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Meta-heuristic algorithms applied to the optimization of type-1 and type 2 TSK fuzzy logic systems for sea water level prediction

Meta-heuristic algorithms applied to the optimization of typ...

引用

IEEE international workshop on Computational Intelligence & applications (IWCIA)

作者： Nguyen Cong Long Phayung Meesad Faculty of Information Technology King Mongkut's University of Technology North Bangkok Bangkok Thailand

This paper describes an approach using Firefly Algorithm, Particle Swarm optimization and Genetic Algorithm to optimize the parameters of Takagi-Sugeno-Kang (TSK) fuzzy logic system (both type-1 and type-2) in order to find the optimal fuzzy logic system for sea water level prediction. The obtained results of the simulations performed are compared among these optimization algorithms in order to find which one is the best optimization algorithm for sea water level prediction.

关键词： Fuzzy logic optimization Genetic algorithms Prediction algorithms Particle swarm optimization Artificial neural networks Sociology

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Nonzero pattern analysis and memory access optimization in GPU-based sparse LU factorization for circuit simulation

Nonzero pattern analysis and memory access optimization in G...

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3rd workshop on Irregular applications: Architectures and algorithms, IA3 2013 - Held in Conjunction with the international Conference for High Performance Computing, Networking, Storage and Analysis, SC 2013

作者： Chen, Xiaoming Su, Du Wang, Yu Yang, Huazhong Department of Electronic Engineering Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing 100084 China

ISBN: (纸本)9781450325035

The sparse matrix solver is a critical component in circuit simulators. Some researches have developed GPU-based LU factorization approaches to accelerate the sparse solver. But the performance of these solvers is constrained by the irregularities of sparse matrices. This work investigates the nonzero patterns and memory access patterns in sparse LU factorization, and explores the common features to give guidelines on the improvements of the GPU solvers. We further propose a crisscross blocked implementation on GPUs. The proposed method attains average speedups of 1.68x compared with the unblocked method and 2.2x compared with 4-threaded PARDISO, for circuit matrices. © 2013 ACM.

关键词： Graphics processing unit

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Zero knowledge LTCs and their applications

Zero knowledge LTCs and their applications

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16th international workshop on Approximation algorithms for Combinatorial optimization Problems, APPROX 2013 and the 17th international workshop on Randomization and Computation, RANDOM 2013

作者： Ishai, Yuval Sahai, Amit Viderman, Michael Weiss, Mor Department of Computer Science Technion Haifa Israel Computer Science Department UCLA United States

ISBN: (纸本)9783642403279

Locally testable codes (LTCs) are error-correcting codes for which membership in the code can be tested by probing few symbols of a purported codeword. Motivated by applications in cryptography, we initiate the study of zero knowledge locally testable codes (ZK-LTCs). ZK-LTCs are LTCs which admit a randomized encoding function, such that even a malicious tester which reads a large number of codeword symbols learns essentially nothing about the encoded message. We obtain ZK-LTCs with good parameters by applying general transformations to standard LTCs. We also obtain LTCs and ZK-LTCs which are stable in the sense that they limit the influence of adaptively corrupted symbols on the output of the testing procedure. Finally, we apply stable ZK-LTCs for obtaining protocols for verifiable secret sharing (VSS) in which the communication complexity required for verifying a shared secret is sublinear in the secrecy threshold. We also obtain the first statistically secure VSS protocols and distributed coin-flipping protocols which use n servers, tolerate a constant fraction of corrupted servers, and have error that vanishes almost exponentially with n using only O(n) bits of communication. These improve over previous VSS and coin-flipping protocols from the literature, which require nearly quadratic communication to achieve similar guarantees. © 2013 Springer-Verlag.

关键词： Codes (symbols)

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AOF-Based Algorithm for Dynamic Multi-Objective Distributed Constraint optimization

AOF-Based Algorithm for Dynamic Multi-Objective Distributed ...

