检索结果-内蒙古大学图书馆

A Dynamic Multistage Hybrid Swarm Intelligence optimization Algorithm for Function optimization

DISCRETE DYNAMICS IN NATURE AND SOCIETY 2012年第unknown期2012卷 578064-1-578064-22页

作者： Wu, Daqing Zheng, Jianguo DongHua Univ Glorious Sun Sch Business & Management Shanghai 200051 Peoples R China Univ S China Comp Sci & Technol Inst Hengyang 421001 Hunan Peoples R China Sichuan Univ Sci & Engn Artificial Intelligence Key Lab Sichuan Prov Zigong 643000 Peoples R China

A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO) and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC) for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.

关键词： global convergence MULTISTAGE Benchmark testing Particle swarm optimization dynamic optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Onboard Learning-Based Fuel Consumption optimization in Series Hybrid Electric Vehicles

Onboard Learning-Based Fuel Consumption Optimization in Seri...

引用

American Control Conference

作者： Rohit Gupta Ilya V. Kolmanovsky Yan Wang Dimitar P. Filev Department of Mechanical Engineering University of Michigan Ann Arbor MI 48109-2125 Department of Aerospace Engineering University of Michigan Ann Arbor MI 48109-2140 Ford Motor Company Dearborn MI

ISBN: (纸本)9781457710957

This paper considers an on-line approach to reducing engine fuel consumption in a series hybrid electric vehicle operated under power smoothing strategy, in which the generator power is slowly varying. An optimization algorithm adjusts the engine speed set-point (and other engine actuator settings as appropriate) on-line, as the vehicle is driven. The actual generator power is estimated, and the battery power output is adjusted to ensure that the wheel power and the drivability are unaffected. With this approach, operating points on the optimal operating line (OOL) can be learned during vehicle driving thus compensating for the effects of the engine aging and variability while reducing upfront calibration time and effort. In the paper, the control approach is defined and an optimization algorithm is examined. Simulation results are reported.

关键词： series hybrid power optimization algorithms vehicle Driveability Hybrid electric vehicles Fuel consumption operating line dynamos Generator

来源：评论

学校读者我要写书评

暂无评论

Randomized Smoothing for (Parallel) Stochastic optimization

Randomized Smoothing for (Parallel) Stochastic Optimization

引用

IEEE Annual Conference on Decision and Control

作者： John C. Duchi Peter L. Bartlett Martin J. Wainwright Department of Electrical Engineering and Computer Science

ISBN: (纸本)9781467320658

By combining randomized smoothing techniques with accelerated gradient methods, we obtain convergence rates for stochastic optimization procedures, both in expectation and with high probability, that have optimal dependence on the variance of the gradient estimates. To the best of our knowledge, these are the first variance-based rates for non-smooth optimization. A combination of our techniques with recent work on decentralized optimization yields order-optimal parallel stochastic optimization algorithms. We give applications of our results to several statistical machine learning problems, providing experimental results (in the full version of the paper) demonstrating the effectiveness of our algorithms.

关键词： gradient methods optimization Procedure Stochastic smoothing optimization algorithms RANDOMISING Parallel Lines Variance

来源：评论

学校读者我要写书评

暂无评论

Distributed Delayed Stochastic optimization

Distributed Delayed Stochastic Optimization

引用

IEEE Annual Conference on Decision and Control

作者： Alekh Agarwal John C. Duchi Microsoft Research Department of EECS University of California Berkeley

ISBN: (纸本)9781467320658

We analyze the convergence of gradient-based optimization algorithms that base their updates on delayed stochastic gradient information. The main application of our results is to gradient-based distributed optimization algorithms where a master node performs parameter updates while worker nodes compute stochastic gradients based on local information in parallel, which may give rise to delays due to asynchrony. We take motivation from statistical problems where the size of the data is so large that it cannot fit on one computer;with the advent of huge datasets in biology, astronomy, and the internet, such problems are now common. Our main contribution is to show that for smooth stochastic problems, the delays are asymptotically negligible and we can achieve order-optimal convergence results. We show n-node architectures whose optimization error in stochastic problems - in spite of asynchronous delays - scales asymptotically as O(1/{the square root of}nT) after T iterations. This rate is known to be optimal for a distributed system with n nodes even in the absence of delays. We additionally complement our theoretical results with numerical experiments on a logistic regression task.

