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检索条件"任意字段=International Workshop on Multilevel Optimization - Algorithms and Applications"

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New Differential Evolution Algorithm with a Second Enhanced Mutation Operator

New Differential Evolution Algorithm with a Second Enhanced ...

引用

international workshop on Intelligent Systems and applications, ISA

作者： Changshou Deng Bingyan Zhao Anyuan Deng Rixin Hu School of Information Science and Technology Jiujiang University China

To improve the efficiency of the original differential evolution algorithm, a new differential evolution algorithm was proposed. A new framework with a single population was used to improve its' exploration ability. And a second enhanced mutation operator was used to ensure the exploitation of previous knowledge about the fitness landscape. Numerical experiments with typical benchmark functions show the proposed new version of differential evolution performs better than original differential evolution algorithm. Performances compared with dynamic differential evolution and particle swarm optimization algorithm show its superiority.

关键词： Genetic mutations Signal processing algorithms Convergence Particle swarm optimization Machine intelligence Fuzzy logic Information science Algorithm design and analysis Power engineering computing Competitive intelligence

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A Scalability Test for Accelerated DE Using Generalized Opposition-Based Learning

A Scalability Test for Accelerated DE Using Generalized Oppo...

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international Conference on Intelligent Systems Design and applications (ISDA)

作者： Hui Wang Zhijian Wu Shahryar Rahnamayan Lishan Kang State Key Laboratory of Software Engineering Wuhan University of China Wuhan China Faculty of Engineering and Applied Science Institute of Technology University of Ontario Oshawa ONT Canada

In this paper a scalability test over eleven scalable benchmark functions, provided by the current workshop (Evolutionary algorithms and other Metaheuristics for Continuous optimization Problems-A Scalability Test), are conducted for accelerated DE using generalized opposition-based learning (GODE). The average error of the best individual in the population has been reported for dimensions 50, 100, 200, and 500 in order to compare with the results of other algorithms which are participating in this workshop. Current work is based on opposition-based differential evolution (ODE) and our previous work, accelerated PSO by generalized OBL.

关键词： Scalability Life estimation System testing Acceleration Benchmark testing Evolutionary computation Robustness Chromium Intelligent systems Design optimization

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Introducing an FPGA based genetic algorithms in the applications of blind signals separation

Introducing an FPGA based genetic algorithms in the applicat...

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IEEE international workshop on System-on-Chip for Real-Time applications

作者： H. Emam M.A. Ashour H. Fekry A.M. Wahdan Atomic energy Auth. NCRRT Cairo Egypt Faculty of engineering Ain Shams University Cairo Egypt

ISBN: (纸本)076951944X

Genetic algorithms (GAs) are one of the most advanced optimization techniques. The main objective of this paper, is introducing an FPGA implementation based genetic algorithm, then applying it, as an adaptive algorithm, on a nonlinear adaptive filters for the purpose of blind signals separation. In this case, the nonlinear estimator has been used to predict the error filter and GA will be used to optimize the filter coefficients through the search for a near optimum solution. The proposed Hardware Genetic algorithms (HGA) has been presented and tested, first, by different sine wave signals, then by audio wave signals to judge the design separation capability. The implementation results declare that HGA approach significantly enhances the system performance as a step toward real time performance.

关键词： Field programmable gate arrays Genetic algorithms Blind source separation Adaptive algorithm Adaptive filters Hardware Testing Signal design Algorithm design and analysis System performance

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Comparison of various encoding schemes for tree in Biobjective optimization scenario

Comparison of various encoding schemes for tree in Biobjecti...

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international workshop on Soft Computing applications (SOFA)

作者： Amit Kumar Singh Sanger Pramod Kumar Singh ABV Indian Institute of Information Technology and Management Gwalior India

To find MST (Minimum Spanning Trees) in complete graph is a classical problem in operation research having network design as an important application. It is possible to solve MST problem efficiently, but its Biobjective versions are NP hard. In this paper, we present a comparison of two encoding schemes for representing tree in Biobjective optimization scenario. The three different instances of Biobjective MST have been solved by using two different encoding methods in the evolutionary algorithms; Pareto optimal front obtained has been taken into account for comparative study. Our evolutionary computation approach involves Biobjective MST problem using NSGAII (Nondominated Sorting Genetic Algorithm II) approach. We compare edge sets encoding with Prüfer encoding in evolutionary algorithm for Biobjective MST, we find edge sets encoding outperforms the Prüfer in Biobjective optimization scenario.

