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Grid job scheduling using Route with Genetic Algorithm support

MELLO, Rodrigo Fernandes de; ANDRADE FILHO, Jose A.; SENGER, Luciano J.; YANG, Laurence T.
Fonte: SPRINGER Publicador: SPRINGER
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
36.26%
In 2006 the Route load balancing algorithm was proposed and compared to other techniques aiming at optimizing the process allocation in grid environments. This algorithm schedules tasks of parallel applications considering computer neighborhoods (where the distance is defined by the network latency). Route presents good results for large environments, although there are cases where neighbors do not have an enough computational capacity nor communication system capable of serving the application. In those situations the Route migrates tasks until they stabilize in a grid area with enough resources. This migration may take long time what reduces the overall performance. In order to improve such stabilization time, this paper proposes RouteGA (Route with Genetic Algorithm support) which considers historical information on parallel application behavior and also the computer capacities and load to optimize the scheduling. This information is extracted by using monitors and summarized in a knowledge base used to quantify the occupation of tasks. Afterwards, such information is used to parameterize a genetic algorithm responsible for optimizing the task allocation. Results confirm that RouteGA outperforms the load balancing carried out by the original Route...

MP-SMO: um algoritmo para a implementação VLSI do treinamento de máquinas de vetores de suporte.; MP-SMO: an algorithm for the VLSI implementation of the support vector machines training.

Acosta Hernández, Raúl
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 02/09/2009 PT
Relevância na Pesquisa
26.33%
Máquinas de aprendizagem, como Redes Neuronais Artificiais (ANNs), Redes Bayesianas, Máquinas de Vetores de Suporte (SVMs) e outras, são aplicadas em problemas de classificação de padrões. Devido ao baixo erro de teste, a SVM possui uma grande quantidade de aplicações, como no reconhecimento de imagens, seleção de genes, classificação de textos, robótica, reconhecimento de escrita a mão e outras. Dos algoritmos desenvolvidos para o treinamento da SVM, o Sequential Minimal Optimization (SMO) é um dos mais rápidos e o mais fácil de implementar em software. Devido a sua importância, várias otimizações para diminuir ainda mais o seu tempo de execução têm sido reportadas. A maioria das implementações do treinamento da SVM foram realizadas em software. Não obstante, a implementação em hardware é necessária em algumas aplicações com restrições: de área, e/ou de energia e/ou de tempo de treinamento, por exemplo, em algumas aplicações portáveis ou móveis. Nas implementações em hardware anteriores a este trabalho, o treinamento da SVM foi realizado com um conjunto de exemplos cuja quantidade é da ordem de somente dezenas, e unicamente uma delas usou o algoritmo SMO. Neste trabalho é apresentada uma modificação do algoritmo SMO...

Seleção de fornecedores de serviço de transporte utilizando leilão combinatório de compras: adaptação e aplicação do algoritmo Iterative Deepening Search A* (IDA*).; Supplier selection of transportation services using reverse combinatorial auction: adaptation and aplication of Iterative Deepening Search A* (IDA*).

Higuita Salazar, Catalina
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 15/12/2011 PT
Relevância na Pesquisa
46.17%
A seleção de fornecedores de transporte é um desafio cada vez maior. O crescimento da rede de clientes a ser coberta demanda uma alocação eficiente em termos de custo não suprida por mecanismos tradicionais de negociação. Neste âmbito, o leilão combinatório torna-se uma alternativa de negociação ao permitir capturar sinergias entre os trajetos que devem ser atendidos. Em conseqüência disso, diminui-se o custo de transporte do fornecedor que se reflete nos menores preços de suas propostas e finalmente no custo total de compra do serviço. Por outro lado, esta decisão envolve fatores além do custo total; a mensuração destes torna-se importante para identificar fornecedores que melhor se ajustam aos requerimentos do comprador. No entanto, é fundamental escolher um método adequado para sua avaliação porque este influência a decisão final. Este problema de compra de serviços de transporte é conhecido na literatura como Winner Determination Problem (WDP) que, devido a sua complexidade, possui uma resolução limitada. Após revisão teórica, foi observado que os estudos relacionados à área de transporte focalizavam o desenvolvimento de modelos matemáticos que fossem representativos da realidade. Alguns destes modelos abordam a utilização de múltiplos critérios atribuindo um coeficiente que pondera cada critério. Evidenciou-se a necessidade do desenvolvimento de um algoritmo alternativo que além de facilitar sinergias entre trajetos...

