检索结果-内蒙古大学图书馆

Proceedings of IEEE East West Design and Test Symposium ( EWDTS 2014)

作者： Kuliev, E. V. Kureychik, V. V. Dukkardt, A. N. Legebokov, A. A. Southern Fed Univ Coll Automat & Comp Sci Rostov Na Donu Russia

ISBN: (纸本)9781479976300

The article discusses the key problem of swarm algorithms and the bioinspired approach, which is to determine the proximity function and study the emerging neighborhoods in order to solve optimization problems. There is a detailed discussion of one of the most important design phases, namely, the VLSI components placement problem, whose solutions fineness directly affects the quality of circuit tracing. The solution of the neighborhoods and solution proximity problem is demonstrated by the study of the solutions by means of hybrid search methods. The main idea of this approach is the sequential use of genetic and swarm algorithms. We propose a new formation principle of the positions' neighborhood in the solution space based on the bee colony algorithm, which uses the concept of neighborhood in a circular search space. There are also experimental studies which show that the time complexity of the developed approach does not go beyond polynomial dependence.

关键词： VLSI genetic algorithms integrated circuit layout particle swarm optimisation VLSI components placement problem circular search genetic algorithm hybrid search method neighborhood research neighborhood search optimization problems proximity function swarm algorithms swarm intelligence Algorithm design and analysis Educational institutions Genetic algorithms Search problems Sociology Statistics Very large scale integration Particle swarm optimization Very large scale integration Genetic Algorithm (GA) Algorithm design and analysis tabu search integrated circuit layout Educational institutions Sociology Neighborhood Search methods

来源：评论

学校读者我要写书评

暂无评论

swarm Intelligence for Optimizing Hybridized Smoothing Filter in Image Edge Enhancement

Swarm Intelligence for Optimizing Hybridized Smoothing Filte...

引用

1st International Conference on swarm, Evolutionary, and Memetic Computing

作者： Rao, B. Tirumala Dehuri, S. Dileep, M. Vindhya, A. ANITS Dept Comp Sci & Engn Visakhapatnam Andhra Pradesh India Fakir Mohan Univ Dept Informat & Commun Technol Vyasa Vihar 756019 Balasore India

ISBN: (纸本)9783642175626

In this modern era, image transmission and processing plays a major role. It would be impossible to retrieve information from satellite and medical images without the help of image processing techniques. Edge enhancement is an image processing step that enhances the edge contrast of an image or video in an attempt to improve its acutance. Edges are the representations of the discontinuities of image intensity functions. For processing these discontinuities in an image, a good edge enhancement technique is essential. The proposed work uses a new idea for edge enhancement using hybridized smoothening filters and we introduce a promising technique of obtaining best hybrid filter using swarm algorithms (Artificial Bee Colony (ABC), Particle swarm Optimization (PSO) and Ant Colony Optimization (ACO)) to search for an optimal sequence of filters from among a set of rather simple, representative image processing filters. This paper deals with the analysis of the swarm intelligence techniques through the combination of hybrid filters generated by these algorithms for image edge enhancement.

关键词： Image edge enhancement hybridized smoothening filter swarm algorithms PSO ACO ABC

来源：评论

学校读者我要写书评

暂无评论

DISTRIBUTED UAV-swarm-BASED REAL-TIME GEOMATIC DATA COLLECTION UNDER DYNAMICALLY CHANGING RESOLUTION REQUIREMENTS 4

DISTRIBUTED UAV-SWARM-BASED REAL-TIME GEOMATIC DATA COLLECTI...

引用

International Conference on Unmanned Aerial Vehicles in Geomatics

作者： Almeida, Miguel Hildmann, Hanno Solmaz, Guerkan NEC Labs Europe Kurfursten Anlage 36 D-69115 Heidelberg Germany Univ Carlos III Madrid UC3M Av Univ 30 Leganes 28911 Spain

Unmanned Aerial Vehicles (UAVs) have been used for reconnaissance and surveillance missions as far back as the Vietnam War, but with the recent rapid increase in autonomy, precision and performance capabilities - and due to the massive reduction in cost and size - UAVs have become pervasive products, available and affordable for the general public. The use cases for UAVs are in the areas of disaster recovery, environmental mapping & protection and increasingly also as extended eyes and ears of civil security forces such as fire-fighters and emergency response units. In this paper we present a swarm algorithm that enables a fleet of autonomous UAVs to collectively perform sensing tasks related to environmental and rescue operations and to dynamically adapt to e.g. changing resolution requirements. We discuss the hardware used to build our own drones and the settings under which we validate the proposed approach.

关键词： Self-organization adaptive behaviour swarm algorithms distributed sensing autonomous decision making

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Bare Bones Particle swarm for Solving Constrained Optimization Problems

Hierarchical Bare Bones Particle Swarm for Solving Constrain...