关键词： optimization algorithms Stochastic delay local information. dimensional data Distributed Systems asymptotic Converge Distribute

来源：评论

学校读者我要写书评

暂无评论

Motion Curve optimization Algorithm Using Genetic Operations and Its Application to Bottling Machine

Motion Curve Optimization Algorithm Using Genetic Operations...

引用

IEEE Annual Conference on Decision and Control

作者： Ken'ichi Kanazawa Ken'ichi Yano Tatsuhiro Nakada Department of Mechanical Engineering Mie University Shibuya Kogyo Co. Ltd.

ISBN: (纸本)9781467320658

We propose a motion curve optimization algorithm based on a genetic algorithm. A motion curve is a function of displacement of a drive part of a machine with respect to time. In a curve optimization using a standard genetic algorithm, an optimized curve is searched by repeatedly applying genetic operations to design variables that define the shape of motion curves. In contrast, the algorithm we proposed can effectively optimize a curve by directly applying genetic operations to curves without design variables. Eventually, the algorithm is applied to an optimization of a motion curve to decrease vibration at the surface of the liquid in a bottle using an actual bottling machine.

关键词： Curves design variable optimization algorithms Surgery Bottles machine Vibration

来源：评论

学校读者我要写书评

暂无评论

Coordinated optimization of Underlay Network Communication for Efficient Use of Spectrum

Coordinated Optimization of Underlay Network Communication f...

引用

IEEE Global Communications Conference

作者： Amber L. Silva Joshua D. Niedzwiecki Alexander M. Wyglinski Brandon Hombs BAE Systems Technology Solutions Electrical and Computer Engineering Worcester Polytechnic Institute

ISBN: (纸本)9781467309202

Underlay communications has the potential to become a valuable tool for increasing the capacity of spectrum provided that the quality of communications is upheld. In this paper, we propose a method for underlay nodes to detect, and coordinate with, other underlay nodes to form a network on preoccupied spectrum. Specifically, we introduce a novel optimization algorithm, called the Coordinated optimization of EXposure Introduced by a Secondary Transmitter (COEXIST), which selects the best communications channel for all nodes within a network while minimizing interference with the primary user. This algorithm does not require knowledge of spatial location of the primary users or underlay nodes. With the proposed solution, underlay nodes can detect other nodes at power levels far below the power level of the primary user and, once a network is formed, nodes can continue to communicate at very low power levels without causing interference to primary users transmitting at the same time.

关键词： power level Ganglia Spectra optimization algorithms Network communication Communication Research Coordinates

来源：评论

学校读者我要写书评

暂无评论

optimization in Multi-Modal Continuous Space with Little Globally Convex using Differential Evolution on Scattered Parents

Optimization in Multi-Modal Continuous Space with Little Glo...

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： Ryo Iwai Shohei Kato Dept. of Computer Science and Engineering Graduate School of Engineering Nagoya Institute of Technology NIT

ISBN: (纸本)9781467317139

Differential Evolution (DE) is a powerful stochastic algorithm for real-coded optimization. However, DE has a problem as well as other traditional stochastic optimization algorithms: difficult to optimize search spaces that are little globally convex. Thus, it is difficult for DE and traditional algorithms to optimize some practical problems where globally convex cannot be assumed. As one of the solution for this problem, we propose Differential Evolution on Scattered Parents (DE-SP) that re-selects the individuals on each dimension when the mutant individual is calculated and some children individuals' candidates unconditionally become the children individuals. We have implemented 2 types of optimization experiment to verify the performance of DE-SP: Noisy Function 1 (NF1), that is a benchmark problem with little globally convex, Noisy Function 2 (NF2), that is the one with globally convex. And we compared the performance of DE-SP with those of 15 types of algorithms. Thereby, it is confirmed that DE-SP was the most stable algorithm to optimize little globally convex spaces among 15 comparative algorithms from the experiment of optimizing NF1. And it is comfirmed that DE-SP can optimize globally convex spaces as well as DE from the experiment of optimizing NF2.

