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A HYBRID IWO/PSO ALGORITHM FOR FAST AND GLOBAL OPTIMIZATION Hossein Hajimirsadeghi, Student Member, IEEE, Caro Lucas, Member, IEEE Abstract: This paper presents a hybrid optimization algorithm which originates from Invas
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Document Date: 2009-03-31 08:04:46


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Piscataway / Tehran / Hyderabad / Indianapolis / Trieste / Changsha / Perth / New York / Beijing / London / San Diego / /

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Neural Networks / Oxford University Press / Electronic Research Laboratory / N. Quijano A. E. / Manufacturing Research Corporation / /

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Australia / United Kingdom / China / India / Italy / Iran / United States / /

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Garyounis University / University of Tehran / University of California / Harbin Institute of Electrical Technology / Institute of Applied Mathematics Chinese Academy / Electronic Research Laboratory / International Center / University of California at Los Angeles / University of Toronto / /

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optimization meta-algorithm / online processing / local search / fabrication devices / evolutionary-based optimization algorithms / real-time processing / control using genetic algorithms / invasive weed optimization algorithm / hybrid algorithm / search area / numerical optimization algorithm / search space / biological computing / uncertain systems / multiagent systems / web content organizing / biological systems / knowledge management / potential solutions / data mining / collective systems / evolutionary algorithms / exploration operator / electricity / recent evolutionary algorithms / multi-agent systems / inspired algorithm / control systems / hybrid optimization algorithm / numerical stochastic optimization algorithm / /

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University of Tehran / PSO IWO / Swarm Intelligence / Engineering Faculty / Applied Mathematics Chinese Academy of Sciences / Plant Intelligence / Garyounis University / Institute for Studies / Oxford University / University of California at Los Angeles / Computational intelligence PC / Particle Swarm Optimization PSO / International Center for Genetic Engineering and Biotechnology / University of California / Berkeley / IEEE / Computational Intelligence / Congress / Harbin Institute of Electrical Technology / Department of Electrical and Computer Engineering / Center of Excellence for Control and Intelligent Processing / International Center for Theoretical Physics / Oxford Univ. / School of Cognitive Science / SIS / SFL IWO / University of Toronto / MAs SFL / /

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Taylor / Mehrabian / Cybernet / Caro Lucas / Hossein Hajimirsadeghi / Honey Bee Swarms / Francis / /

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founder / Research Associate / Professor / Managing Editor / Professor / and the Director / representative / Chairman of the ECE Department / Associate Professor / adaptive controller / Editor / Researcher / same controller / Research Assistant / Chairman / Director / Assistant Professor / controller / the chairman of several International Conferences / /

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California / Ontario / New Jersey / South Dakota / /

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Annals of Botany / Journal of The Royal Society Interface / Site Selection / /

Technology

control using genetic algorithms / SIMULATION STUDIES A. Comparing IWO/PSO withh Five Recent Evolutionary Algorithms / Genetic Engineering / above-mentioned algorithms / IWO/PSO algorithm / ecologically inspired algorithm / two algorithms / recent evolutionary algorithms / neural network / hybrid algorithm / extended algorithm / Bacterial foraging Algorithm / hybrid IWO/PSO algorithm / numerical optimization algorithm / recombination / business intelligence / Invasive Weed Optimization algorithm / three algorithms / Bioinformatics / five evolutionary algorithms / Biotechnology / machine learning / numerical stochastic optimization algorithm / optimization meta-algorithm / hybrid optimization algorithm / knowledge management / proposed algorithm / five evolutionary-based optimization algorithms / Swarm intelligence clustering algorithm / data mining / simulation / IWO algorithm / /

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