关键词： Encoding Algorithm design and analysis optimization Evolutionary computation Sorting Pediatrics Memetics

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A Hybrid Particle Swarm optimization Algorithm for Multimodal Function optimization

A Hybrid Particle Swarm Optimization Algorithm for Multimoda...

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international workshop on Intelligent Systems and applications, ISA

作者： Jirong Gu Lin Lin Hui Wang College of Geography and Resources Sciences Sichuan Normal University China Department of Computer Science Yili Normal School Yining China State Key Laboratory of Software Engineering Wuhan University of China China

Particle swarm optimization (PSO) has shown its good performance on numerical function problems. However, on some multimodal functions the PSO easily suffers from premature convergence because of the rapid decline in velocity. In this paper, a hybrid PSO algorithm, called HPSO, is proposed, which employs a modified velocity model to guarantee a non-zero velocity. In addition, a Cauchy mutation operator conducted on the global best particle is used for improving the global search ability of PSO. Experimental studies on a suite of multimodal functions with many local minima show that the HPSO outperforms the standard PSO, CEP, Gaussian swarm with Gaussian mutation (GPSO+GJ) and Gaussian swarm with Cauchy mutation (GPSO+CJ) on most test functions.

关键词： Particle swarm optimization Genetic mutations Evolutionary computation Equations Software algorithms Educational institutions Geography Computer science Software engineering Convergence

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Full-Wave optimization of Nanoparticle Arrays for Beam-Steering applications

Full-Wave Optimization of Nanoparticle Arrays for Beam-Steer...

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international Electromagnetic Compatibility Conference (EMC Turkiye)

作者： Aşkın Altınoklu Özgür Ergül Middle East Technical University Ankara Turkey

We present design and optimization of nanoarrays, which consist of metallic nanoparticles that possess plasmonic properties. Optimal two-dimensional arrangements of nanoparticles are found by using an optimization environment involving genetic algorithms and a three-dimensional full-wave solver based on the multilevel fast multipole algorithm. The nanoarrays are designed to provide maximum radiation at desired directions when they are excited via isotopic sources. The designed structures and their radiation characteristics are extensively investigated by considering various parameters, such as grid size, nanoparticle shape, distance between nanoparticles, and material. The results demonstrate the favorable radiation characteristics of the designs, as well as the capabilities of the optimization environment to design compact nanoarrays for beam-steering applications.

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Two-level partitioning of image processing algorithms for the parallel Map-oriented Machine 96

Two-level partitioning of image processing algorithms for th...

引用

international workshop on Hardware/Software Codesign (CODES)

作者： R.W. Hartenstein J. Becker R. Kress University of Kaiserslautern Kaiserslautern Germany

ISBN: (纸本)9780818672439

The partitioning of image processing algorithms with a novel hardware/software co-design framework (CoDe-X) is presented in this paper, where a new Xputer-architecture (parallel Map-oriented Machine) is used as universal accelerator based on a reconfigurable datapath hardware for speeding-up image processing applications. CoDe-X accepts C-programs and carries out both, the profiling-driven host/accelerator partitioning for performance optimization, and the resource-driven sequential/structural partitioning of the accelerator source code to optimize the utilization of its reconfigurable datapath resources.

关键词： Image processing Partitioning algorithms Hardware Layout Design optimization Optimizing compilers Computer architecture Software performance Research and development Application specific processors

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Bio-inspired heuristic for optimizing protein structure alignment using distributed modified extremal optimization

Bio-inspired heuristic for optimizing protein structure alig...