关键词： optimization Globally convex Differential Evolution Evolutionary computing Convexity Benchmark testing MT Scattering Little's Disease Neurofibromin 2 optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Direct Search and Stochastic optimization Applied to Two Nonconvex Nonsmooth Problems 12

Direct Search and Stochastic Optimization Applied to Two Non...

引用

Simulation Multiconference

作者： David R. Easterling Layne T. Watson Michael L. Madigan Brent S. Castle Michael W. Trosset Departments of Computer Science Virginia Polytechnic Institute and State University Engineering Science & Mechanics Virginia Polytechnic Institute and State University Department of Statistics Indiana University

The optimization of two problems with high dimensionality and many local minima are investigated under two different optimization algorithms: DIRECT and QNSTOP, a new algorithm developed at Indiana University.

ISBN: (纸本)9781618397881

关键词： Stochastic optimization Deterministic optimization Biomechanics Quadratic optimization DIRECT QNSTOP Biomechanics direct search Mentoring optimization algorithms Stochastic Indiana

来源：评论

学校读者我要写书评

暂无评论

Genetic Vector Ordinal optimization Algorithm Based on RSM for UCAV Attack Planning

Genetic Vector Ordinal Optimization Algorithm Based on RSM f...

引用

International Conference on Computer Science and Network Technology

作者： Xueqiang Gu Jing Chen Jie Li Haifeng Liu College of Mechatronics Engineering and Automation National University of Defense Technology The Second Artillery Equipment Academy

ISBN: (纸本)9781467329637

The problem of generating optimal air-to-ground attack plan for the UCAV is studied. In order to deal with the difficulties about time-consuming evaluation of the objective cost values and the large solution space, a genetic vector ordinal optimization algorithm (GVOOA) based on response surface methodology (RSM) is proposed. In this paper, several constraints including flight capability constraint, battlefield constraints and weapon constraint, are considered. And two optimal cost functions are built using Monte Carlo simulation criteria of the weapon attack operation, and then the attack planning problem is transformed into a simulation multi-objective optimization problem (SMOOP). In order to improve the convergence performance, an approximate model building approach based on RSM is presented, and a combining vector ordinal optimization (VOO) with NSGA-II is designed. The proposed approach is demonstrated using a typical air-to-ground attack mission scenario. The simulation results indicate that the proposed approach is better in the solving speed compared to the traditional approach, under the near optimal performance.

关键词： Attack planning Simulation multi-objective optimization Genetic vector ordinal optimization algorithm Response surface methodology response surface methodology unmanned combat aerial vehicles drone honey bees Ordinal optimum performance Simulation results SURFACE AIR Cost functions optimization algorithms Solution space weapons

来源：评论

学校读者我要写书评

暂无评论

Energetic optimization of the Driving Speed based on Geographic Information System Data

Energetic Optimization of the Driving Speed based on Geograp...

引用

IEEE Vehicular Technology Conference

作者： Sousso Kelouwani Kodjo Agbossou Yves Dube Loic Boulon Hydrogen Research Institute and the Department of Mechanical Engineering of Universite du Quebec a Trois-Rivieres Hydrogen Research Institute and the Electrical and Computer Engineering of Universite du Quebec a Trois-Rivieres

ISBN: (纸本)9781467318808

This work is based on the road-trip knowledge in order to reduce the energy consumption of a vehicle. The proposed algorithm computes the globally optimal speed profile and the associated energy consumption profile from the road-trip information. This profile is the lowest boundary of the energy required to move the vehicle. The energetic plan obtained is independent of the powertrain architecture, it can be used with any vehicle (conventional internal-combustion engine vehicle, hybrid electric vehicle and fuel cell vehicle). This optimization algorithm runs offline and (in future works) the results will be integrated into a real-time energy management system. Algorithm results are compared to experimental data obtained with a low-speed vehicle. For the same trip duration, the presented preliminary results show that the energy consumption is lower with the optimal driving cycle.

关键词： energy consumption vehicle fuel cell vehicles Operating speed OFF LINE optimization algorithms Vehicle speed Travel information geographic information systems Experimental data optimal velocity

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：