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Referenzsammlung.bib
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% würde ich gerne lesen
@article {springerlink:10.1007/s11721-010-0043-7,
author = {Torres, Claudio and Rossi, Louis and Keffer, Jeremy and Li, Ke and Shen, Chien-Chung},
affiliation = {University of Delaware Department of Mathematical Sciences Newark DE 19716 USA},
title = {Modeling, analysis and simulation of ant-based network routing protocols},
journal = {Swarm Intelligence},
publisher = {Springer New York},
issn = {1935-3812},
keyword = {Technik},
pages = {221-244},
volume = {4},
issue = {3},
url = {http://dx.doi.org/10.1007/s11721-010-0043-7},
note = {10.1007/s11721-010-0043-7},
year = {2010}
}
% References of face detection
@article{mobahi2006swarm,
title={Swarm contours: A fast self-organization approach for snake initialization},
author={Mobahi, H. and Ahmadabadi, M.N. and Araabi, B.N.},
journal={Complexity},
volume={12},
number={1},
pages={41--52},
year={2006},
publisher={Wiley Online Library}
}
% überblick
@article{kennedy2006swarm,
title={Swarm intelligence},
author={Kennedy, J.},
journal={Handbook of Nature-Inspired and Innovative Computing},
pages={187--219},
year={2006},
publisher={Springer}
}
% Swarmintelligence
@PHDTHESIS{Diplom.Thiem,
author = {Stefanie Thiem},
title = {Swarmintelligence - Simulation, Optimization and Comparative Analysis},
school = {Chemnitz University of Technology},
month = {January},
year = {2008},
type = {Diplom Thesis}
}
% from springer journal "swarm intelligence"
% wahrscheinlichkeitsüberprüfung
@article {springerlink:10.1007/s11721-009-0037-5,
author = {El-Abd, Mohammed and Kamel, Mohamed},
affiliation = {ECE Department, University of Waterloo, 200 University Av. W., Waterloo, Ontario N2L3G1, Canada},
title = {A cooperative particle swarm optimizer with migration of heterogeneous probabilistic models},
journal = {Swarm Intelligence},
publisher = {Springer New York},
issn = {1935-3812},
keyword = {Technik},
pages = {57-89},
volume = {4},
issue = {1},
url = {http://dx.doi.org/10.1007/s11721-009-0037-5},
note = {10.1007/s11721-009-0037-5},
year = {2010}
}
$ technischer Ansatz
@article {springerlink:10.1007/s11721-011-0053-0,
author = {Ducatelle, Frederick and Di Caro, Gianni and Pinciroli, Carlo and Gambardella, Luca},
affiliation = {“Dalle Molle” Institute for Artificial Intelligence Studies (IDSIA), Galleria 2, 6928 Manno, Switzerland},
title = {Self-organized cooperation between robotic swarms},
journal = {Swarm Intelligence},
publisher = {Springer New York},
issn = {1935-3812},
keyword = {Technik},
pages = {73-96},
volume = {5},
issue = {2},
url = {http://dx.doi.org/10.1007/s11721-011-0053-0},
note = {10.1007/s11721-011-0053-0},
year = {2011}
}
% Prozessorientirtes Optimieren
@article {springerlink:10.1007/s11721-011-0061-0,
author = {Pellegrini, Paola and Stützle, Thomas and Birattari, Mauro},
affiliation = {IRIDIA, CoDE, Université Libre de Bruxelles (ULB), Brussels, Belgium},
title = {A critical analysis of parameter adaptation in ant colony optimization},
journal = {Swarm Intelligence},
publisher = {Springer New York},
issn = {1935-3812},
keyword = {Technik},
pages = {1-26},
url = {http://dx.doi.org/10.1007/s11721-011-0061-0},
note = {10.1007/s11721-011-0061-0},
}
% general Overview
@INPROCEEDINGS{5623005,
author={Yan-fei Zhu and Xiong-min Tang},
booktitle={Computer Application and System Modeling (ICCASM), 2010 International Conference on},
title={Overview of swarm intelligence},
year={2010},
month={oct.},
volume={9},
number={},
pages={V9-400 -V9-403},
keywords={Kazadi two-step process;artificial literature;biological basis;biologists;naturalists;operational principle;robot system;swarm engineering;swarm intelligence;artificial intelligence;particle swarm optimisation;},
doi={10.1109/ICCASM.2010.5623005},
ISSN={},}
% physical application
@INPROCEEDINGS{5760115,
author={Affijulla, S. and Chauhan, S.},
booktitle={Emerging Trends in Electrical and Computer Technology (ICETECT), 2011 International Conference on},
title={Swarm intelligence solution to large scale thermal power plant Load Dispatch},
year={2011},
month={march},
volume={},
number={},
pages={196 -199},
keywords={artificial intelligence;economic load dispatch;energy management system;evolutionary computation;evolutionary programming;generating units;genetic algorithm;nonconvex objective functions;nondifferential objective functions;optimal real power settings;particle swarm optimization;power generation;swarm intelligence solution;thermal power plant;artificial intelligence;energy management systems;evolutionary computation;particle swarm optimisation;power generation dispatch;thermal power stations;},
doi={10.1109/ICETECT.2011.5760115},
ISSN={},}
@book{engelbrecht2005fundamentals,
title={Fundamentals of computational swarm intelligence},
author={Engelbrecht, A.P.},
volume={1},
year={2005},
publisher={Wiley Chichester,, UK}
}
% collective intelligence; 672 zitationen; grundlagen werk
@article{DBLP:journals/corr/cs-LG-9908014,
author = {David Wolpert and