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7th Multi-Disciplinary international workshop on Artificial Intelligence (MIWAI)

作者： Okimoto, Tenda Clement, Maxime Inoue, Katsumi Transdisciplinary Res Integrat Ctr Tokyo 1018430 Japan Pierre & Marie Curie Univ Paris 6 F-75005 Paris France Natl Inst Informat Tokyo 1018430 Japan

ISBN: (纸本)9783642449499;9783642449482

Many real world problems involve multiple criteria that should be considered separately and optimized simultaneously. A Multi-Objective Distributed Constraint optimization Problem (MO-DCOP) is the extension of a mono-objective Distributed Constraint optimization Problem (DCOP). A DCOP is a fundamental problem that can formalize various applications related to multi-agent cooperation. This problem consists of a set of agents, each of which needs to decide the value assignment of its variables so that the sum of the resulting rewards is maximized. An MO-DCOP is a DCOP which involves multiple criteria. Most researches have focused on developing algorithms for solving static problems. However, many real world problems are dynamic. In this paper, we focus on a change of criteria/objectives and model a Dynamic MO-DCOP (DMO-DCOP) which is defined by a sequence of static MO-DCOPs. Furthermore, we develop a novel algorithm for DMO-DCOPs. The characteristics of this algorithm are as follows: (i) it is a reused algorithm which finds Pareto optimal solutions for all MO-DCOPs in a sequence using the information of previous solutions, (ii) it utilizes the Aggregate Objective Function (AOF) technique which is the widely used classical method to find Pareto optimal solutions, and (iii) the complexity of this algorithm is determined by the induced width of problem instances.

关键词： Multi agent systems

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Parallelization of algorithms for Linear Discrete optimization Using ParaPhrase

Parallelization of Algorithms for Linear Discrete Optimizati...

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international workshop on Database and Expert Systems applications

作者： Michael Roßbory Werner Reisner Software Competence Center Hagenberg GmbH Austria

In industry optimization of processes, production planing, or resource usage is important to reduce costs and increase profit. Mathematical models for optimization can contribute to achieve this, but they also pose some challenges. Not only expertise in mathematics is needed to apply these optimization models, but furthermore expertise in programming is needed for implementation and integration into the software landscape of the company. Additionally most optimization algorithms are computationally very expensive and finding a solution takes a long time. Parallelization reduces the time and can lead to better results, but makes implementation even more challenging. How the high-level pattern-based approach of ParaPhrase [5] and its provided tools reduces this challenges will be described in this paper using a real-world example from industry.

关键词： optimization Pipelines Linear programming Companies Paper mills

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A (μ, λ) evolutionary and particle swarm hybrid algorithm, with an application to dinosaur gait optimization

A (μ, λ) evolutionary and particle swarm hybrid algorithm,...

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IEEE international workshop on Computational Intelligence & applications (IWCIA)

作者： Yoshiyuki Matsumura Ayumu Kobayashi Kiyotaka Sugiyama Todd Pataky Toshiyuki Yasuda Kazuhiro Ohkura Bill Sellers Shinshu University Ueda Nagano Japan Hiroshima University Higashihiroshima Hiroshima Japan University of Manchester Manchester UK

A hybrid evolutionary algorithm based on (μ, λ) evolutionary algorithms and particle swarm optimization is proposed for the numerical optimization problems. In order to find out the performance of the hybrid, the computer experiment is tested on dinosaur's gait generation problem. Experimental results show that hybrid optimization finds maximum fitness and is faster in the first phase.

关键词： Particle swarm optimization optimization Dinosaurs Muscles Sociology Statistics

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Cyber security problem based on Multi-Objective Distributed Constraint optimization technique

Cyber security problem based on Multi-Objective Distributed ...

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2013 43rd Annual IEEE/IFIP Conference on Dependable Systems and Networks workshop, DSN-W 2013

作者： Okimoto, Tenda Ikegai, Naoto Inoue, Katsumi Okada, Hitoshi Ribeiro, Tony Maruyama, Hiroshi Transdisciplinary Research Integration Center Tokyo Japan National Institute of Informatics Tokyo Japan Graduate University for Advanced Studies Tokyo Japan Institute of Mathematical Statistics Tokyo Japan

ISBN: (纸本)9781479901814

A cyber security problem is an important application domain for systems resilience. The increase of malware, computer viruses, and intensive cyber attacks are serious problems for our information society. In this paper, we introduce a new presentation of a cyber security problem. Our model is based on a Multi-Objective Distributed Constraint optimization Problem (MO-DCOP) which is a fundamental problem that can formalize various applications related to multi-agent cooperation. MO-DCOP is suitable for modeling a cyber security problem, since cyber security problems involve multiple criteria, e.g., risk (security), surveillance (privacy) and cost. Furthermore, MO-DCOP is a decentralized model. In this model, variables and constraints are distributed among agents. Since there exists no single agent which maintains all informations, it is resilient against intensive cyber attacks. Furthermore, we develop a novel algorithm for solving a cyber security problem which utilizes well-known and widely used branch and bound technique and depth-first search strategy and finds all trade-off solutions. We also propose the extension of this algorithm which utilizes a preprocessing technique called soft Arc Consistency. The softAC is a well-known preprocessing technique which transforms a constraint optimization problem into a simplified problem that can be solved efficiently. In the experiments, we examine the run time of our proposed algorithms in cyber security problems and show that our algorithms can solve cyber security problems quickly. © 2013 IEEE.

