检索结果-内蒙古大学图书馆

12th International Conference on Hybrid Intelligent Systems (HIS)

作者： Nandy, Sudarshan Karmakar, Manoj Sarkar, Partha Pratim Das, Achintya Abraham, Ajith Paul, Diptarup Univ Kalyani Dept Engn & Technol Studies Nadia W Bengal India Machine Intelligence Res Labs Auburn WA 98071 USA VSB Tech Univ Ostrava Ctr Excellence IT Innovat * Czech Republic JS Coll Engn Dept Comp Sci & Engn Kalyani W Bengal India Kalyani Govt Engn Coll Dept Elect & Telecommun Engn Kalyani W Bengal India Tech Mahindra Ltd Pune 411057 Maharashtra India

ISBN: (纸本)9781467351157;9781467351164

Nature Inspired meta-heuristic algorithms are one of the most efficient solution to many engineering optimization problems. The firefly algorithm is one of the nature inspired solution. The objective of the proposed work is of two folds. In the first fold the firefly algorithm is applied to the back-propagation training phase to optimize the overall training process. One of the problem in this type of implementation is the adjustment of algorithmic parameters and number of firefly population, and for a dynamic system the manual modification of parameter is a troublesome matter. In the second fold, the proposed work is implemented a statistical hypothesis based agent which is adaptively control the various parameters and number of firefly populations in firefly algorithm based back-propagation method and this makes it more convenient for dynamic systems. The effectiveness of automatic parameter adjustment over the performance of algorithm is analyzed through correct classification rate and sum of squared error. The proposed method is tested over five bench mark non-linear standard data set and it is compared with genetic algorithm based back-propagation method. It is observed from the experiment that the agent automatically adjust the parameters and number of firefly populations in each iteration of the back-propagation optimization phase and it is finally converged within a minimum number of iteration.

关键词： firefly algorithm Back-propagation training optimization meta-heuristic algorithm statistical hypothesis

来源：评论

学校读者我要写书评

暂无评论

firefly-Inspired Synchronization for Energy-Efficient Distance Estimation in Mobile Ad-hoc Networks

Firefly-Inspired Synchronization for Energy-Efficient Distan...

引用

31st IEEE International Performance Computing and Communications Conference (IPCCC)

作者： Merkel, Sabrina Becker, Christian Werner Schmeck, Hartmut Karlsruhe Inst Technol Inst AIFB D-76128 Karlsruhe Germany

ISBN: (纸本)9781467348836;9781467348812

Mobile ad hoc networks (MANETs) are gaining increasing significance with computing devices becoming ubiquitous and equipped with wireless communication modules. Many applications for such networks require the devices to know their position within the network or their distance to other devices. Precise determination of these parameters often fails due to lack of information, missing hardware, or inaccessibility of needed resources, making an approximation necessary. We introduce an algorithm to calculate hop counts and, thereby, derive distances between devices. The algorithm is based on synchronization of all devices in the MANET. We show that an intentional phase shift of a periodically sent signal allows to estimate the distance between all devices in a network and a specific reference device. This approach significantly reduces the communication overhead leading to a more resource-efficient operation of the communication module and, thus, potentially extending the lifetime of the mobile devices. Experiments demonstrate that a network with an average of ten devices within communication range can be synchronized using a firefly-inspired decentralized synchronization algorithm. Also, we show that the resulting distance estimates have a higher accuracy compared to the results of an algorithm which is based on asynchronous exchange of messages.

关键词： ad hoc networks distance estimation firefly algorithm synchronization nature-inspired computing resource-saving energy-efficient communication

来源：评论

学校读者我要写书评

暂无评论

Shape and size optimization of truss structures considering dynamic constraints through modern metaheuristic algorithms

引用

EXPERT SYSTEMS WITH APPLICATIONS 2012年第10期39卷 9458-9467页

作者： Fadel Miguel, Leticia Fleck Fadel Miguel, Leandro Fleck Univ Fed Rio Grande do Sul Dept Mech Engn Porto Alegre RS Brazil Univ Fed Santa Catarina Dept Civil Engn Florianopolis SC Brazil

Mass optimization on shape and sizing with multiple natural frequency constraints are highly nonlinear dynamic optimization problems. Multiple natural frequency constraints normally cause difficult dynamic sensitivity analysis and, in addition, two different types of design variables, nodal coordinates and cross-sectional areas, often lead to divergence. Thus, the choice of the appropriated method to solve this kind of problem is of paramount importance. Within this context, in this paper two of the most recent metaheuristic algorithms developed in the last decade, Harmony Search (HS) and firefly algorithm (FA), are used, for the first time here, to solve truss shape and sizing optimization with multiple natural frequency constraints. Since these metaheuristic algorithms are not a gradient-based search, they avoid most of the pitfalls of any gradient-based search algorithms. The effectiveness of Harmony Search and firefly algorithm is demonstrated through four benchmark structural optimization problems for solving shape and sizing optimization of trusses with multiple frequency constraints. The results showed that both metaheuristic algorithms reached, in a relatively low computational time, better results than the literature in three of the four examples considered, and in the other example the structure is approximately equal to the best one found, emphasizing the excellent capacity of both methods. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Nonlinear dynamic optimization problems Engineering design Frequency constraints Harmony Search firefly algorithm

