval coefficients and solved by using the optimality theory of linear programming. The optimal values are taken as fitness values, by which the best and the worst optimal solutions can be obtained. In order to illustrate the efficiency of the proposed algorithm, two examples are solved and the results show that the algorithm is feasible and robust. View full abstract»

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  • An Energy-Aware Optimization Model Based on Data Placement and Task Scheduling

    Publication Year: 2013 , Page(s): 45 - 49
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (484 KB) |  | HTML iconHTML  

    Recently, technologies on reducing energy consumption of data centers have drawn considerable attentions. One constructive way is to improve energy efficiency of servers. Aiming at this goal, we propose a new energy-aware optimization model based on the combination of data placement and task scheduling in this paper. The main contributions are: (1)The impact of servers' performance on energy consumption is explored. (2) The model guarantees 100% data locality to save network bandwidth. (3) As tasks involved in cloud computing are usually tens of thousands, in order to solve this large scale optimization model efficiently, specific-design encoding and decoding methods are introduced. Based on these, an effective evolutionary algorithm is proposed. Finally, numerical experiments are made and the results indicate the effectiveness of the proposed algorithm. View full abstract»

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  • Genetic Algorithm Nested with Simulated Annealing for Big Job Shop Scheduling Problems

    Publication Year: 2013 , Page(s): 50 - 54
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (539 KB) |  | HTML iconHTML  

    In so many combinatorial optimization problems, Job shop scheduling problems have earned a reputation for being difficult to solve. Genetic algorithm has demonstrated considerable success in providing efficient solutions to many non-polynomial-hard optimization problems. In the field of job shop scheduling, genetic algorithm has been intensively researched, but it's converge speed is not favorable. To solve this issue, in this paper, we proposed a novel method that is genetic algorithm nested with local search procedure. After crossing and mutation operations in every generation of genetic algorithm, a local search operation be carried out form every population individual. In our experiments, some big benchmark problems were tried with the proposed algorithm for validation, and the results are encouraging. View full abstract»

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  • A Differential Evolution with Two Mutation Strategies for Linear Bilevel Programming Problems

    Publication Year: 2013 , Page(s): 55 - 60
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (112 KB) |  | HTML iconHTML  

    An improved constraint handling technique based on a comparison mechanism is presented, and then it is combined with selection operator in differential evolution to fulfill constraint handling and selection simultaneously. A differential evolution with two mutation strategies based on this new constraint handling technique is developed to solve the linear bilevel programming problems. The simulation results show that the proposed algorithm can find global optimal solutions with less computation burden. View full abstract»

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  • A Parallel Genetic Algorithm for Solving the Probabilistic Minimum Spanning Tree Problem

    Publication Year: 2013 , Page(s): 61 - 65
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (165 KB) |  | HTML iconHTML  

    The probabilistic minimum spanning tree (PMST) problem is NP-complete and is hard to solve. However, it has important theoretical significance and wide application prospect. A parallel genetic algorithm based on coarse-grained model is proposed to solve PMST problem in this paper. Firstly, we discuss several problems of determinant factorization encoding, and develop repairing method for illegal individuals. Secondly, a coarse-grained parallel genetic algorithm, which combines message passing interface (MPI) and genetic algorithm, is designed to solve probabilistic minimum spanning tree problems. Finally, the proposed algorithm is used to test several probabilistic minimum spanning tree problems which are generated by the method introduced in the literature. The statistical data of the test results show that the expectation best solution and average best solution obtained by the proposed algorithm are better than those provided in the literature. View full abstract»

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  • Evolutionary Algorithm Based on Automatically Designing of Genetic Operators

    Publication Year: 2013 , Page(s): 66 - 70
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (443 KB) |  | HTML iconHTML  

    At present there is a wide range of evolutionary algorithms available to researchers and practitioners. Despite the great diversity of these algorithms, virtually all of the algorithms share one feature: they have been manually designed. Can evolutionary algorithms be designed automatically by computer? In this paper, a novel evolutionary algorithm based on automatically designing of genetic operators is presented to address this problem. The resulting algorithm not only explores solutions in the problem space, but also automatically generates genetic operators in the operator space for each generation. In order to verify the performance of the proposed algorithm, comprehensive experiments on 23 well-known benchmark optimization problems are conducted, and the results show that the proposed algorithm can outperform standard Differential Evolution (DE) algorithm. View full abstract»

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  • Research on Related Vehicle Routing Problem for Multiple Depots Basing on Dynamic Constraint

    Publication Year: 2013 , Page(s): 71 - 75
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (417 KB) |  | HTML iconHTML  

    The research based on multi-depot RVRP with road capacity dynamic constraint. Building relevant mathematic model, and brought simulated annealing mechanism into chaos genetic algorithm. The simulated result shows that the proposed algorithm can be applied to solve this problem. View full abstract»

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  • Research on Related Vehicle Routing Problem for Single Distribution Centre Based on Dynamic Constraint

    Publication Year: 2013 , Page(s): 76 - 79
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (404 KB) |  | HTML iconHTML  

    Related Vehicle Routing Problem is another form of Vehicle Routing Problem. RVRP also belongs to NP-Hard, The research based on single distribution center RVRP with road capacity dynamic constraint. Road capacity factor shows as a road condition coefficient, then added it into the objective function. To build a model of single distribution center and single vehicle type RVRP with soft time windows and dynamic constraint. The simulated result shows that the self-adapting chaos genetic algorithm is flexible and feasible to solve this kind of model. View full abstract»

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  • A New Evolutionary Algorithm for Portfolio Optimization and Its Application

    Publication Year: 2013 , Page(s): 80 - 84
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (412 KB) |  | HTML iconHTML  

    Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) are two of the most widely used and important risk measures in financial risk management models. Because VaR and CVaR portfolio optimization models are often nonlinear and non-convex optimization models, traditional optimization methods usually can not get their global optimal solutions, instead, they often get a local optimal solution. In this paper, the uniform design is integrated into evolutionary algorithm to enhance the search ability of the evolutionary algorithm. The resulted algorithm will has a strong search ability and has more possibility to get the global optimal solution. Based on this idea, a new evolutionary algorithm is proposed for VaR and CVaR optimization models. Computer simulations on ten randomly chosen stocks from Shenzhen Stock Exchange in China are conducted and the analysis to the results is given. The experiment results indicate the proposed algorithm is efficient. View full abstract»

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