Kagan Tumer},
title = {An Introduction to Collective Intelligence},
journal = {CoRR},
volume = {cs.LG/9908014},
year = {1999},
ee = {http://arxiv.org/abs/cs.LG/9908014},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
% collective intelligence particle physics
@article{lykourentzou2011collective,
title={Collective Intelligence Systems: Classification and Modeling},
author={Lykourentzou, I. and Vergados, D.J. and Kapetanios, E. and Loumos, V.},
journal={Journal of Emerging Technologies in Web Intelligence},
volume={3},
number={3},
pages={217--226},
year={2011}
}
% Datenbanksuche, Collective Intelligence
@inproceedings{zhu2010overview,
title={Overview of swarm intelligence},
author={Zhu, Y. and Tang, X.},
booktitle={Computer Application and System Modeling (ICCASM), 2010 International Conference on},
volume={9},
pages={V9--400},
year={2010},
organization={IEEE}
}
% Datenbanksuche, Swarm Intelligence
@inproceedings{sun2004global,
title={A global search strategy of quantum-behaved particle swarm optimization},
author={Sun, J. and Xu, W. and Feng, B.},
booktitle={Cybernetics and Intelligent Systems, 2004 IEEE Conference on},
volume={1},
pages={111--116},
year={2004},
organization={IEEE}
}
% quantum-behave particle swarm optimization
@article{du2010improved,
title={Improved Quantum Particle Swarm Optimization by Bloch Sphere},
author={Du, Y. and Duan, H. and Liao, R. and Li, X.},
journal={Advances in Swarm Intelligence},
pages={135--143},
year={2010},
publisher={Springer}
}
% swarm intelligence, quantum
@inproceedings{affijulla2011swarm,
title={Swarm intelligence solution to large scale thermal power plant Load Dispatch},
author={Affijulla, S. and Chauhan, S.},
booktitle={Emerging Trends in Electrical and Computer Technology (ICETECT), 2011 International Conference on},
pages={196--199},
year={2011},
organization={IEEE}
}
% swarm intelligence, power plant; 25 zitationen
@article{coelho2008solving,
title={Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches},
author={Coelho, L.S. and Lee, C.S.},
journal={International Journal of Electrical Power \& Energy Systems},
volume={30},
number={5},
pages={297--307},
year={2008},
publisher={Elsevier}
}
% swarm intelligence, load dispatch; 62 zitationen
% kommt in frage zum lesen
@inproceedings{el2001swarm,
title={Swarm intelligence for hybrid cost dispatch problem},
author={El-Gallad, AI and El-Hawary, M. and Sallam, AA and Kalas, A.},
booktitle={Electrical and Computer Engineering, 2001. Canadian Conference on},
volume={2},
pages={753--757},
year={2001},
organization={IEEE}
}
% swarm intelligence, load dispatch; 22 zitationen
%w\"urde ich gern lesen
@article{selvakumar2007new,
title={A new particle swarm optimization solution to nonconvex economic dispatch problems},
author={Selvakumar, A.I. and Thanushkodi, K.},
journal={Power Systems, IEEE Transactions on},
volume={22},
number={1},
pages={42--51},
year={2007},
publisher={IEEE}
}
% swarm intelligence, load dispatch; 167 zitationen
% This file was created with JabRef 2.7b.
% Encoding: UTF-8
@ARTICLE{bib:pso_pid_gaing,
author = {Zwe-Lee Gaing},
title = {A particle swarm optimization approach for optimum design of PID
controller in AVR system},
journal = {Energy Conversion, IEEE Transactions on},
year = {2004},
volume = {19},
pages = {384 - 391},
number = {2},
month = {june},
doi = {10.1109/TEC.2003.821821},
}
abstract = { In this paper, a novel design method for determining the optimal
proportional-integral-derivative (PID) controller parameters of an
AVR system using the particle swarm optimization (PSO) algorithm
is presented. This paper demonstrated in detail how to employ the
PSO method to search efficiently the optimal PID controller parameters
of an AVR system. The proposed approach had superior features, including
easy implementation, stable convergence characteristic, and good
computational efficiency. Fast tuning of optimum PID controller parameters
yields high-quality solution. In order to assist estimating the performance
of the proposed PSO-PID controller, a new time-domain performance
criterion function was also defined. Compared with the genetic algorithm
(GA), the proposed method was indeed more efficient and robust in
improving the step response of an AVR system.},
doi = {10.1109/TEC.2003.821821},
issn = {0885-8969},
keywords = { AVR system; PID controller parameter; genetic algorithm; particle
swarm optimisation algorithm; optimisation; three-term control; voltage
regulators;},
owner = {cornelius},
timestamp = {2011.11.26}
}
@INPROCEEDINGS{bib:pso_kennedy,
author = {Kennedy, J. and Eberhart, R.},
title = {Particle swarm optimization},
booktitle = {Neural Networks, 1995. Proceedings., IEEE International Conference
on},
year = {1995},
volume = {4},
pages = {1942 -1948 vol.4},
month = {nov/dec},
note = {Zitiert durch: 15780
Begruender von PSO},
abstract = {A concept for the optimization of nonlinear functions using particle
swarm methodology is introduced. The evolution of several paradigms
is outlined, and an implementation of one of the paradigms is discussed.