A self-learning simulated annealing algorithm for global optimizations of electromagnetic devices

Yang, S. Y.; Machado, J. M.; Ni, G. Z.; Ho, S. L.; Zhou, P.
Fonte: Institute of Electrical and Electronics Engineers (IEEE) Publicador: Institute of Electrical and Electronics Engineers (IEEE)
Tipo: Artigo de Revista Científica Formato: 1004-1008
ENG
Relevância na Pesquisa
36.06%
A self-learning simulated annealing algorithm is developed by combining the characteristics of simulated annealing and domain elimination methods. The algorithm is validated by using a standard mathematical function and by optimizing the end region of a practical power transformer. The numerical results show that the CPU time required by the proposed method is about one third of that using conventional simulated annealing algorithm.

A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest

Rodrigues, Douglas; Pereira, Luís A.M.; Nakamura, Rodrigo Y.M.; Costa, Kelton A.P.; Yang, Xin-She; Souza, André N.; Papa, João Paulo
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
35.99%
Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness. © 2013 Elsevier Ltd. All rights reserved.

Optimizing urban traffic flow using genetic algorithm with petri net analysis as fitness function

Dezani, Henrique; Bassi, Regiane Denise Solgon; Marranghello, Norian; Gomes, Luis Filipe dos Santos; Damiani, Furio; Silva, Ivan Nunes da
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Artigo de Revista Científica Formato: 162-167
ENG
Relevância na Pesquisa
36.03%
This paper describes a new methodology adopted for urban traffic stream optimization. By using Petri net analysis as fitness function of a Genetic Algorithm, an entire urban road network is controlled in real time. With the advent of new technologies that have been published, particularly focusing on communications among vehicles and roads infrastructures, we consider that vehicles can provide their positions and their destinations to a central server so that it is able to calculate the best route for one of them. Our tests concentrate on comparisons between the proposed approach and other algorithms that are currently used for the same purpose, being possible to conclude that our algorithm optimizes traffic in a relevant manner.

Busca na web e agrupamento de textos usando computação inspirada na biologia; Search in the web and text clustering using computing inspired by biology

Andre Luiz Vizine Pereira
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 18/12/2007 PT
Relevância na Pesquisa
26.24%
A Internet tornou-se um dos principais meios de comunicação da atualidade, reduzindo custos, disponibilizando recursos e informação para pessoas das mais diversas áreas e interesses. Esta dissertação desenvolve e aplica duas abordagens de computação inspirada na biologia aos problemas de otimização do processo de busca e recuperação de informação na web e agrupamento de textos. Os algoritmos investigados e modificados são o algoritmo genético e o algoritmo de agrupamento por colônia de formigas. O objetivo final do trabalho é desenvolver parte do conjunto de ferramentas que será usado para compor o núcleo de uma comunidade virtual acadêmica adaptativa. Os resultados obtidos mostraram que o algoritmo genético é uma ferramenta adequada para otimizar a busca de informação na web, mas o algoritmo de agrupamento por colônia de formigas ainda apresenta limitações quanto a sua aplicabilidade para agrupamento de textos.; The Internet became one of the main sources of information and means of communication, reducing costs and providing resources and information to the people all over the world. This dissertation develops and applies two biologically-inspired computing approaches, namely a genetic algorithm and the ant-clustering algorithm...

Optimizing surface finish in WEDM using the taguchi parameter design method

Pasam,Vamsi Krishna; Battula,Surendra Babu; Madar Valli,P.; Swapna,M.
Fonte: Associação Brasileira de Engenharia e Ciências Mecânicas - ABCM Publicador: Associação Brasileira de Engenharia e Ciências Mecânicas - ABCM
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/06/2010 EN
Relevância na Pesquisa
35.93%
Wire electrical discharge machining (WEDM) is extensively used in machining of materials when precision is of major factor. Selection of optimum machining parameter combinations for obtaining higher accuracy is a challenging task in WEDM due to the presence of a large number of process variables and complex stochastic process mechanisms. In the present work, WEDM of titanium alloy (Ti6Al4V) is experimentally studied. The behavior of eight control parameters such as Ignition pulse current (A), Short pulse duration(B), Time between two pulses(C), Servo speed(D), Servo reference voltage(E), Injection pressure(F), Wire speed(G) and Wire tension(H) on surface finish was studied using Taguchi parameter design. A mathematical model is developed by means of linear regression analysis to establish relationship between control parameters and surface finish as process response. An attempt is made to optimize the surface roughness prediction model using Genetic Algorithm (GA). Optimum values of control parameters at level A1, B1, C1, D3, E1, F3, G2, H3 for the selected range and workpiece material are obtained.