来源：评论

学校读者我要写书评

暂无评论

Efficiency Analysis of Swarm Intelligence and Randomization Techniques

引用

JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2012年第2期9卷 189-198页

作者： Yang, Xin-She Natl Phys Lab Teddington TW11 0LW Middx England

Swarm intelligence has becoming a powerful technique in solving design and scheduling tasks. Metaheuristic algorithms are an integrated part of this paradigm, and particle swarm optimization is often viewed as an important landmark. The outstanding performance and efficiency cif swarm-based algorithms inspired many new developments, though mathematical understanding of metaheuristics remains partly a mystery. In contrast to the classic deterministic algorithms, metaheuristics such as PSO always use some form of randomness, and such randomization now employs various techniques. This paper intends to review and analyze some of the convergence and efficiency associated with metaheuristics such as firefly algorithm, random walks, and Levy flights. We will discuss how these techniques are used and their implications for further research.

关键词： algorithm Cuckoo Search firefly algorithm Swarm Intelligence Metaheuristics Optimization Randomization

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Intelligent algorithm for Short-Term Energy Price Forecasting in the Ontario Market

A Hybrid Intelligent Algorithm for Short-Term Energy Price F...

引用

General Meeting of the IEEE-Power-and-Energy-Society

作者： Mandal, Paras Haque, Ashraf U. Meng, Julian Martinez, Ralph Srivastava, Anurag K. Univ Texas El Paso Dept Ind Mfg & Syst Engn El Paso TX 79968 USA Univ New Brunswick Dept Elect & Comp Engn Fredericton NB E3B 5A3 Canada Univ Texas El Paso CERM El Paso TX 79968 USA Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99163 USA

ISBN: (纸本)9781467327299

Price forecasting is a crucial information for market participants in an electricity market. However, the electricity price is a complex signal due to its nonlinearity, stochasticity, and time dependent behavior. This paper presents a novel hybrid intelligent algorithm that uses the combination of a data filtering technique based on the wavelet transform (WT), an optimization technique based on firefly (FF) algorithm, and a soft computing model based on fuzzy ARTMAP (FA) network. The innovative contribution of this paper is an application of the FF algorithm to optimize FA network that utilizes the historical ill-behaved energy price time-series through the WT. Good forecast performance and adaptability of the proposed hybrid WT+FF+FA model to changes in the data is illustrated using the Ontario market power system data. The test results demonstrates that the proposed hybrid technique is able to improve the day-ahead price forecasting performance significantly when compared with other conventional soft computing models available in literature.

关键词： Electricity market firefly algorithm fuzzy ARTMAP short-term price forecasting wavelet transform

来源：评论

学校读者我要写书评

暂无评论

Damping the Cavity-Mode Anti-Resonances' Peaks on a Power Plane by Swarm Intelligence algorithms

Damping the Cavity-Mode Anti-Resonances' Peaks on a Power Pl...

引用

IEEE International Symposium on Circuits and Systems

作者： Tripathi, Jai Narayan Chhabra, Nitin Kumar Nagpal, Raj Kumar Malik, Rakesh Mukherjee, Jayanta Indian Inst Technol Dept EE Bombay 400076 Maharashtra India STMicroelect Pvt Ltd Greater Noida India

ISBN: (纸本)9781467302197

Swarm intelligence is applied to a module of high speed system design problem. To maintain power integrity in a high speed system, an effective methodology for suppressing the cavity-mode anti-resonances' peaks is presented. The optimal values and the optimal positions of the decoupling capacitors are found using three different swarm intelligence methods - particle swarm optimization, cuckoo search method and firefly algorithm. Optimum values and locations of decoupling capacitors are obtained, by which anti-resonances' peaks of loaded board are minimized.

关键词： Power Plane Cavity-Mode Anti-Resonances Decoupling Capacitor Swarm Intelligence Particle Swarm Optimization Cuckoo Search Method firefly algorithm

来源：评论

学校读者我要写书评

暂无评论

FA-based Optimal Strategy of Train's Energy Saving with Energy Materials

FA-based Optimal Strategy of Train's Energy Saving with Ener...