Benchmark testing of the paradigm is described, and applications,
including nonlinear function optimization and neural network training,
are proposed. The relationships between particle swarm optimization
and both artificial life and genetic algorithms are described},
doi = {10.1109/ICNN.1995.488968},
keywords = {artificial life;evolution;genetic algorithms;multidimensional search;neural
network;nonlinear functions;optimization;particle swarm;simulation;social
metaphor;artificial intelligence;genetic algorithms;neural nets;search
problems;simulation;},
owner = {cornelius},
timestamp = {2011.11.26}
}
@ARTICLE{bib:pso_wsn_kulkarni,
author = {Kulkarni, R.V. and Venayagamoorthy, G.K.},
title = {Particle Swarm Optimization in Wireless-Sensor Networks: A Brief
Survey},
journal = {Systems, Man, and Cybernetics, Part C: Applications and Reviews,
IEEE Transactions on},
year = {2011},
volume = {41},
pages = {262 -267},
number = {2},
month = {march},
abstract = {Wireless-sensor networks (WSNs) are networks of autonomous nodes used
for monitoring an environment. Developers of WSNs face challenges
that arise from communication link failures, memory and computational
constraints, and limited energy. Many issues in WSNs are formulated
as multidimensional optimization problems, and approached through
bioinspired techniques. Particle swarm optimization (PSO) is a simple,
effective, and computationally efficient optimization algorithm.
It has been applied to address WSN issues such as optimal deployment,
node localization, clustering, and data aggregation. This paper outlines
issues in WSNs, introduces PSO, and discusses its suitability for
WSN applications. It also presents a brief survey of how PSO is tailored
to address these issues.},
doi = {10.1109/TSMCC.2010.2054080},
issn = {1094-6977},
keywords = {clustering issue;data aggregation issue;node localization issue;optimal
deployment issue;particle swarm optimization;wireless sensor networks;particle
swarm optimisation;wireless sensor networks;},
owner = {cornelius},
timestamp = {2011.11.26}
}
@INPROCEEDINGS{bib:pso_energy_wsn_latiff,
author = {Latiff, N.M.A. and Tsimenidis, C.C. and Sharif, B.S.},
title = {Energy-Aware Clustering for Wireless Sensor Networks using Particle
Swarm Optimization},
booktitle = {Personal, Indoor and Mobile Radio Communications, 2007. PIMRC 2007.
IEEE 18th International Symposium on},
year = {2007},
pages = {1 -5},
month = {sept.},
abstract = {Wireless sensor networks (WSNs) are mainly characterized by their
limited and non-replenishable energy supply. Hence, the need for
energy efficient infrastructure is becoming increasingly more important
since it impacts upon the network operational lifetime. Sensor node
clustering is one of the techniques that can expand the lifespan
of the whole network through data aggregation at the cluster head.
In this paper, we present an energy-aware clustering for wireless
sensor networks using particle swarm optimization (PSO) algorithm
which is implemented at the base station. We define a new cost function,
with the objective of simultaneously minimizing the intra-cluster
distance and optimizing the energy consumption of the network. The
performance of our protocol is compared with the well known cluster-based
protocol developed for WSNs, LEACH (low-energy adaptive clustering
hierarchy) and LEACH-C, the later being an improved version of LEACH.
Simulation results demonstrate that our proposed protocol can achieve
better network lifetime and data delivery at the base station over
its comparatives.},
doi = {10.1109/PIMRC.2007.4394521},
keywords = {LEACH;LEACH-C;WSN;cluster-based protocol;cost function;data aggregation;data
delivery;energy consumption;energy efficient infrastructure;energy-aware
clustering;intra-cluster distance;low-energy adaptive clustering
hierarchy;network operational lifetime;nonreplenishable energy supply;particle
swarm optimization;sensor node clustering;wireless sensor networks;particle
swarm optimisation;protocols;wireless sensor networks;},
owner = {cornelius},
timestamp = {2011.11.26}
}
@ARTICLE{bib:pso_electromagn_robinson,
author = {Robinson, J. and Rahmat-Samii, Y.},
title = {Particle swarm optimization in electromagnetics},
journal = {Antennas and Propagation, IEEE Transactions on},
year = {2004},
volume = {52},
pages = {397 - 407},
number = {2},
month = {feb.},
abstract = {The particle swarm optimization (PSO), new to the electromagnetics
community, is a robust stochastic evolutionary computation technique
based on the movement and intelligence of swarms. This paper introduces
a conceptual overview and detailed explanation of the PSO algorithm,
as well as how it can be used for electromagnetic optimizations.
This paper also presents several results illustrating the swarm behavior
in a PSO algorithm developed by the authors at UCLA specifically
for engineering optimizations (UCLA-PSO). Also discussed is recent
progress in the development of the PSO and the special considerations
needed for engineering implementation including suggestions for the
selection of parameter values. Additionally, a study of boundary
conditions is presented indicating the invisible wall technique outperforms
absorbing and reflecting wall techniques. These concepts are then
integrated into a representative example of optimization of a profiled
corrugated horn antenna.},
doi = {10.1109/TAP.2004.823969},
issn = {0018-926X},
keywords = { UCLA; antenna design; corrugated horn antenna; electromagnetics;
genetic algorithm; invisible wall technique; particle swarm optimization;
stochastic evolutionary computation technique; antenna theory; electromagnetism;
evolutionary computation; genetic algorithms; horn antennas;},
owner = {cornelius},
timestamp = {2011.11.26}
}
@ARTICLE{bib:pso_methods_sedighizadeh,
author = {Sedighizadeh, D. and Masehian, E.},
title = {Particle Swarm Optimization Methods, Taxonomy and Applications},
journal = {International Journal of Computer Theory and Engineering},
year = {2009},
volume = {1},
pages = {1793--8201},
number = {5},
owner = {cornelius},
timestamp = {2011.11.26}
}
@INPROCEEDINGS{bib:salesman_wang,
author = {Kang-Ping Wang and Lan Huang and Chun-Guang Zhou and Wei Pang},
title = {Particle swarm optimization for traveling salesman problem},
booktitle = {Machine Learning and Cybernetics, 2003 International Conference on},
year = {2003},
volume = {3},
pages = { 1583 - 1585 Vol.3},
month = {nov.},
abstract = { This paper proposes a new application of particle swarm optimization
for traveling salesman problem. We have developed some special methods
for solving TSP using PSO. We have also proposed the concept of swap
operator and swap sequence, and redefined some operators on the basis
of them, in this way the paper has designed a special PSO. The experiments
show that it can achieve good results.},
doi = {10.1109/ICMLC.2003.1259748},
keywords = { particle swarm optimization; swap operator; swap sequence; traveling
salesman problem; genetic algorithms; travelling salesman problems;},
owner = {cornelius},
timestamp = {2011.11.26}
}
@comment{jabref-meta: selector_publisher:}
@comment{jabref-meta: selector_author:}
@comment{jabref-meta: selector_journal:}
@comment{jabref-meta: selector_keywords:}
%% überblick über verschiedene Algorithmen
%% favorisierte Quelle!
