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Optimal design of frequency selective surfaces with fractal motifs

IET MICROWAVES ANTENNAS & PROPAGATION 2014年第9期8卷 627-631页

作者： da Silva, Marcelo Ribeiro Nobrega, Clarissa de Lucena Silva, Paulo Henrique da F. D'Assuncao, Adaildo Gomes Univ Fed Rio Grande do Norte UFRN CT DCO BR-59078970 Natal RN Brazil Fed Inst Educ Sci & Technol Paraiba IFPB BR-58015430 Joao Pessoa Paraiba Brazil

An alternative electromagnetic (EM) optimisation technique for the optimal design of frequency selective surfaces (FSSs) with fractal motifs is described. Based on computational intelligence tools, the proposed technique overcomes the high computational cost associated with FSS parametric full-wave analysis. In an application example, a fast and accurate multilayer perceptrons model of a FSS band-stop spatial filter with a Vicsek fractal motif is developed. This neural network model is used for repetitive cost function computations in population-based search algorithm simulations. A bees algorithm, continuous genetic algorithm and particle swarm optimisation are used for FSS optimisation with specific resonant frequency and bandwidth. The performance of these algorithms is compared in terms of numerical convergence. Consistent results are presented for a second-pass of designed FSS prototype with Vicsek fractal elements.

关键词： electrical engineering computing frequency selective surfaces genetic algorithms multilayer perceptrons particle swarm optimisation search problems spatial filters frequency selective surfaces optimal design alternative electromagnetic optimisation technique computational intelligence tools FSS parametric full-wave analysis multilayer perceptrons FSS band-stop spatial fllter Vicsek fractal motif neural network model repetitive cost function computations population-based search algorithm simulations particle swarm optimisation continuous genetic algorithm bees algorithm

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Decision support system based on genetic algorithm and multi-criteria satisfaction analysis (MUSA) method for measuring job satisfaction

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ANNALS OF OPERATIONS RESEARCH 2017年第1期256卷 3-20页

作者： Aouadni, Ismahene Rebai, Abdelwaheb Univ Sfax MODILS FSEG Sfax 3018 Tunisia

In this paper, we propose a Decision Support System based on theMUSA method and the continuous genetic algorithm in order to measure job satisfaction. The objective is to help organizations evaluate and measure their employees' satisfaction. Our study is composed of two parts. Firstly, we propose to combine continuous genetic algorithm and the MUSA method in order to obtain a robust solution of good performance. The aim of the development of this algorithm is to verify its efficiency regarding the classical MUSA algorithm. Therefore, we compare the result of continuous genetic algorithm with that of the MUSA algorithm. In the second part, we present our Decision Support Systems called "GMUSA System", it was developed in order to facilitate the applications and the use of the GMUSA tools and overcome the increasing complexity of managerial contexts. Our new system "GMUSA" is applied at the University of Sfax to measure teachers' job satisfaction.

关键词： continuous genetic algorithm Decision support system Job satisfaction MUSA method

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MPP detection of a partially shaded PV array by continuous GA and hybrid PSO

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AIN SHAMS ENGINEERING JOURNAL 2015年第2期6卷 471-479页

作者： Shankar, G. Mukherjee, V. Indian Sch Mines Dept Elect Engn Dhanbad 826004 Jharkhand India

Power versus voltage curve of a uniformly shaded photovoltaic (PV) array has only one maximum power point (MPP). However, when the array is partially shaded, there may be several local MPPs and there exists only one global MPP. The commonly employed perturb and observe (P&O) algorithm works well in detecting the MPP of a PV array when the array is uniformly shaded. However, when the array is partially shaded, the operating point based on P&O method may converge to a local MPP of the array rather than the global one resulting in its under-utilization. In this paper, continuous genetic algorithm (CGA) and hybrid particle swarm optimization (HPSO) algorithm are adopted in detecting the global MPP of a partially shaded PV array. It is observed that the performance of the HPSO is better than either P&O method or the CGA in finding the global maxima of a partially shaded PV array. (C) 2014 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/3.0/).