Nulls and side lobe levels control in a time modulated linear antenna array by optimizing excitations and element locations using RGA

Goswami,Bipul; Mandal,Durbadal
Fonte: Sociedade Brasileira de Microondas e Optoeletrônica e Sociedade Brasileira de Eletromagnetismo Publicador: Sociedade Brasileira de Microondas e Optoeletrônica e Sociedade Brasileira de Eletromagnetismo
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/12/2013 EN
Relevância na Pesquisa
35.93%
In this paper a novel approach based on the Real coded Genetic Algorithm (RGA) is proposed to improve nulling performance as well as suppression of Side Lobe Level (SLL) of a Time Modulated Linear Antenna Arrays (TMLA). RGA adjusts the static excitation amplitudes as well as the location of each element from the origin to place deeper nulls in the desired direction. Three design examples are presented that illustrate the use of the RGA, and the optimization goal in each example is easily achieved.

A Multiuser Detector Based on Artificial Bee Colony Algorithm for DS-UWB Systems

Yin, Zhendong; Liu, Xiaohui; Wu, Zhilu
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Publicado em 31/07/2013 EN
Relevância na Pesquisa
26.26%
Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. The ABC algorithm was developed to solve optimizing numerical problems and revealed premising results in processing time and solution quality. In ABC, a colony of artificial bees search for rich artificial food sources; the optimizing numerical problems are converted to the problem of finding the best parameter which minimizes an objective function. Then, the artificial bees randomly discover a population of initial solutions and then iteratively improve them by employing the behavior: moving towards better solutions by means of a neighbor search mechanism while abandoning poor solutions. In this paper, an efficient multiuser detector based on a suboptimal code mapping multiuser detector and artificial bee colony algorithm (SCM-ABC-MUD) is proposed and implemented in direct-sequence ultra-wideband (DS-UWB) systems under the additive white Gaussian noise (AWGN) channel. The simulation results demonstrate that the BER and the near-far effect resistance performances of this proposed algorithm are quite close to those of the optimum multiuser detector (OMD) while its computational complexity is much lower than that of OMD. Furthermore...

Optimizing Strategic Safety Stock Placement in Two-Layer Supply Chains

Lesnaia, Ekaterina
Fonte: MIT - Massachusetts Institute of Technology Publicador: MIT - Massachusetts Institute of Technology
Tipo: Artigo de Revista Científica Formato: 104685 bytes; application/pdf
EN_US
Relevância na Pesquisa
35.93%
In this paper, we minimize the holding cost of the safety stock in the supply chain subject to linear constraints on the service times between the nodes of the network. In the problem, the objective function is concave as we assume the demand to be bounded by a concave function. The optimal solutions of the problem belong to the set of extreme points of the polyhedron, specified by the constraints of the problem. We first characterize the extreme points for the two-layer networks and then provide bounds to use in a branch and bound algorithm.; Singapore-MIT Alliance (SMA)

Application of Genetic Algorithm in Optimization of Advanced Photovoltaic Devices

Michael, Sherif
Fonte: Escola de Pós-Graduação Naval Publicador: Escola de Pós-Graduação Naval
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
35.99%
Proceedings of the 11th WSEAS International Conference on CIRCUITS, Agios Nikolaos, Crete Island, Greece, July 23-25, 2007; The primary goal of multijunction solar cell design is to maximize the output power for a given solar spectrum. The construction of multijunction cells places the individual junction layers in series, thereby limiting the overall output current to that of the junction layer producing the lowest current. The solution to optimizing a multijunction design involves both the design of individual junction layers which produce an optimum output power and the design of a series-stacked configuration of these junction layers which yields the highest possible overall output current. This paper demonstrates the use of Genetic Algorithm in a two-part process to refine a given multijunction solar cell design for near-optimal output power for a desired light spectrum.