%% http://www.springerlink.com/content/mt70684828340818/
@incollection {springerlink:10.1007/978-3-642-23935-9_3,
author = {Chu, Shu-Chuan and Huang, Hsiang-Cheh and Roddick, John and Pan, Jeng-Shyang},
affiliation = {School of Computer Science, Engineering and Mathematics, Flinders University of South Australia, Australia},
title = {Overview of Algorithms for Swarm Intelligence},
booktitle = {Computational Collective Intelligence. Technologies and Applications},
series = {Lecture Notes in Computer Science},
editor = {Jedrzejowicz, Piotr and Nguyen, Ngoc and Hoang, Kiem},
publisher = {Springer Berlin / Heidelberg},
isbn = {978-3-642-23934-2},
keyword = {Computer Science},
pages = {28-41},
volume = {6922},
url = {http://dx.doi.org/10.1007/978-3-642-23935-9_3},
note = {10.1007/978-3-642-23935-9_3},
year = {2011}
}
%%googlescholar "swarm intelligence"
%%allgemein über das thema
@book{shi2011handbook,
title={Handbook of Swarm Intelligence: Concepts, Principles and Applications},
author={Shi, Y.},
volume={8},
year={2011},
publisher={Springer Verlag}
}
%% google bücher: Künstlichen Intelligenz
%% allgemin, robotik, softwareagent
@book{görz2003handbuch,
title={Handbuch der k{\"u}nstlichen Intelligenz},
author={G{\"o}rz, G.},
year={2003},
publisher={Oldenbourg Wissenschaftsverlag}
}
%%scholar allintitle: algorithms swarm intelligence
%% Algorithms for Data Clustering
%% zitiert durch 42
@incollection {springerlink:10.1007/978-0-387-69935-6_12,
author = {Abraham, Ajith and Das, Swagatam and Roy, Sandip},
affiliation = {Norwegian University of Science and Technology Center of excellence for Quanti¯able Quality of Service (Q2S) Trondheim Norway},
title = {Swarm Intelligence Algorithms for Data Clustering},
booktitle = {Soft Computing for Knowledge Discovery and Data Mining},
editor = {Maimon, Oded and Rokach, Lior},
publisher = {Springer US},
isbn = {978-0-387-69935-6},
keyword = {Computer Science},
pages = {279-313},
url = {http://dx.doi.org/10.1007/978-0-387-69935-6_12},
note = {10.1007/978-0-387-69935-6_12},
year = {2008}
}%Suchen auf Deutsch
% Begriff "Schwarmintelligenz Physik und Informatik" bei google eingeben --> vorallem Bachelorarbeiten und Diplomarbeiten als Ergebnisse
%Begriffe "intitle:Schwarmintelligenz" und "filetype_pdf intitle:Schwarmintelligenz" bei google eingeben
%google Scholar verwenden
%Suchen auf Englisch
%Begriffe "swarm intelligence" oder "collective intelligence" bei Google und Google Scholar eingeben; wenn englischer Name nicht bekannt ist, dann bei Wikipedia schauen und Sprache der Wahl anklicken
%Unterbegriffe "swarm intelligence literature overview" und "swarm intelligence thesis" bei google eingeben
%%Bücher
%zitiert von 219 bei Google Scholar
%alle Angaben beim Eintrag vollständig, bis auf die empfehlenswerte Angabe address
@book{segaran2007programming,
title={Programming collective intelligence: building smart web 2.0 applications},
author={Segaran, T.},
year={2007},
publisher={O'Reilly Media}
}
%zitiert von 672 bei Google Scholar
%alle Angaben beim Eintrag vollständig, bis auf die empfehlenswerte Angabe address
%Anmerkung: eher ein Übersichtsbuch, sollte bei der endgültigen Version der Arbeit vllt. nicht zitiert werden
@book{engelbrecht2005fundamentals,
title={Fundamentals of computational swarm intelligence},
author={Engelbrecht, A.P.},
year={2005},
publisher={Wiley Chichester,, UK}
}
%%Wissenschaftliche Artikel
%zitiert durch 84 bei Google Scholar
%alle Angaben beim Eintrag vollständig, bis auf die empfehlenswerte Angabe issue/number
@article{garnier2007biological,
title={The biological principles of swarm intelligence},
author={Garnier, S. and Gautrais, J. and Theraulaz, G.},
journal={Swarm Intelligence},
volume={1},
pages={3--31},
year={2007}
}
%zitiert von 157 bei Google Scholar
%beim Eintrag nur die notwendigsten Angaben vorhanden; empfehlenswerte Angaben (volume, year und issue/number) fehlen+
%Anmerkung: Artikel allerdings schon älter, sollte bei der endgültigen Version der Arbeit vllt. nicht zitiert werden
@article{wolpert1999introduction,
title={An introduction to collective intelligence},
author={Wolpert, D.H. and Tumer, K.},
journal={Arxiv preprint cs/9908014},
year={1999}
}
%zitiert von 97 bei Google Scholar
%beim Eintrag alle Angaben vorhanden
@article{mondada2005cooperation,
title={The cooperation of swarm-bots: Physical interactions in collective robotics},
author={Mondada, F. and Gambardella, L.M. and Floreano, D. and Nolfi, S. and Deneuborg, J.L. and Dorigo, M.},
journal={Robotics \& Automation Magazine, IEEE},
volume={12},
number={2},
pages={21--28},
year={2005}
}
%zitiert von 125 bei Google Scholar
%beim Eintrag alle Angaben vorhanden
%Anmerkung: Artikel stellt Verbindung zwischen Scharmintelligenz und Physik dar, geht aber eher in Richtung der biologischen Physik; Artikel leider schon von 2000
@article{czirók2000collective,
title={Collective behavior of interacting self-propelled particles},
author={Czir{\'o}k, A. and Vicsek, T.},