关键词： Cybersecurity

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Online primal-dual for non-linear optimization with applications to speed scaling

Online primal-dual for non-linear optimization with applicat...

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10th international workshop on Approximation and Online algorithms, WAOA 2012

作者： Gupta, Anupam Krishnaswamy, Ravishankar Pruhs, Kirk Computer Science Department Carnegie Mellon University Pittsburgh PA 15213 United States Computer Science Department Princeton University Princeton NJ 08542 United States Computer Science Department University of Pittsburgh Pittsburgh PA 15260 United States

ISBN: (纸本)9783642380150

We give a principled method to design online algorithms (for potentially non-linear problems) using a mathematical programming formulation of the problem, and also to analyze the competitiveness of the resulting algorithm using the dual program. This method can be viewed as an extension of the online primal-dual method for linear programming problems, to nonlinear programs. We show the application of this method to two online speed-scaling problems: one involving scheduling jobs on a speed scalable processor so as to minimize energy plus an arbitrary sum scheduling objective, and one involving routing virtual circuit connection requests in a network of speed scalable routers so as to minimize the aggregate power or energy used by the routers. This analysis shows that competitive algorithms exist for problems that had resisted analysis using the dominant potential function approach in the speed-scaling literature, and provides alternate cleaner analysis for other known results. This gives us another tool in the design and analysis of primal-dual algorithms for online problems. © Springer-Verlag 2013.

关键词： Scheduling

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Parallel Stochastic Decomposition algorithms for Multi-Agent Systems

Parallel Stochastic Decomposition Algorithms for Multi-Agent...

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IEEE 14th workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Yang, Yang Scutari, Gesualdo Palomar, Daniel P. Hong Kong Univ Sci & Tech Dept Elect & Comp Eng Hong Kong Hong Kong Peoples R China State Univ New York SUNY Dept Elect Engn Buffalo NY 14260 USA

ISBN: (纸本)9781467355773

In a stochastic optimization problem, the objective function is given in the form of the expectation with respect to some random variables. In many applications, this expectation cannot be computed accurately (e.g., because the statistics of the random variables are unknown). The common approach followed in the literature to deal with this issue is using stochastic gradient schemes, which however suffer from slow convergence. In this paper, we propose for the first time a class of provably convergent Jacobi best-response algorithms for general nonconvex stochastic sum-utility optimization problems, which arise naturally in the design of wireless multi-user interfering systems. The proposed novel decomposition enables all users to update their optimization variables in parallel by solving a sequence of strongly convex subproblems, one for each user. Finally, we customize our algorithms to solve the stochastic sum rate maximization problem over MIMO interference channels and multiple access channels. Numerical results show that our algorithms are much faster than state-of-the-art stochastic gradient schemes.

关键词： Multi-agent systems parallel optimization stochastic approximation

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Structural optimization of neural network for data prediction using dimensional compression and tabu search

Structural optimization of neural network for data predictio...

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IEEE international workshop on Computational Intelligence & applications (IWCIA)

作者： Tomohiro Hayashida Ichiro Nishizaki Tsubasa Matsumoto Hiroshima University Hiroshima Japan

In the traditional procedures, data classification with a high degree of accuracy by neural networks requires heuristic structural optimization by using expert knowledge. However, the optimization procedure takes an immense amount of time and effort. Additionally, high-dimensional data is difficult to classify for many analysts, thus, it would appears that accuracy of data classification grows higher by proper selection and dimensional compression of input data. This study suggests new procedure for data classification by using neural networks. For dimensional compression of input data, the suggested procedure uses sandglass type neural networks, and tabu search algorithms are applied for input data selection and structural optimization of union between a sandglass type and a feedforward neural networks.

关键词： Conferences Computational intelligence

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