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检索条件"任意字段=Conference on High Performance Algorithms and Software for Nonlinear Optimization"

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A Modified PSO Algorithm with Line Search

A Modified PSO Algorithm with Line Search

引用

International conference on Computational Intelligence and software Engineering (CiSE)

作者： Guanghui Zeng Yuewen Jiang Department of Electronic and Information Engineering Guangzhou City Construction College Conghua China School of Physics and Optical Information Sciences Jiaying University Meizhou China

Recently Particle Swarm optimization (PSO) algorithm gained popularity and employed in many engineering applications because of its simplicity and efficiency. The performance of the PSO algorithm can further be improved by using hybrid techniques. There are various hybrid PSO algorithms published in the literature where researchers combine the benefits of PSO with other heuristics algorithms. In this paper, we propose a cooperative line search particle swarm optimization (CLS-PSO) algorithm by integrating local line search technique and basic PSO (B-PSO). The performance of the proposed hybrid algorithm, examined through four typically nonlinear optimization problems, is reported. Our experimental results show that CLS-PSO outperforms basic PSO.

关键词： Algorithm design and analysis Particle swarm optimization Optimized production technology Heuristic algorithms Convergence

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Lock Elision for Read-Only Critical Sections in Java 10

Lock Elision for Read-Only Critical Sections in Java

引用

ACM SIGPLAN conference on Programming Language Design and Implementation

作者： Nakaike, Takuya Michael, Maged M. IBM Res Tokyo Tokyo Japan

ISBN: (纸本)9781450300193

It is not uncommon in parallel workloads to encounter shared data structures with read-mostly access patterns, where operations that update data are infrequent and most operations are read-only. Typically, data consistency is guaranteed using mutual exclusion or read-write locks. The cost of atomic update of lock variables result in high overheads and high cache coherence traffic under active sharing, thus slowing down single thread performance and limiting scalability. In this paper, we present SOLERO (software Optimistic Lock Elision for Read-Only critical sections), a new lock implementation called for optimizing read-only critical sections in Java based on sequential locks. SOLERO is compatible with the conventional lock implementation of Java. However, unlike the conventional implementation, only critical sections that may write data or have side effects need to update lock variables, while read-only critical sections need only read lock variables without writing them. Each writing critical section changes the lock value to a new value. Hence, a read-only critical section is guaranteed to be consistent if the lock is free and its value does not change from the beginning to the end of the read-only critical section. Using Java workloads including SPECjbb2005 and the HashMap and TreeMap Java classes, we evaluate the performance impact of applying SOLERO to read-mostly locks. Our experimental results show performance improvements across the board, often substantial, in both single thread speed and scalability over the conventional lock implementation (mutual exclusion) and read-write locks. SOLERO improves the performance of SPECjbb2005 by 3-5% on single and multiple threads. The results using the HashMap and TreeMap benchmarks show that SOLERO outperforms the conventional lock implementation and read-write locks by substantial multiples on multi-threads.

关键词： algorithms Languages performance Java Just-In-Time compiler synchronization monitor lock optimization lock elision

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Intelligent task mapping using machine learning

Intelligent task mapping using machine learning

引用

International conference on Computational Intelligence and software Engineering

作者： Tetzlaff, Dirk Glesner, Sabine Group Software Engineering for Embedded Systems Berlin Institute of Technology Germany

ISBN: (纸本)9781424453924

Task scheduling and task allocation, which are vital parts of mapping parallel programs to concurrent architectures, must take into account the interprocessor communication, whose overheads have emerged as the major performance limitation in parallel applications. Furthermore, its power consumption is an important research focus which must be addressed. Finding an optimal solution requires information about the runtime behavior, which is not known at compile time. Moreover, the computational complexity leads to heuristic approaches based on conservative assumptions that are unable to exploit all of the program's optimization potential. In this paper, we propose a novel approach to automatically generate architecture- and application-specific heuristics for power- and communication-aware task mapping using machine learning techniques to predict how programs behave at runtime. The key advantage of machine learning techniques is their ability to find relevant information in a high-dimensional space. This yields more precise heuristics than those based on pure static assumptions, as our experimental results show. Because learning is done in an off-line training phase once per architecture, the compile time itself is not extended as in other heuristic approaches like genetic or evolutionary algorithms. ©2010 IEEE.