journal={Physica A: Statistical Mechanics and its Applications},
volume={281},
number={1},
pages={17--29},
year={2000}
}
%zitiert von 23 bei Google Scholar
%beim Eintrag alle Angaben vorhanden
%Anmerkung: Artikel behandelt eine Erweiterung des Themas "Collective behavior of interacting self-propelled particles"; Artikel ist neuer, von 2008
@article{li2008minimal,
title={Minimal mechanisms for school formation in self-propelled particles},
author={Li, Y.X. and Lukeman, R. and Edelstein-Keshet, L.},
journal={Physica D: Nonlinear Phenomena},
volume={237},
number={5},
pages={699--720},
year={2008}
}
%%Internetartikel
%Internetartikel über Guttenberg und die "Macht der Masse" --> könnte als einführendes Beispiel in der Einleitung verwendet werden; \usepackage{url} im Header laden, damit die URL richtig geladen wird --> stellt dann den Befehl \url{hier Addy eingeben} bei Latex bereit
%alle Angaben beim Eintrag vollständig
@online{GuttenPlagWiki,
author={Matthias Kremp},
title={Im Netz der Plagiate-Jäger},
url={http://www.spiegel.de/netzwelt/web/0,1518,746582,00.html},
year={2011},
urlddate={26.11.2011}
}
% Koriphaen:
% ==============
% Gerardo Beni hat zusammen mit Jin Wang den Begriff "swarm intelligence" im
% Zusammenhang mit Robotik erfunden
@article{beni1993swarm,
title={Swarm intelligence in cellular robotic systems},
author={Beni, G. and Wang, J.},
journal={Robots and Biological Systems: Towards a New Bionics?},
pages={703--712},
year={1993},
publisher={Springer}
}
% James Kennedy
% 15780 citations laut Google Scholar; Suchbegriff: particle swarm optimization
% Das ist wohl DAS paper zu dem Thema!
@inproceedings{Kennedy1995,
author = {Kennedy, J. and Eberhart, R.},
title = {Particle swarm optimization},
booktitle = {Proc. Conf. IEEE Int Neural Networks},
year = {1995},
volume = {4},
pages = {1942--1948},
abstract = {A concept for the <span class='snippet'>optimization</span> of nonlinear
functions using <span class='snippet'>particle</span> <span class='snippet'>swarm</span>
methodology is introduced. The evolution of several paradigms is
outlined, and an implementation of one of the paradigms is discussed.
Benchmark testing of the paradigm is described, and applications,
including nonlinear function <span class='snippet'>optimization</span>
and neural network training, are proposed. The relationships between
<span class='snippet'>particle</span> <span class='snippet'>swarm</span>
<span class='snippet'>optimization</span> and both artificial life
and genetic algorithms are described},
doi = {10.1109/ICNN.1995.488968},
}
% 4160 citations laut Google Scholar; Suchbegriff: swarm intelligence
@article{kennedy2006swarm,
author = {Kennedy, J.},
title = {Swarm intelligence},
journal = {Handbook of Nature-Inspired and Innovative Computing},
year = {2006},
pages = {187--219},
publisher = {Springer}
}
% 283 citations laut ACM; Suchbegriff: swarm intelligence
@book{Eiben:2003:IEC:954563,
title = {Introduction to Evolutionary Computing},
publisher = {SpringerVerlag},
year = {2003},
author = {Eiben, Agoston E. and Smith, J. E.},
isbn = {3540401849}
}
% 605 citations laut ACM; meistzitiertes Buch zum Suchbegriff: swarm intelligence
@book{Bonabeau:1999:SIN:328320,
title = {Swarm intelligence: from natural to artificial systems},
publisher = {Oxford University Press, Inc.},
year = {1999},
author = {Bonabeau, Eric and Dorigo, Marco and Theraulaz, Guy},
address = {New York, NY, USA},
isbn = {0-19-513159-2}
}
% Folgende Referenzen kommen von IEEE und sind maximal 2 Jahre alt, mit
% dem Bezug von Schwarmintelligenz und Physik/Ingenieurswissenschaften
% Suchbegriffe: swarm intelligence physic
% swarm intelligence engineering
@inproceedings{Jiang2011a,
author = {Jiang, F. and Frater, M. and Ling, S. S. H. },
title = {A distributed smart routing scheme for terrestrial sensor networks
with hybrid Neural Rough Sets},
booktitle = {Proc. IEEE Int Fuzzy Systems (FUZZ) Conf},
year = {2011},
pages = {2238--2244},
abstract = {The limited power consumption, as a major constraint, presents challenges
in improving the network throughput for Wireless Sensor Networks
(WSNs). Due to the limited computational power, the applications
of WSNs in Terrestrial Networks require the capability to pre-process
the observation data so as to remove irrelevant features or factors
from multi-dimensional dataset. This paper proposes a intelligent
distributed energy efficient routing algorithm inspired from natural
learning and adaptation process with the aid of hybrid Neural Rough
Sets theory, which is used to efficiently reduce the dimensionality
of input dataset. The algorithmic implementation and experimental
validation are described in this paper. Details of the algorithm
and its testing procedures are presented in comparison with the other
power-aware protocols, e.g., mini-hop. The validation of the proposed
model is carried out via a wireless sensor network test-bed implemented
in Castalia Simulator. The experimental results show the network
performance measurements such as delay, throughput and packet loss
