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A multiobjective optimization of a UWB antenna using a self organizing genetic algorithm

MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2012年第8期54卷 1824-1828页

作者： Silva, C. R. M. Lins, H. W. C. Martins, S. R. Barreto, E. L. F. d'Assuncao, A. G. Fed Univ Rio Grande do Norte UFRN Dept Commun Engn BR-59072970 Natal RN Brazil

This article presents the optimization process of a microstrip antenna designed to operate with ultra-wideband applications. This optimization process was accomplished by applying a self organizing multiobjective genetic algorithm (GA) to optimize a geometry aspect of a ring monopole antenna (a slit in the ground plane) considering three objectives simultaneously (bandwidth, return loss and central frequency deviation). Each of these objectives are modeled as dependent of two variables (the slit dimensions) and used in a single weighted compound aggregate objective function in which weights are defined by an adjuster GA that uses another GA to evaluate each combination of possible weights. The results were compared with the ones obtained to the same antenna using a simulator program (Ansoft HFSS) and with the results of a real prototype antenna built from the slit dimension optimal values obtained after the optimization process. (C) 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:18241828, 2012;View this article online at ***. DOI 10.1002/mop.26945

关键词： microstrip antennas multiobjective evolutionary algorithms genetic algorithms optimization

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Solving optimization Problems in Nimrod/OK using a genetic Algorithm

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Procedia Computer Science 2012年 9卷 1647-1656页

作者： Yu Hua Lim Jefferson Tana David Abramsonb Monash University Clayton 3800 Victoria Australia

A scientific workflow can be viewed as formal model of the flow of data between processing components. It often involves a combination of data integration, computation, analysis, and visualization steps. An emerging use case involves determining some input parameters that minimize (or maximize) the output of a computation. Kepler is a good framework for specifying such optimizations because arbitrary computations can be composed into a pipeline, which is then repeated until an optimal set of inputs is found. genetic algorithms are generic optimization algorithms based on the principles of genetics and natural selection, and are well suited for models with discontinuous objective functions. This paper discusses an implementation of a genetic Algorithm in Kepler, building on the Nimrod/OK framework. The resulting tool is generic and flexible enough to support a variety of experimental domains. The paper reports a number of experiments that demonstrate the performance with a set of benchmarking functions.

关键词： Scientific Worklows Kepler genetic algorithms optimization

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Distributed Unequal Error Protection Rateless Codes over Erasure Channels: A Two-Source Scenario

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IEEE TRANSACTIONS ON COMMUNICATIONS 2012年第8期60卷 2084-2090页

作者： Talari, Ali Rahnavard, Nazanin Oklahoma State Univ Dept Elect & Comp Engn Stillwater OK 74078 USA

In distributed rateless coding, multiple disjoint sources need to deliver their rateless coded symbols (where a symbol may contain a single bit or thousands of bits) to a common destination via a single relay. In this paper, we propose and design novel distributed rateless codes called DU-rateless codes that can provide Unequal Error Protection (UEP) for disjoint sources with unequal data lengths on erasure channels. To design DU-rateless codes, we tune the coding parameters at each source and propose to smartly combine the encoded symbols at the relay. We analyze DU-rateless codes employing And-Or tree analysis technique and leverage our analysis to design several sets of codes for various setups employing the-state-of-the-art multi-objective genetic algorithms. We evaluate the performance of the designed codes using numerical simulations and discuss their advantages.

关键词： Distributed rateless codes genetic algorithms optimization unequal error protection erasure channels

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Fractional adaptive control for an automatic voltage regulator

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ISA TRANSACTIONS 2013年第6期52卷 807-815页

作者： Aguila-Camacho, Norelys Duarte-Mermoud, Manuel A. Univ Chile Dept Elect Engn Santiago Chile Adv Min Technol Ctr Santiago Chile