引用

IEEE Congress on Evolutionary Computation

作者： Campos, Mauro Krohling, Renato A. Univ Fed Espirito Santo Dept Estat Programa Posgrad Informat Av Fernando Ferrari 514 BR-29075910 Vitoria ES Brazil Univ Fed Espirito Santo Dept Engn Producao Programa Posgrad Informat BR-29075910 Vitoria ES Brazil

ISBN: (纸本)9781479904549;9781479904532

Bare bones particle swarm optimization (BBPSO) is a well-known swarm algorithm which has shown potential for solving single-objective unconstrained optimization problems. In this paper, firstly, we propose a generalization of the BBPSO, named by us as hierarchical BBPSO, HBBPSO for short. Next a hybrid approach is introduced combining the constraint-handling method based on sum of ranks with the HBBPSO algorithm for solving single-objective constrained optimization problems. In the HBBPSO, the position of a particle is selected from a multivariate t-distribution. The multivariate t-distribution is used in its hierarchical form as a member of the flexible class of scale mixtures of normal distributions. The t-distribution has heavier tails than those of the normal distribution, which increases the ability of the particles to escape from a local optimum. In addition, the t-distribution includes the normal case when the number of degrees of freedom of the t-distribution is sufficiently large. As a result, the t-distribution can be applied during the optimization process, while maintaining the proper equilibrium between exploration and exploitation. An empirical study has been carried out to evaluate the performance of the proposed approach. The experimental results show the suitability of the proposed algorithm in terms of effectiveness and robustness to find good solutions for all benchmark problems tested.

关键词： Constrained optimization constraint handling ranking swarm algorithms scale mixtures of normal distributions

来源：评论

学校读者我要写书评

暂无评论

Proof of swarm Based Ensemble Learning for Federated Learning Applications 23

Proof of Swarm Based Ensemble Learning for Federated Learnin...

引用

38th Annual ACM Symposium on Applied Computing (ACM SAC)

作者： Raza, Ali Kim Phuc Tran Koehl, Ludovic Li, Shujun Univ Lille ENSAIT GEMTEX Genie & Mat Text ULR 2461 Lille France Univ Kent Inst Cyber Secur Soc iCSS Canterbury Kent England Univ Kent Sch Comp Canterbury Kent England

ISBN: (纸本)9781450395175

Ensemble learning combines results from multiple machine learning models in order to provide a better and optimised predictive model with reduced bias, variance and improved predictions. However, in federated learning it is not feasible to apply centralised ensemble learning directly due to privacy concerns. Hence, a mechanism is required to combine results of local models to produce a global model. Most distributed consensus algorithms, such as Byzantine fault tolerance (BFT), do not normally perform well in such applications. This is because, in such methods predictions of some of the peers are disregarded, so a majority of peers can win without even considering other peers' decisions. Additionally, the confidence score of the result of each peer is not normally taken into account, although it is an important feature to consider for ensemble learning. Moreover, the problem of a tie event is often left un-addressed by methods such as BFT. To fill these research gaps, we propose PoSw (Proof of swarm), a novel distributed consensus algorithm for ensemble learning in a federated setting, which was inspired by particle swarm based algorithms for solving optimisation problems. The proposed algorithm is theoretically proved to always converge in a relatively small number of steps and has mechanisms to resolve tie events while trying to achieve sub-optimum solutions. We experimentally validated the performance of the proposed algorithm using ECG classification as an example application in healthcare, showing that the ensemble learning model outperformed all local models and even the FL-based global model.

关键词： Privacy federated learning ensemble consensus protocol evolutionary computing swarm algorithms healthcare

来源：评论

学校读者我要写书评

暂无评论

Tornado: A robust adaptive foraging algorithm for swarm robots

Tornado: A robust adaptive foraging algorithm for swarm robo...

引用

4th Global Congress on Intelligent Systems (GCIS)

作者： Magdy, Dina Alkabani, Yousra Bedor, Hassan Ain Shams Univ Dept Comp & Syst Engn Cairo Egypt

ISBN: (纸本)9781479928859

Foraging is a benchmark problem for swarm robotics. It is inspired by swarms of insects cooperating to locate and/or transport food items that a single individual cannot move. The challenge is to program a swarm of simple robots, with minimal communication and individual capability, to search the environment for some search target and return it to their base collectively. In this paper we introduce a novel foraging algorithm: Tornado. The Tornado algorithm is inspired by the spiral tornado motion. The algorithm can scan an area with high speed given a large swarm. However, it can adapt in case of failure of some robots and successfully finish the job at a slower speed. Experimental results show that the algorithm provides better coverage and robustness compared to previous foraging algorithms.

关键词： Foraging Robustness swarm algorithms swarm Robots

来源：评论

学校读者我要写书评

暂无评论

A new method for designing multiplierless two-channel filterbank using shifted-Chebyshev polynomials

引用

INTERNATIONAL JOURNAL OF ELECTRONICS 2019年第4期106卷 537-552页

作者： Kumar, Anil Sharma, Ila PDPM Indian Inst Informat Technol Design & Mfg Dept Elect & Commun Jabalpur India