that have been greatly improved as the outcome of applying this integration
with Neural Rough Sets.},
doi = {10.1109/FUZZY.2011.6007725},
}
@article{Kentzoglanakis2011a,
author = {Kentzoglanakis, K. and Poole, M. },
title = {A Swarm Intelligence Framework for Reconstructing Gene Networks:
Searching for Biologically Plausible Architectures},
journal = IEEE_J_CBB,
year = {2011},
number = {99},
abstract = {In this paper, we investigate the problem of reverse-<span class='snippet'>engineering</span>
the topology of gene regulatory networks from temporal gene expression
data. We adopt a computational <span class='snippet'>intelligence</span>
approach comprising <span class='snippet'>swarm</span> <span class='snippet'>intelligence</span>
techniques, namely particle <span class='snippet'>swarm</span> optimization
(PSO) and ant colony optimization (ACO). In addition, the recurrent
neural network (RNN) formalism is employed for modelling the dynamical
behaviour of gene regulatory systems. More specifically, ACO is used
for searching the discrete space of network architectures and PSO
for searching the corresponding continuous space of RNN model parameters.
We propose a novel solution construction process in the context of
ACO for generating biologically plausible candidate architectures.
The objective is to concentrate the search effort into areas of the
structure space that contain architectures which are feasible in
terms of their topological resemblance to real-world networks. The
proposed framework is first applied to an artificial data set with
added noise for reconstructing a subnetwork of the genetic interaction
network of S. cerevisiae (yeast). The framework is also applied to
a real-world data set for reverse-<span class='snippet'>engineering</span>
the SOS response system of the bacterium Escherichia coli. Results
demonstrate the relative advantage of utilizing problem-specific
knowledge regarding biologically plausible structural properties
of gene networks over conducting a problem-agnostic search in the
vast space of network architectures.},
doi = {10.1109/TCBB.2011.87},
}
@inproceedings{Lee2011,
author = {Chang Jun Lee and Prasad, V. and Jong Min Lee},
title = {Robust design of catalysts using stochastic nonlinear optimization},
booktitle = {Proc. Int Advanced Control of Industrial Processes (ADCONIP) Symp},
year = {2011},
pages = {198--203},
abstract = {Computational methods for designing an optimal catalyst have recently
been gaining more popularity in the fields of catalysis and reaction
<span class='snippet'>engineering</span> of energy systems. However,
in general, the problem in these approaches is that uncertainties
present in process models should be handled correctly to achieve
a robust design. To find the optimal design under these uncertainties,
a stochastic <span class='snippet'>optimization</span> method can
be employed. In this work, the optimal properties of a catalyst for
ammonia decomposition to produce hydrogen are investigated, and uncertainties
associated <span class='snippet'>with</span> the reactions and their
parameters are modeled as exogenous uncertain variables which follow
known probability distributions. The goal of this work is to find
the optimal binding energies of the catalyst that maximize conversion
of ammonia in a microreactor. Our stochastic <span class='snippet'>optimization</span>
problem is nonlinear, and involves the expectation operator as well
as integration in the objective function. To tackle this complex
system, the expectation of conversion based on a sample average approximation
(SAA) method is evaluated. However, the exponential increase in the
number of samples to be considered <span class='snippet'>with</span>
the number of uncertain parameters lead to severe computational problems
when using all possible combinations of the uncertain parameters.
To solve this, linearity analysis, together <span class='snippet'>with</span>
partial least squares, is implemented to reduce the number of uncertain
parameters. In the <span class='snippet'>optimization</span> step,
a <span class='snippet'>particle</span> <span class='snippet'>swarm</span>
<span class='snippet'>optimization</span> (PSO) is employed. The
results indicate that the stochastic optimum shows higher conversion
and different optimal binding energies than the deterministic optimum,
and is a more robust solution.},
}
@inproceedings{Rahmat-Samii2011a,
author = {Rahmat-Samii, Y. },
title = {Let swarms of bees optimize your future communication antennas},
booktitle = {Proc. IEEE Radio and Wireless Symp. (RWS)},
year = {2011},
abstract = {<span class='snippet'>Optimization</span> is the process of upgrading
something to perform better. Engineers constantly look for improving
their designs in multi parametric solution space. Imagine that you
will be able to use nature's evolutionary processes to obtain the
best parameters for your designs. This is the subject of this presentation.