This paper presents the application of a direct Fractional Order Model Reference Adaptive Controller (FOMRAC) to an Automatic Voltage Regulator (AVR). A direct FOMRAC is a direct Model Reference Adaptive Control (MRAC), whose controller parameters are adjusted using fractional order differential equations. Four realizations of the FOMRAC were designed in this work, each one considering different orders for the plant model. The design procedure consisted of determining the optimal values of the fractional order and the adaptive gains for each adaptive law, using genetic algorithm optimization. Comparisons were made among the four FOMRAC designs, a fractional order PID (FOPID), a classical PID, and four Integer Order Model Reference Adaptive Controllers (IOMRAC), showing that the FOMRAC can improve the controlled system behavior and its robustness with respect to model uncertainties. Finally, some performance indices are presented here for the controlled schemes, in order to show the advantages and disadvantages of the FOMRAC. (C) 2013 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Fractional order adaptive control Model reference adaptive control Automatic voltage regulator genetic algorithms optimization

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BIM-based approach for optimizing life cycle costs of sustainable buildings

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JOURNAL OF CLEANER PRODUCTION 2018年 188卷 217-226页

作者： Marzouk, Mohamed Azab, Shimaa Metawie, Mahmoud Cairo Univ Fac Engn Struct Engn Dept Giza Egypt Inst Natl Planning Environm Planning & Dev Ctr Environm Planning Dept Cairo 11765 Egypt Cairo Univ Fac Engn Struct Engn Dept Giza 12613 Egypt

This paper proposes a framework that integrates Building Information Modeling (BIM) with genetic Algorithm optimization and Monte-Carlo Simulation. The developed framework helps in implementing a stochastic Life-Cycle Cost (LCC) model for building to select the optimum building materials alternatives and discover the most influential building system in each cost element starting from initial cost to end on life cost. genetic algorithms optimization technique is utilized to select the optimum alternatives of building systems taking in consideration sustainability aspects. The Monte-Carlo Simulation model is used as a fitness function for the optimization model. The environmental aspect of building is achieved by considering the maximum number of points that can be awarded under the Leadership in Energy and Environmental Design (LEED) rating system. Sensitivity analysis is performed on the optimum solutions that are chosen by optimization a model to examine the effect of different building systems on LCC and its components through computing the rank order of building systems and the target output. A case study is presented to demonstrate the practical features of the proposed framework. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Green-building materials Life-cycle cost (LCC) Monte-carlo simulation genetic algorithms optimization Building information modeling (BIM)

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Sub optimal scheduling in a grid using genetic algorithms

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PARALLEL COMPUTING 2004年第5-6期30卷 553-565页

作者： Di Martino, V Mililotti, M CASPUR Interuniv Supercomp Consortium I-00185 Rome Italy

The computing GRID infrastructure could benefit of techniques that can improve the overall throughput of the system. It is possible that job submission will include different ontology in resource requests due to the generality of the GRID infrastructure. Such flexible resource request could offer the opportunity to optimize several parameters, from network load to job costs in relation to due time, more generally the quality of services. We present the result of the simulation of GRID jobs allocation. The search strategy for this input case does not converge to the optimal case inside the limited number of trial performed, in contrast with previous work on up to 24 jobs [Scheduling in a Grid Computing Environment using genetic algorithms, in: Proceedings of the International Parallel and Distributed Processing Symposium: IPDPS 2002 Workshops]. The benefits of the usage of the genetic algorithms to improve the quality of the scheduling is discussed. The simulation has been obtained using an environment GGAS suitable to study the scheduling of jobs in a distributed group of parallel machines. The modular structure of GGAS allows to expand its functionalities to include other first level schedule policy with respect to the FCFS that is considered. The result of this paper suggests the usage of local search strategy to improve the convergence when the number of jobs to be considered is as big as in real world operation. (C) 2004 Published by Elsevier B.V.

关键词： GRID computing scheduling genetic algorithms optimization

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Optimal Design of Circular Antenna Array Using genetic algorithms 9

Optimal Design of Circular Antenna Array Using Genetic Algor...

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9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Enache, Florin Departcanu, Daniel Popescu, Florin Mil Tech Acad Commun & Mil Elect Syst Dept Bucharest Romania

ISBN: (纸本)9781509064588

This paper presents the design of circular antenna array with isotropic radiators by optimization using genetic algorithms. The problem consists in the obtaining of an imposed radiation pattern starting from a uniform circular antenna array. The goal is achieved using non-uniform distances between the circles which the radiators are disposed on and a non-uniform distribution of currents through the radiators on the successive circles. The work results and the paper conclusions reveal advantage of our approach versus the uniform circular antenna array. Also an important result is the reduction of the number of variables in the fitness function, which creates good premises for the implementation of real-time algorithms useful in complex/dynamic adaptive arrays.