关键词： continuous genetic algorithm Hybrid particle swarm optimization Maximum power point tracker Perturb and observe algorithm Photovoltaic array

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Optimization of controlled release nanoparticle formulation of verapamil hydrochloride using artificial neural networks with genetic algorithm and response surface methodology

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EUROPEAN JOURNAL OF PHARMACEUTICS AND BIOPHARMACEUTICS 2015年第1期94卷 170-179页

作者： Li, Yongqiang Abbaspour, Mohammadreza R. Grootendorst, Paul V. Rauth, Andrew M. Wu, Xiao Yu Univ Toronto Adv Pharmaceut & Drug Delivery Lab Leslie Dan Fac Pharm Toronto ON M5S 3M2 Canada Mashhad Univ Med Sci Sch Pharm Targeted Drug Delivery Res Ctr Mashhad Iran McMaster Univ Dept Econ Hamilton ON L8S 4M4 Canada Univ Toronto Fac Med Toronto ON M5G 2M9 Canada

This study was performed to optimize the formulation of polymer-lipid hybrid nanoparticles (PLN) for the delivery of an ionic water-soluble drug, verapamil hydrochloride (VRP) and to investigate the roles of formulation factors. Modeling and optimization were conducted based on a spherical central composite design. Three formulation factors, i.e., weight ratio of drug to lipid (X-1), and concentrations of Tween 80 (X-2) and Pluronic F68 (X-3), were chosen as independent variables. Drug loading efficiency (Y-1) and mean particle size (Y-2) of PLN were selected as dependent variables. The predictive performance of artificial neural networks (ANN) and the response surface methodology (RSM) were compared. As ANN was found to exhibit better recognition and generalization capability over RSM, multi-objective optimization of PLN was then conducted based upon the validated ANN models and continuous genetic algorithms (GA). The optimal PLN possess a high drug loading efficiency (92.4%, w/w) and a small mean particle size (similar to 100 nm). The predicted response variables matched well with the observed results. The three formulation factors exhibited different effects on the properties of PLN. ANN in coordination with continuous GA represent an effective and efficient approach to optimize the PLN formulation of VRP with desired properties. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Optimization Artificial neural networks continuous genetic algorithm Response surface methodology Polymer-lipid hybrid nanoparticles Verapamil hydrochloride

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Optimal Design of Transmission Shafts Using a Vortex Search algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第4期46卷 3293-3300页

作者： Rodriguez-Cabal, M. A. Betancur-Gomez, J. D. Grisales-Norena, L. F. Montoya, Oscar Danilo Hincapie, Diego Inst Tecnol Metropolitano Calle 54A 30-01 Medellin Colombia Inst Univ Pascual Bravo Calle 73 73A-226 Medellin Colombia Univ Dist Francisco Jose de Caldas Carrera 7 40B-53 Bogota 11021 Colombia Univ Tecnol Bolivar Km 1 Via Turbaco Cartagena 131001 Colombia Inst Tecnol Metropolitano Calle 73 76A-354 Medellin Colombia

This paper analyzes the problem of optimally sizing a transmission shaft via the vortex search algorithm (VSA) optimizer. The objective function was to minimize the shaft weight through an adequate selection of the diameters of each section of the device, and the constraints were the physical conditions that should be met to design safe, fatigue-proof shafts. The solution and the mathematical model were validated using Autodesk Inventor. In addition, the performance of the VSA was compared to that of the continuous genetic algorithm . The numerical results show that the programmed model has the physical and methodological characteristics needed to produce a better output than conventional design techniques. Therefore, this model can be a powerful tool to solve nonlinear non-convex optimization problems such as the case investigated here.