Optimizing safe motion for autonomous vehicles

Shirasaka, Masahide
Fonte: Monterey, California. Naval Postgraduate School Publicador: Monterey, California. Naval Postgraduate School
Tipo: Tese de Doutorado Formato: 67 p.;28 cm.
EN_US
Relevância na Pesquisa
35.99%
Approved for public release; distribution is unlimited; There are two goals for autonomous vehicle navigation planning: shortest path and safe path. These goals are often in conflict; path safety is more important. Safety of the autonomous vehicle's navigation is determined by the clearances between the vehicle and obstacles. Because a Voronoi boundary is the set of points locally maximizing the clearance from obstacles, safety is maximized on it. Therefore Voronoi Diagrams are suitable for motion planning of autonomous vehicles. We use the derivative of curvature k of the vehicle motion (dk/ds) as the only control variable for the vehicle where s is the length along the vehicle trajectory. Previous motion planning of the autonomous mobile robot Yamabico-11 at Naval Postgraduate School used a path tracking method. Before the mission began the vehicle was given a track to follow; motion planning consisted of calculating the point on the track closest to the vehicle and calculating dk/ ds then steering the vehicle to get onto track. We propose a method of planning safe motions of the vehicle to calculate optimal dk/ds at each point directly from the information of the world without calculating the track to follow. This safe navigation algorithm is fundamentally different from the path tracking using a path specification. Additionally motion planning is simpler and faster than the path tracking method. The effectiveness of this steering function for vehicle motion control is demonstrated by algorithmic simulation and by use on the autonomous mobile robot Yamabico 11 at the Naval Postgraduate School; http://archive.org/details/optimizingsafemo00shir; Lieutenant Junior Grade...

The Algorithm of Pipelined Gossiping

De Florio, Vincenzo; Blondia, Chris
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 13/04/2015
Relevância na Pesquisa
35.93%
A family of gossiping algorithms depending on a parameter permutation is introduced, formalized, and discussed. Several of its members are analyzed and their asymptotic behaviour is revealed, including a member whose model and performance closely follows the one of hardware pipelined processors. This similarity is exposed. An optimizing algorithm is finally proposed and discussed as a general strategy to increase the performance of the base algorithms.; Comment: Paper published in the Journal of Systems Architecture, Vol. 52 (2006). Elsevier

A Novel and Robust Evolution Algorithm for Optimizing Complicated Functions

Gao, Yifeng; Gong, Shuhong; Zhao, Ge
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 17/08/2011
Relevância na Pesquisa
26.29%
In this paper, a novel mutation operator of differential evolution algorithm is proposed. A new algorithm called divergence differential evolution algorithm (DDEA) is developed by combining the new mutation operator with divergence operator and assimilation operator (divergence operator divides population, and, assimilation operator combines population), which can detect multiple solutions and robustness in noisy environment. The new algorithm is applied to optimize Michalewicz Function and to track changing of rain-induced-attenuation process. The results based on DDEA are compared with those based on Differential Evolution Algorithm (DEA). It shows that DDEA algorithm gets better results than DEA does in the same premise. The new algorithm is significant for optimizing and tracking the characteristics of MIMO (Multiple Input Multiple Output) channel at millimeter waves.; Comment: 4papers

A Standard Approach for Optimizing Belief Network Inference using Query DAGs

Darwiche, Adnan; Provan, Gregory M.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 06/02/2013
Relevância na Pesquisa
26.31%
This paper proposes a novel, algorithm-independent approach to optimizing belief network inference. rather than designing optimizations on an algorithm by algorithm basis, we argue that one should use an unoptimized algorithm to generate a Q-DAG, a compiled graphical representation of the belief network, and then optimize the Q-DAG and its evaluator instead. We present a set of Q-DAG optimizations that supplant optimizations designed for traditional inference algorithms, including zero compression, network pruning and caching. We show that our Q-DAG optimizations require time linear in the Q-DAG size, and significantly simplify the process of designing algorithms for optimizing belief network inference.; Comment: Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997)