关键词： circular antenna array non-uniform currents distribution genetic algorithms optimization sidelobe level beamwidth

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Ensemble feature selection for diagnosing multiple faults in rotating machinery

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Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2007年第1期221卷 29-41页

作者： Zio, E. Baraldi, P. Gola, G. Department of Nuclear Engineering Polytechnic of Milan Milan Italy

Recent research in fault classification has shown that one of the benefits of using ensembles of classifiers is that they achieve higher accuracy than single ones. For an ensemble to be effective, it should consist of base classifiers that give diverse predictions. One method for constructing an ensemble is to have the base classifiers work on different feature sets. In the current paper, the problem of selecting the feature sets for the base classifiers is handled by means of genetic algorithms aimed at maximizing the fault classification performance and at minimizing the number of features. A voting technique is then used to combine effectively the outputs of the base classifiers to construct the ensemble output. For verification, some numerical experiments are conducted on multiple fault classification in rotating machinery and the results are compared with those obtained using an optimal single classifier. © 2008, Institution of Mechanical Engineers. All rights reserved.

关键词： ensemble classification fault diagnosis feature selection genetic algorithms optimization multiple faults rotating machinery

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Speed Optimal-setting of Electric Multiple Units Based on Energy-efficient Operation

Speed Optimal-setting of Electric Multiple Units Based on En...

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2015 IEEE Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2015)

作者： ZHANG Qiongjie ZHANG Tiezhu School of Vehicle Engineering Zhengzhou Railway Vocational and Technical College

ISBN: (纸本)9781479919819

The operation process of electric multiple units(EMUs) is characterized by complex running environment,and frequent change of operating *** is difficult to accomplish energy-efficient operation using the given V-S curve(velocity versus position curve).And the alternation of coasting points will have different running time and energy consumption,then appropriate coasting scheme is an effective energy-saving *** on the traction energy consumption and running time model,we exploit a data-driven RBF(radial basis function)neural network to build a model that connects the coasting points and their corresponding energy consumption and running ***,we adopt GA(genetic algorithms) to optimize the outputs of the RBF,and the speed optimal-setting profile satisfying punctual,energy-efficient operation is *** results show the effectiveness of this method.

关键词： electric multiple units energy consumption coasting points RBF genetic algorithms optimization

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LaPS: LiDAR-assisted Placement of Wireless Sensor Networks in Forests

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ACM TRANSACTIONS ON SENSOR NETWORKS 2019年第2期15卷 17-17页

作者： Demetri, Silvia Picco, Gian Pietro Bruzzone, Lorenzo Univ Trento Dept Informat Engn & Comp Sci DISI Via Sommarive 9 I-38123 Trento Italy

The deployment of a wireless sensor network (WSN) is crucial to its reliability and performance. Yet, node placement is typically determined in-field via effort-demanding trial-and-error procedures, because existing approaches over-simplify the radio environment;this especially holds for forests, the focus of this article, where trees greatly affect communication. We present LaPS (LiDAR-assisted Placement for wireless Sensor networks), an approach exploiting remote sensing to identify the best node placement automatically and prior to deployment. Airborne Light Detection and Ranging (LiDAR) data acquired for the target forest are automatically processed to estimate its properties (e.g., tree position and diameter) that, once incorporated into a specialized path loss model, enable per-link estimates of the radio signal attenuation induced by trees. Finally, a genetic algorithm explores placement options by evolving toward a (sub-)optimal solution while satisfying the user's spatial and network requirements, whose formulation is very flexible and broadly applicable. Our experiments, focused on a real forest, confirm that LaPS yields topologies of significantly higher quality w.r.t. approaches using a regular placement or a standard path loss model. Further, the ability to quickly explore the impact that changes in user requirements have on topology is invaluable to improve the operation of WSNs and reduce the effort of their in-field deployment.

关键词： Wireless sensor networks low-power wireless communication remote sensing LiDAR genetic algorithms optimization

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