关键词： Heuristic methods

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An Estimation of Distribution Algorithm Based Portfolio Selection Approach

An Estimation of Distribution Algorithm Based Portfolio Sele...

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15th Annual International conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Xu, Rui-Tian Zhang, Jun Liu, Ou Huang, Rui-Zhang Sun Yat Sen Univ Dept CS Key Lab Digital Life Minist EducKey Lab Software TechnolEduc Dept Gu Guangzhou Guangdong Peoples R China Hong Kong Polytech Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9780769542539

A portfolio selection problem is about finding an optimal scheme to allocate a fixed amount of capital to a set of available assets. The optimal scheme is very helpful for investors in making decisions. However, finding the optimal scheme is difficult and time-consuming especially when the number of assets is large and some actual investment constraints are considered. This paper proposes a new approach based on estimation of distribution algorithms (EDAs) for solving a cardinality constrained portfolio selection (CCPS) problem. The proposed algorithm, termed PBILCCPS, hybridizes an EDA called population-based incremental learning (PBIL) algorithm and a continuous PBIL (PBILc) algorithm, to optimize the selection of assets and the allocation of capital respectively. The proposed algorithm adopts an adaptive parameter control strategy and an elitist strategy. The performance of the proposed algorithm is compared with a genetic algorithm and a particle swarm optimization algorithm. The results demonstrate that the proposed algorithm can achieve a satisfactory result for portfolio selection and perform well in searching nondominated portfolios with high expected returns.

关键词： estimation of distribution algorithm population-based incremental learning algorithm portfolio selection problem

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Efficient adaptive noise cancellation using genetic optimization

Efficient adaptive noise cancellation using genetic optimiza...

引用

International conference on Signal Processing Proceedings (ICSP)

作者： Shiva Gholami-Boroujeny Mohammad Eshghi Electrical and Computer Engineering Faculty Shahid Beheshti University Tehran Iran

In this paper, the use of the genetic algorithm in optimization of a nonlinear filter in adaptive noise cancellation (ANC) system is proposed. While the standard adaptive algorithms in nonlinear systems may converge to a local minimum, genetic algorithms (GAs) handle this problem efficiently. Computer simulations show that a superior performance is achieved using the proposed system with a not complex GA. A comparison of the proposed system with a popular ANC system also shows a high reduction of estimation error's power.

关键词： Noise cancellation Genetics Adaptive systems Noise measurement Distortion measurement nonlinear filters

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Detecting Roots of nonlinear Equations through a Novel Differential Evolution Algorithm

Detecting Roots of Nonlinear Equations through a Novel Diffe...

引用

International conference on Information Engineering and Computer Science (ICIECS)

作者： Qiang Yin Yibo Qi Jiaqing Xiao Department of Mathematics School of Science Wuhan University of Technology Wuhan China

Though many numerical methods have been put for nonlinear equations, their convergence and performance are highly sensitive to the initial guesses of the solution pre-supplied. However, the selection of good initial guess is often of hard work. Aiming at this, a novel approach is proposed to resolve nonlinear equations. It takes genetic algorithms' new achievement differential evolution algorithms as the main technique. With a function deflection technique and a novel space contraction method to re-initialize, it resolve nonlinear equations by transform them into correspondent optimization problems. Convergence reliability, computational cost and applicability of different algorithms were compared by testing several classical nonlinear equations and a benchmark mechanics problem. The numerical experiments done show that the put approach has reliable convergence probability, high convergence rate and solution precision. And DE is a successful approach in solving equations both in theory and application.