The ever increasing advances in computational power have fueled the
temptation of using global <span class='snippet'>optimization</span>
techniques. The well-known brute force design methodologies are systematically
being replaced by the state-of-the-art Evolutionary <span class='snippet'>Optimization</span>
(EO) techniques. In recent years, EO techniques are finding growing
applications to the design of all kind of systems <span class='snippet'>with</span>
increasing complexity. Among various EO's, nature inspired techniques
such as <span class='snippet'>Particle</span> <span class='snippet'>Swarm</span>
<span class='snippet'>Optimization</span> (PSO) have attracted considerable
attention. PSO is a robust stochastic evolutionary computation technique
based on the movement and intelligence of <span class='snippet'>swarms</span>
of bees looking for the most fertile feeding location applying their
cognitive and social knowledge. This presentation will focus on:
(a) an <span class='snippet'>engineering</span> introduction to PSO
by describing in a novel fashion the underlying concepts and recent
advances for those who have used these techniques and for those who
have not had any experiences in these areas, (b) ease of deployment
of PSO on parallel computational platforms, (c) demonstration of
the potential applications of PSO to a variety of communication antenna
designs including advanced cellphone antennas, E-shaped antennas
for multiband and broadband MIMO applications, novel reconfigurable
antennas and array antennas, and (d) assessment of the advantages
and limitations of this technique.},
doi = {10.1109/RWS.2011.5725518},
}
@inproceedings{Lee2011,
author = {Chang Jun Lee and Prasad, V. and Jong Min Lee},
title = {Robust design of catalysts using stochastic nonlinear optimization},
booktitle = {Proc. Int Advanced Control of Industrial Processes (ADCONIP) Symp},
year = {2011},
pages = {198--203},
abstract = {Computational methods for designing an optimal catalyst have recently
been gaining more popularity in the fields of catalysis and reaction
<span class='snippet'>engineering</span> of energy systems. However,
in general, the problem in these approaches is that uncertainties
present in process models should be handled correctly to achieve
a robust design. To find the optimal design under these uncertainties,
a stochastic <span class='snippet'>optimization</span> method can
be employed. In this work, the optimal properties of a catalyst for
ammonia decomposition to produce hydrogen are investigated, and uncertainties
associated <span class='snippet'>with</span> the reactions and their
parameters are modeled as exogenous uncertain variables which follow
known probability distributions. The goal of this work is to find
the optimal binding energies of the catalyst that maximize conversion
of ammonia in a microreactor. Our stochastic <span class='snippet'>optimization</span>
problem is nonlinear, and involves the expectation operator as well
as integration in the objective function. To tackle this complex
system, the expectation of conversion based on a sample average approximation
(SAA) method is evaluated. However, the exponential increase in the
number of samples to be considered <span class='snippet'>with</span>
the number of uncertain parameters lead to severe computational problems
when using all possible combinations of the uncertain parameters.
To solve this, linearity analysis, together <span class='snippet'>with</span>
partial least squares, is implemented to reduce the number of uncertain
parameters. In the <span class='snippet'>optimization</span> step,
a <span class='snippet'>particle</span> <span class='snippet'>swarm</span>
<span class='snippet'>optimization</span> (PSO) is employed. The
results indicate that the stochastic optimum shows higher conversion
and different optimal binding energies than the deterministic optimum,
and is a more robust solution.},
}
@inproceedings{Rahmat-Samii2011a,
author = {Rahmat-Samii, Y. },
title = {Let swarms of bees optimize your future communication antennas},
booktitle = {Proc. IEEE Radio and Wireless Symp. (RWS)},
year = {2011},
abstract = {<span class='snippet'>Optimization</span> is the process of upgrading
something to perform better. Engineers constantly look for improving
their designs in multi parametric solution space. Imagine that you
will be able to use nature's evolutionary processes to obtain the
best parameters for your designs. This is the subject of this presentation.
The ever increasing advances in computational power have fueled the
temptation of using global <span class='snippet'>optimization</span>
techniques. The well-known brute force design methodologies are systematically
being replaced by the state-of-the-art Evolutionary <span class='snippet'>Optimization</span>
(EO) techniques. In recent years, EO techniques are finding growing
applications to the design of all kind of systems <span class='snippet'>with</span>
increasing complexity. Among various EO's, nature inspired techniques
such as <span class='snippet'>Particle</span> <span class='snippet'>Swarm</span>
<span class='snippet'>Optimization</span> (PSO) have attracted considerable
attention. PSO is a robust stochastic evolutionary computation technique
based on the movement and intelligence of <span class='snippet'>swarms</span>
of bees looking for the most fertile feeding location applying their
cognitive and social knowledge. This presentation will focus on:
(a) an <span class='snippet'>engineering</span> introduction to PSO
by describing in a novel fashion the underlying concepts and recent
advances for those who have used these techniques and for those who
have not had any experiences in these areas, (b) ease of deployment
of PSO on parallel computational platforms, (c) demonstration of
the potential applications of PSO to a variety of communication antenna
designs including advanced cellphone antennas, E-shaped antennas
for multiband and broadband MIMO applications, novel reconfigurable
antennas and array antennas, and (d) assessment of the advantages
and limitations of this technique.},
doi = {10.1109/RWS.2011.5725518},
}
@article{Kentzoglanakis2011a,
author = {Kentzoglanakis, K. and Poole, M. },
title = {A Swarm Intelligence Framework for Reconstructing Gene Networks:
Searching for Biologically Plausible Architectures},
journal = IEEE_J_CBB,
year = {2011},
number = {99},
abstract = {In this paper, we investigate the problem of reverse-<span class='snippet'>engineering</span>
the topology of gene regulatory networks from temporal gene expression
data. We adopt a computational <span class='snippet'>intelligence</span>
approach comprising <span class='snippet'>swarm</span> <span class='snippet'>intelligence</span>
techniques, namely particle <span class='snippet'>swarm</span> optimization
(PSO) and ant colony optimization (ACO). In addition, the recurrent
neural network (RNN) formalism is employed for modelling the dynamical
behaviour of gene regulatory systems. More specifically, ACO is used
for searching the discrete space of network architectures and PSO
for searching the corresponding continuous space of RNN model parameters.