This paper presents an efficient design method for a digital multiplierless two-channel filterbank using the shifted-Chebyshev polynomials and common sub-expression elimination (CSE) algorithm for reducing hardware requirements such as adders and multipliers. For designing a two-channel filterbank, the design problem is constructed as minimization of integral meansquare error between the desired and designed response of a prototype filter in the passband and stopband. For controlling the performance in passband and stopband, two parameters (K-P, and K-S) are used, whose optimum values are determined by swam optimization techniques such as differential evolution algorithm, artificial bee colony optimization, particle swarm optimizations, cuckoo search algorithm and hybrid method using a fitness function, constructed by perfect reconstruction condition of a filterbank. The number of polynomials used for approximation depends upon the order of a prototype filter. A new hybrid CSE is proposed for further reduction of hardware requirement. A comparative study of various CSE techniques such as horizontal, vertical and proposed hybrid CSE is also made. Numerical examples illustrate the effectiveness of the proposed algorithm in the reduction of adders with comparisons accomplished using existing methods. It has been found that almost 43% adder gain can be achieved when a filter is designed with N =32 and wordlength (WL) as 12 using proposed methodology.

关键词： Filterbank swarm algorithms shifted-Chebyshev polynomial canonic sign digit sub-expression elimination

来源：评论

学校读者我要写书评

暂无评论

Influence of assortment allocation management in the warehouse on the human workload

引用

CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2020年第2期28卷 779-795页

作者： Kudelska, I Pawlowski, G. Poznan Univ Tech Fac Engn Management Strzelecka 11 PL-60965 Poznan Poland WSB Univ Poznan Fac Finance & Banking Powstancow Wielkopolskich 5 PL-61895 Poznan Poland

Activities related to allocation of goods in the warehouse are crucial for logistics. All operations with goods in the warehousing area are labour-intensive and time-consuming, which in turn generates warehousing costs. Despite the ongoing automation in this area, human labour is still applicable in many warehouses. This study includes researches on impact of the assortment allocation in the warehouse on the human factor (HR). It includes the analysis for research literature concerning the warehousing and picking influence on HR in its scope, as well as simulation of goods allocation in the warehousing area and discussion concerning the obtained results. The simulation is carried out with application of the swarm algorithm, and the results were subjected to the statistical analysis. Correlation of cost optimization and factors influencing the ergonomics of operation in the warehousing area is determined.

关键词： Human factor Warehousing Products location Warehouse costs swarm algorithms

来源：评论

学校读者我要写书评

暂无评论

Multiobjective Small-World Optimization for Energy Saving in Grid Environments

引用

COMPUTER JOURNAL 2015年第3期58卷 432-447页

作者： Arsuaga-Rios, Maria Vega-Rodriguez, Miguel A. CERN European Org Nucl Res Beams Dept CH-1211 Geneva 23 Switzerland Univ Extremadura Escuela Politecn Dept Technol Comp & Commun ARCO Res Grp Caceres 10003 Spain

Job scheduling is a challenging task in grid environments and reducing execution time is one of the most important ways to tackle this problem. Nowadays, society is also more conscious about energy saving, making this fact a really important issue to pursue. In this paper, a new multiobjective algorithm is presented based on the small-world phenomenon: Multiobjective Small-World Optimization (MOSWO), to optimize both objectives: energy consumption and execution time. MOSWO is compared with another recent and swarm algorithm based on the firefly's behaviour: Multiobjective Firefly Algorithm (MO-FA). Both algorithms are compared with the standard multiobjective algorithm Non-dominated Sorting Genetic Algorithm II to show their efficiency as multiobjective approaches. Moreover, the best algorithm proposed, MOSWO, is compared with MOHEFT (multiobjective version of one of the most used algorithms in workflow-scheduling, HEFT) and also with two real grid schedulers: Workload Management System and Deadline Budget Constraint. The results show the advantages of our proposal.

关键词： multiobjective optimization grid environments swarm algorithms energy saving execution time small-world

来源：评论

学校读者我要写书评

暂无评论

A generic framework for handling constraints with agent-based optimization algorithms and application to aerodynamic design

引用

OPTIMIZATION AND ENGINEERING 2017年第3期18卷 659-691页

作者： Poole, Daniel J. Allen, Christian B. Rendall, Thomas C. S. Univ Bristol Dept Aerosp Engn Bristol BS8 1TR Avon England

A generic constraint handling framework for use with any swarm-based optimization algorithm is presented. For swarm optimizers to solve constrained optimization problems effectively modifications have to be made to the optimizers to handle the constraints, however, these constraint handling frameworks are often not universally applicable to all swarm algorithms. A constraint handling framework is therefore presented in this paper that is compatible with any swarm optimizer, such that a user can wrap it around a chosen swarm algorithm and perform constrained optimization. The method, called separation-sub-swarm, works by dividing the population based on the feasibility of individual agents. This allows all feasible agents to move by existing swarm optimizer algorithms, hence promoting good performance and convergence characteristics of individual swarm algorithms. The framework is tested on a suite of analytical test function and a number of engineering benchmark problems, and compared to other generic constraint handling frameworks using four different swarm optimizers;particle swarm, gravitational search, a hybrid algorithm and differential evolution. It is shown that the new framework produces superior results compared to the established frameworks for all four swarm algorithms tested. Finally, the framework is applied to an aerodynamic shape optimization design problem where a shock-free solution is obtained.

关键词： Optimization Constraint handling swarm algorithms Aerodynamic shape optimization

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：