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IEEE international workshop on Computational Intelligence & applications (IWCIA)

作者： Keiichi Tamura Hajime Kitakami Yoshifumi Takahashi Graduate School of Information Sciences Hiroshima City University Hiroshima Japan

Proteins with similar three-dimensional structures usually have similar biological functions and interactions in living organisms. Thus, detecting similar structures in proteins is presently one of the most attractive research topics in computational biology. Many techniques for the comparison of protein structures are based on protein structure alignment. This is similar to sequence alignment, and is one of the most effective methods for extracting similar structures. The contact map overlap (CMO) maximization problem (hereafter, the CMO problem) is formulated as a combinatorial optimization to find the optimal structure alignment. The CMO problem is known to be NP-hard; hence, a number of researchers have been developing heuristic approaches. In this paper, we propose a novel bio-inspired heuristic algorithm using distributed modified extremal optimization (DMEO) for the CMO problem. DMEO is a hybrid of population-based modified extremal optimization (PMEO) and distributed genetic algorithms inspired by the island model. In the island model, a population is divided into two or more sub-populations (called islands), and each island evolves separately. Islands can maintain different types of individuals at the end of alternation of generations. Therefore, DMEO can maintain more diversity than PMEO. The experiments compared the DMEO-based heuristic with the PMEO-based heuristic, and the results show that DMEO performs well for 25 out of 32 protein pairs.

关键词： Proteins optimization Sociology Statistics Heuristic algorithms Biological system modeling Linear programming

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Budget constrained minimum cost connected medians 26th

Budget constrained minimum cost connected medians

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26th international workshop on Graph-Theoretic Concepts in Computer Science, WG 2000

作者： Konjevod, Goran Krumke, Sven O. Marathe, Madhav Department of Mathematical Sciences Carnegie Mellon University Los Alamos National Laboratory PittsburghPA15213-3890 United States Konrad-Zuse-Zentrum für Informationstechnik Berlin Department Optimization Takustr 7 Berlin-Dahlem14195 Germany Los Alamos National Laboratory P O Box 1663 MS B265 Los AlamosNM87545 United States

ISBN: (纸本)3540411836

Several practical instances of network design problems re- quire the network to satisfy multiple constraints. In this paper, we ad- dress the Budget Constrained Connected Median Problem: We are given an undirected graph G = (V;E) with two difierent edge-weight functions c (modeling the construction or communication cost) and d (modeling the service distance), and a bound B on the total service distance. The goal is to find a subtree T of G with minimum c-cost c(T) subject to the constraint that the sum of the service distances of all the remaining nodes v 2 V n T to their closest neighbor in T does not exceed the speci- fied budget B. This problem has applications in optical network design and the eficient maintenance of distributed databases. We formulate this problem as bicriteria network design problem, and present bicriteria approximation algorithms. We also prove lower bo- unds on the approximability of the problem that demonstrate that our performance ratios are close to best possible. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Approximation algorithms

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Computational optimization of a vehicle dynamic simulation model

Computational optimization of a vehicle dynamic simulation m...

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international workshop/Symposium on Distributed Simulation and Real-Time applications (DS-RT)

作者： Ruslan Shaiakhmetov Department of Computer Science and Engineering Alma Mater Studiorum—Università di Bologna Cesena Italy

ISBN: (数字)9798331527211

ISBN: (纸本)9798331527228

The importance of simulation in motor-sport racing has steadily increased. Fine-tuning these simulators is complex and traditionally manual, relying on engineers’ *** study develops a virtual driver and automates a parameter tuning pipeline for a race car simulator. The virtual driver based on the Data-enabled Predictive Control (DeePC) algorithm outperforms traditional Proportional Integral Derivative (PID) and Model Predictive Control (MPC) controllers and can potentially be extended to compute an optimal trajectory.A YAML-configured optimization pipeline with a custom Domain-Specific Language (DSL) was developed to accelerate parameter tuning. However, parameter interdependency is problematic for traditional optimization algorithms. Bayesian optimization can potentially find a global minimum despite interdependencies and converge to the optimum faster than traditional optimization methods.

关键词： Heuristic algorithms Pipelines Prediction algorithms Trajectory Bayes methods Automobiles optimization Tuning Vehicles Predictive control

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