We propose a novel solution construction process in the context of
ACO for generating biologically plausible candidate architectures.
The objective is to concentrate the search effort into areas of the
structure space that contain architectures which are feasible in
terms of their topological resemblance to real-world networks. The
proposed framework is first applied to an artificial data set with
added noise for reconstructing a subnetwork of the genetic interaction
network of S. cerevisiae (yeast). The framework is also applied to
a real-world data set for reverse-<span class='snippet'>engineering</span>
the SOS response system of the bacterium Escherichia coli. Results
demonstrate the relative advantage of utilizing problem-specific
knowledge regarding biologically plausible structural properties
of gene networks over conducting a problem-agnostic search in the
vast space of network architectures.},
doi = {10.1109/TCBB.2011.87},
}
@inproceedings{Jiang2011a,
author = {Jiang, F. and Frater, M. and Ling, S. S. H. },
title = {A distributed smart routing scheme for terrestrial sensor networks
with hybrid Neural Rough Sets},
booktitle = {Proc. IEEE Int Fuzzy Systems (FUZZ) Conf},
year = {2011},
pages = {2238--2244},
abstract = {The limited power consumption, as a major constraint, presents challenges
in improving the network throughput for Wireless Sensor Networks
(WSNs). Due to the limited computational power, the applications
of WSNs in Terrestrial Networks require the capability to pre-process
the observation data so as to remove irrelevant features or factors
from multi-dimensional dataset. This paper proposes a intelligent
distributed energy efficient routing algorithm inspired from natural
learning and adaptation process with the aid of hybrid Neural Rough
Sets theory, which is used to efficiently reduce the dimensionality
of input dataset. The algorithmic implementation and experimental
validation are described in this paper. Details of the algorithm
and its testing procedures are presented in comparison with the other
power-aware protocols, e.g., mini-hop. The validation of the proposed
model is carried out via a wireless sensor network test-bed implemented
in Castalia Simulator. The experimental results show the network
performance measurements such as delay, throughput and packet loss
that have been greatly improved as the outcome of applying this integration
with Neural Rough Sets.},
doi = {10.1109/FUZZY.2011.6007725},
}
@article{Jin2010,
author = {Nanbo Jin and Rahmat-Samii, Y. },
title = {Hybrid Real-Binary Particle Swarm Optimization (HPSO) in Engineering
Electromagnetics},
journal = IEEE_J_AP,
year = {2010},
volume = {58},
pages = {3786--3794},
number = {12},
abstract = {The applications of a hybrid real-binary <span class='snippet'>particle</span>
<span class='snippet'>swarm</span> <span class='snippet'>optimization</span>
(HPSO) algorithm in <span class='snippet'>engineering</span> electromagnetics
are described. In HPSO, each candidate design is designated by a
hybridized vector consisting of both real and binary variables. These
variables are evolved in the <span class='snippet'>optimization</span>
by following the velocity/position updating formulas of real-number
PSO (RPSO) and binary PSO (BPSO), respectively. Both single- and
multi-objective implementations of the algorithm are validated by
functional testbeds. Simulation and measurement results of three
examples, i.e, the design of a non-uniform antenna array, a multilayered
planar radar absorbing material (RAM) and a dual-band handset antenna
are presented, in order to illustrate the effectiveness of the algorithm
in representative topology exploration and material selection problems.},
doi = {10.1109/TAP.2010.2078477},
}
@inproceedings{Basak2010,
author = {Basak, A. and Pal, S. and Das, S. and Abraham, A. },
title = {Circular antenna array synthesis with a Differential Invasive Weed
Optimization algorithm},
booktitle = {Proc. 10th Int Hybrid Intelligent Systems (HIS) Conf},
year = {2010},
pages = {153--158},
abstract = {In this article we describe an <span class='snippet'>optimization</span>-based
design method for non-uniform, planar, and circular antenna arrays
<span class='snippet'>with</span> the objective of achieving minimum
side lobe levels for a specific first null beamwidth and also a minimum
size of the circumference. Central to our design is a hybridization
of two prominent metaheuristics of current interest namely the Invasive
Weed <span class='snippet'>Optimization</span> (IWO) and the Differential
Evolution (DE). IWO is a derivative-free real parameter <span class='snippet'>optimization</span>
technique that mimics the ecological behavior of colonizing weeds.
Owing to its superior performance in comparison <span class='snippet'>with</span>
many other existing metaheuristics, recently IWO is being used in
several <span class='snippet'>engineering</span> design problems
from diverse domains. For the present application, we have modified
classical IWO by incorporating the difference vector based mutation
schemes from the realm of DE. Three difficult instances of the circular
array design problem have been presented to illustrate the effectiveness
of the hybrid Differential IWO (DIWO) algorithm. The design results
obtained <span class='snippet'>with</span> modified IWO have been
shown to comfortably outperform the results obtained <span class='snippet'>with</span>
other state-of-the-art metaheuristics like <span class='snippet'>Particle</span>
<span class='snippet'>Swarm</span> <span class='snippet'>Optimization</span>
(PSO), and Differential Evolution (DE) in a statistically significant
fashion.},
doi = {10.1109/HIS.2010.5600021},
}
@inproceedings{Yan2010,
author = {Qiao Yan and ChangBin Wu and Songzhao Lv and MingLiang Bi},