Optimizing Auto-correlation for Fast Target Search in Large Search Space

Mahmood, Arif; Mian, Ajmal; Owens, Robyn
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
26.29%
In remote sensing image-blurring is induced by many sources such as atmospheric scatter, optical aberration, spatial and temporal sensor integration. The natural blurring can be exploited to speed up target search by fast template matching. In this paper, we synthetically induce additional non-uniform blurring to further increase the speed of the matching process. To avoid loss of accuracy, the amount of synthetic blurring is varied spatially over the image according to the underlying content. We extend transitive algorithm for fast template matching by incorporating controlled image blur. To this end we propose an Efficient Group Size (EGS) algorithm which minimizes the number of similarity computations for a particular search image. A larger efficient group size guarantees less computations and more speedup. EGS algorithm is used as a component in our proposed Optimizing auto-correlation (OptA) algorithm. In OptA a search image is iteratively non-uniformly blurred while ensuring no accuracy degradation at any image location. In each iteration efficient group size and overall computations are estimated by using the proposed EGS algorithm. The OptA algorithm stops when the number of computations cannot be further decreased without accuracy degradation. The proposed algorithm is compared with six existing state of the art exhaustive accuracy techniques using correlation coefficient as the similarity measure. Experiments on satellite and aerial image datasets demonstrate the effectiveness of the proposed algorithm.

A natural-inspired optimization machine based on the annual migration of salmons in nature

Mozaffari, Ahmad; Fathi, Alireza
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 14/12/2013
Relevância na Pesquisa
26.34%
Bio inspiration is a branch of artificial simulation science that shows pervasive contributions to variety of engineering fields such as automate pattern recognition, systematic fault detection and applied optimization. In this paper, a new metaheuristic optimizing algorithm that is the simulation of The Great Salmon Run(TGSR) is developed. The obtained results imply on the acceptable performance of implemented method in optimization of complex non convex, multi dimensional and multi-modal problems. To prove the superiority of TGSR in both robustness and quality, it is also compared with most of the well known proposed optimizing techniques such as Simulated Annealing (SA), Parallel Migrating Genetic Algorithm (PMGA), Differential Evolutionary Algorithm (DEA), Particle Swarm Optimization (PSO), Bee Algorithm (BA), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Cuckoo Search (CS). The obtained results confirm the acceptable performance of the proposed method in both robustness and quality for different bench-mark optimizing problems and also prove the authors claim.; Comment: 12 pages, 3 figures, 2 tables

Optimizing the Gossip Algorithm with Non-Uniform Clock Distribution over Classical & Quantum Networks

Jafarizadeh, Saber
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 11/12/2015
Relevância na Pesquisa
26.41%
Distributed gossip algorithm has been studied in literature for practical implementation of the distributed consensus algorithm as a fundamental algorithm for the purpose of in-network collaborative processing. This paper focuses on optimizing the convergence rate of the gossip algorithm for both classical and quantum networks. A novel model of the gossip algorithm with non-uniform clock distribution is proposed which can reach the optimal convergence rate of the continuous-time consensus algorithm. It is described that how the non-uniform clock distribution is achievable by modifying the rate of the Poisson process modeling the clock of the gossip algorithm. The minimization problem for optimizing the asymptotic convergence rate of the proposed gossip algorithm and its corresponding semidefinite programming formulation is addressed analytically. It is shown that the optimal results obtained for uniform clock distribution are suboptimal compared to those of the non-uniform one and for non-uniform distribution the optimal answer is not unique i.e. there is more than one set of probabilities that can achieve the optimal convergence rate. Based on the optimal continuous-time consensus algorithm and the detailed balance property, an effective method of obtaining one of these optimal answers is proposed. Regarding quantum gossip algorithm...

Self-Optimizing Mechanisms for EMF Reduction in Heterogeneous Networks

Sidi, Habib B. A.; Altman, Zwi; Tall, Abdoulaye
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 15/01/2014
Relevância na Pesquisa
36.17%
This paper focuses on the exposure to Radio Frequency (RF) Electromagnetic Fields (EMF) and on optimization methods to reduce it. Within the FP7 LEXNET project, an Exposure Index (EI) has been defined that aggregates the essential components that impact exposure to EMF. The EI includes, among other, downlink (DL) exposure induced by the base stations (BSs) and access points, the uplink (UL) exposure induced by the devices in communication, and the corresponding exposure time. Motivated by the EI definition, this paper develops stochastic approximation based self-optimizing algorithm that dynamically adapts the network to reduce the EI in a heterogeneous network with macro- and small cells. It is argued that the increase of the small cells' coverage can, to a certain extent, reduce the EI, but above a certain limit, will deteriorate DL QoS. A load balancing algorithm is formulated that adapts the small cell' coverage based on UL loads and a DL QoS indicator. The proof of convergence of the algorithm is provided and its performance in terms of EI reduction is illustrated through extensive numerical simulations.