引用

2nd International Conference on Information Science, Automation and Material System (ISAM 2012)

作者： Hu, Hui E China Jiaotong Univ Nanchang 330013 Peoples R China

ISBN: (纸本)9783037853740

According to an energy-efficient mathematical model which usually uses GA algorithm to solve, a new algorithm based on firefly optimization algorithm is applied to solve it. Through calculation for a cited example, simulation results such as speed-position curve are got. Compared with other methods, it demonstrates this new FA-based algorithm has a better performance and can be considered to be put into practical use..

关键词： Energy Saving Optimal Strategy firefly algorithm Comparison Analysis Energy Materials

来源：评论

学校读者我要写书评

暂无评论

A Metaheuristic Optimization algorithm for Unsupervised Robotic Learning

A Metaheuristic Optimization Algorithm for Unsupervised Robo...

引用

IEEE International Conference on Computational Intelligence and Cybernetics (CYBERNETICSCOM 2012)

作者： Lin, Jiann-Horng Li, Yu-Lin I Shou Univ Dept Informat Management Kaohsiung Taiwan

ISBN: (纸本)9781467308922;9781467308915

A meta-heuristic search algorithm intended to introduce chaotic dynamics and Levy flights into the algorithm is presented in this paper. Among most evolutionary computation for optimization problem including meta-heuristic search algorithms, the solution is drawn like a moth to a flame and cannot keep away. The fine balance between intensification (exploitation) and diversification (exploration) is very important to the overall efficiency and performance of an algorithm. Too little exploration and too much exploitation could cause the system to be trapped in local optima, which makes it very difficult or even impossible to find the global optimum. The track of chaotic variable can travel ergodically over the whole search space. In general, the chaotic variable has special characters, i.e., ergodicity, pseudo-randomness and irregularity. To enrich the searching behavior and to avoid being trapped into local optimum, chaotic sequence and a chaotic Levy flight are incorporated in the meta-heuristic search for efficiently generating new solutions. The proposed algorithm with quite general objective function is used to study the ability to develop unsupervised robotic learning such as the maze exploring ability.

关键词： metaheuristic optimization firefly algorithm Levy flight chaotic sequence robotic learning

来源：评论

学校读者我要写书评

暂无评论

Interference Mitigation Spectrum Allocation for Energy Efficient OFDMA Femtocell Networks

Interference Mitigation Spectrum Allocation for Energy Effic...

引用

2nd International Conference on Computer Science and Network Technology (ICCSNT)

作者： Lu, Jing Zheng, Wei Su, Tao Wen, Xiangming Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conve Beijing 100088 Peoples R China

ISBN: (纸本)9781467329644;9781467329637

Femtocell networks, consisting of a conventional macrocell and overlaying femtocells, forming a hierarchical cell structure, constitute an attractive solution to improve the capacity and coverage. However, the inter-tier and intra-tier interferences in such systems can significantly reduce the capacity and cause an unacceptably high level of outage. This paper treats the downlink interference problem in orthogonal frequency-division multiple-access (OFDMA) based femtocell networks with partial co-channel deployment. We first propose an inter-tier interference mitigation strategy by forcing the macro-interfering femtocells to use the dedicated sub-channels. The non-interfering femtocells, on the other hand, will use the shared sub-channels which are also used by the macrocell. We then present the sub-channels allocation scheme based on the firefly algorithm to mitigate the intra-tier interference. Finally, we reassign the ratio between the dedicated and shared subchannels to maximize the efficient utilization. The propose interference mitigation scheme offers significant performance improvement by substantially reducing the inference and improving fairness and energy efficiency of utilization in the system.

关键词： Femtocell OFDMA interference mitigation firefly algorithm Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of Different Optimization algorithms for Power Demand Forecasting Applications in a Smart Grid Environment

引用

Procedia Computer Science 2012年 12卷 320-325页

作者： Ashraf Ul Haque Paras Mandal Julian Meng Ricardo L. Pineda Department of Electrical and Computer Engineering University of New Brunswick Fredericton NB E3B 5A3 Canada Department of Industrial Manufacturing and Systems Engineering University of Texas at El Paso El Paso TX 79968 USA

This paper presents an in-depth performance evaluation of three different optimization algorithms, in particular genetic algorithm (GA), particle swarm optimization (PSO), and firefly (FF) algorithm for power demand forecasting in a deregulated electricity market and smart grid environments. In this framework, this paper proposes a hybrid intelligent algorithm for power demand forecasts using the combination of wavelet transform (WT) and fuzzy ARTMAP (FA) network that is optimized by using FF optimization algorithm. The effectiveness and accuracy of the proposed hybrid WT+FF+FA model is trained and tested utilizing the data obtained from ISO-NE electricity market.

关键词： Electricity market firefly algorithm fuzzy ARTMAP genetic algorithm load demand forecasting neural networks particle swarm optimization smart grid

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：