关键词： Mechanical analysis Machine elements design Weight shaft optimization Vortex search algorithm continuous genetic algorithm Nonlinear non-convex optimization

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A note on a reliability redundancy allocation problem using a tuned parameter genetic algorithm

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OPSEARCH 2016年第2期53卷 426-442页

作者： Chambari, Amirhossain Sadeghi, Javad Bakhtiari, Fakhri Jahangard, Reza Islamic Azad Univ Qazvin Branch Young Researchers & Elite Club Qazvin Iran SUNY Binghamton Syst Sci & Ind Engn Binghamton NY 13902 USA Payame Noor Univ West Tehran Branch Dept Business Adm Fac Management Tehran Iran Islamic Azad Univ Dept Business Adm Persian Gulf Int Branch Khorramshahr Iran

This paper presents an improved continuous genetic algorithm (CGA) to optimize the reliability redundancy allocation problem (RRAP) which determines the best redundancy strategies, the number of components, and levels of each subsystem to maximize the system reliability. In this system, both active and cold-standby redundancies can be chosen for individual subsystems. The RRAP belongs to NP-hard problems in the computational complexity theory that is the main reason for employing CGA to solve it. In addition, the response surface methodology (RSM) is used to increase the performance of CGA considering the design of experiments. This algorithm employs a new chromosome so that frees offspring to repair during the evolution process. Considering several numerical examples, the proposed algorithm presents better solutions than the previous studies based on the system reliability. Finally, the conclusion and future research are considered.

关键词： Redundancy allocation problem Series-parallel systems continuous genetic algorithm Parameter tuning Response surface methodology (RSM)

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Efficient Traffic Signal Control for Multi-phase Intersections

Efficient Traffic Signal Control for Multi-phase Intersectio...

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International Conference on Artificial Intelligence, Robotics and Control (AIRC)

作者： Bouriachi, F. Tolbi, B. Saidi, K. Kloucha, O. Kada Univ Sci & Technol Houari Boumediene Dept Instrumentat & Automat Lab Automat & Informat Guelma Algiers Algeria Univ Djillali Liabes Sidi Bel Abbes Lab Automat & Informat Guelma Dept Automat Fac Genie Elect Sidi Bel Abbes Algeria Univ Tlemcen LAT Tilimsen 13000 Algeria Univ Djillali Liabes Sidi Bel Abbes Elect Engn Fac Sidi Bel Abbes Algeria

ISBN: (纸本)9781450376716

In this paper, we present an efficient traffic signal control strategy for multi-phase intersections. This strategy is used to determine the signal timing for fully actuated traffic control, keeping effective phases times on each cycle. The obtund values can be used to estimate the delay of vehicles. We show that the proposed strategy can be formulated as a nonlinear programming problem, solved by continuous genetic algorithm. We illustrate the proposed control strategy with several traffic scenarios based on real data collected from an existing complex multi-phase intersection in Algiers, Algeria. The experiment results show that the traffic signal plan obtained by the proposed control approach outperforms those currently used traffic signal control strategies under various demand scenarios.

关键词： Multi-phase road traffic continuous genetic algorithm optimal signal timing

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Efficient Traffic Signal Control for Multi-phase Intersections 19

Efficient Traffic Signal Control for Multi-phase Intersectio...

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Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control

作者： F. Bouriachi B. Tolbi K. Saidi O. Kada Kloucha Laboratoire d'Automatique et Informatique de Guelma Département Instrumentation et Automatique Université des sciences et de la technologie Houari Boumediene Alger Laboratoire d'Automatique et Informatique de Guelma Département d'automatique Djillali Liabes University Sidi Bel Abbes ALGERIA LAT Laboratoire d'Automatique de Tlemcen Université de Tlemcen Tlemcen ALGERIA Electrical Engineering faculty Djillali Liabes University Sidi Bel Abbes ALGERIA

ISBN: (纸本)9781450376716

关键词： Multi-phase optimal signal timing road traffic continuous genetic algorithm

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