关键词： Mathematical model Algebra nonlinear equations optimization Convergence Polynomials

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Multi-Parent Crossover Algorithm with Discrete Recombination

Multi-Parent Crossover Algorithm with Discrete Recombination

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International conference on Computational Intelligence and software Engineering (CiSE)

作者： Dazhi Jiang Yulin Du Jiali Lin Zhijian Wu Department of Computer Shantou University Shantou China Business School Shantou University Shantou China State Key Laboratory of Software Engineering Wuhan University of China Wuhan China

As a novel and promising algorithm, differential evolution (DE) has shown good performance in lots of optimization problems. It has been said that DE is one of the most competitive EAs for continuous optimization. As a kind of EAs, GT algorithm is a novel algorithm which based on multi-parent crossover. Compared with GT algorithm, DE performances better to find the global minima obviously. This paper presents a concept of pattern analyses to analyze the reason of the DE's high performance. Then a new algorithm based on GT's multi-parent crossover and traditional DE's discrete recombination is presented for enhancing the performance of the obsolete and inefficient GT algorithm. According to the pattern analyses, the new algorithm obtains several patterns similar to DE. The experiments show the efficiency of the proposed new algorithm.

关键词： Algorithm design and analysis optimization Pattern analysis Evolutionary computation Analytical models Vectors software algorithms

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Improved Particle Swarm optimization Algorithm

Improved Particle Swarm Optimization Algorithm

引用

International conference on Computational Intelligence and software Engineering (CiSE)

作者： Ye Gao Shan Li College of Computer Science and Technology Xi''an University of Science and Technology Xi'an China

Particle Swarm optimization (PSO) is a new random computational method for tackling optimization functions. However, it is easily trapped into the local optimum when solving the complexity and high-dimensional problems, which makes the performance of PSO greatly reduced. To overcome this shortcoming, the paper proposes an Improved Particle Swarm optimization (IPSO), by adding the third particle of having a more room for progress to guide the current particles' velocity updating rule, Which can keep the diversity of the particles and reduce the probability of trapping into the local optimization .Besides, the program enhances and improves the stability and the convergence speed of the algorithm according to adjusting the particles which go beyond the default position space in each interiors. Five benchmark functions are tested, and the results indicate the effectiveness of the new program.

关键词： Particle swarm optimization optimization Heuristic algorithms Convergence Charge carrier processes Stability criteria

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Null control by position perturbation of selected elements for phased antenna array using hybrid EPSO/DE optimization

Null control by position perturbation of selected elements f...

引用

International conference on Communication Systems

作者： M. T. Alsharaa M. A. Mangoud H. M. Elragal Department of Electrical and Electronics Engineering University of Bahrain Isa Town Bahrain

This paper describes the null steering method by position perturbation of selected elements with minimum sidelobe level using a proposed hybrid Enhanced Particle Swarm optimization (EPSO) / Differential Evolution (DE) algorithm. EPSO and DE algorithms are proved to be high-performance evolutionary algorithms capable of solving nonlinear optimization problems. Therefore, in this paper the two algorithms are combined and a robust EPSO/DE hybrid method is developed. This newly developed optimization algorithm is then applied to find the optimum number and the position of selected perturbed elements in the array to control the null towards the interference direction. The position perturbations of antenna elements are performed over the axial direction, elevation direction or combination of both. The hybridized algorithm has shown to give best result compared to each individual algorithm when applied to optimize different configurations.

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A high diversity hybrid ensemble of classifiers

A high diversity hybrid ensemble of classifiers

引用

International conference on software Engineering and Data Mining (SEDM)

作者： Sahand Khakabimamaghani Farnaz Barzinpour Mohammad Reza Gholamian Industrial Engineering Department Iran University of Science and Technology Tehran Iran Iran

ISBN: (纸本)9781424473243;9788988678220;9788988678213

Ensemble has been proved a successful approach for enhancing the performance of single classifiers. But there are two key factors influencing the performance of an ensemble directly: accuracy of each single member and diversity between the members. There have been many approaches used in the literature to create the mentioned diversity. In this paper we add a novel approach, in which classifier type variance is utilized along with feature subset diversification to create a high diversity ensemble of different classifiers and an optimization is conducted on the initial population using a multi-objective evolutionary algorithm. The results of experiment over some standard data sets exhibit the outperformance of the suggested approach in comparison to existing ones in specific situations.

关键词： Diversity reception Neural networks Genetic algorithms Artificial neural networks Industrial engineering Decision trees Taxonomy optimization methods Genetic programming Evolutionary computation

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