Features


A PSOaided neurofuzzy classifier employing linguistic hedge concepts Amitava Chatterjee and Patrick Siarry Expert Systems with Applications The present paper proposes the development of an adaptive neurofuzzy classifier which employs two relatively less explored and comparatively new problem solving domains in fuzzy systems. The relatively less explored field is the domain of the fuzzy linguistic hedges which has been employed here to define the flexible shapes of the fuzzy membership functions (MFs). To achieve finer and finer adaptation, and hence control, over the fuzzy MFs, each MF is composed of several piecewise MF sections and the shape of each such MF section is varied by applying a fuzzy linguistic operator on it. The system employs a TakagiSugeno based neurofuzzy system where the rulenext term consequences are described by zero order elements. This proposed linguistic hedge based neurofuzzy classifier (LHBNFC) employs a relatively new field in the area of combinatorial metaheuristics, called particle previous termswarmnext term optimization (PSO), for its efficient learning. PSO has been employed in this scheme to simultaneously tune the shape of the fuzzy MFs as well as the previous termrulenext term consequences for the entire fuzzy previous termrulenext term base. The performance of the proposed system is demonstrated by implementing it for two classical benchmark data sets: (i) the iris data and (ii) the thyroid data. Performance comparison visàvis other available algorithms shows the effectiveness of our proposed algorithm.

Tree swarm optimization: an approach to PSObased tree
discovery
Veenhuis, C. Koppen, M. Kruger, J.
Nickolay, B.
Evolutionary Computation, 2005. The 2005 IEEE Congress on Publication Date: 2
In recent years a swarmbased optimization methodology called particle swarm
optimization (PSO) has developed. PSO is highly explorative and primarily used
in function optimization. This paper proposes a swarmbased learning algorithm
based on PSO which is able to discover trees in tree spaces. Particles are
flying through a tree space forming flocks around peaks of a fitness function.
Because it inherits the explorative property of PSO, it needs only few
evaluations to find suitable trees.
Supervised Classification in High Dimensional Space: Geometrical,
Statistical, and Asymptotical Properties of Multivariable Data
L. O. Jimenez, and D. A. Landgrebe
IEEE Transactions on Systems, Man and Cybernetics, Vol 28, No. 1, February
1998, pp. 3954.
Classification of Hyperdimensional Data Based On Feature and
Decision Fusion Approaches Using Projection Pursuit, Majority Voting, and
Neural Networks
L. O. Jimenez
IEEE Transactions on Geoscience and Remote Sensing, accepted.
Hyperspectral Data Analysis and Feature Reduction Via Projection
Pursuit
L. O. Jimenez and D. A. Landgrebe
IEEE Transactions on Geoscience and Remote Sensing, submitted and accepted.

Conferences

Particle Swarm Optimization
Kennedy, J., Eberhart, R., 1995,
Proc. IEEE Int’l. Conf. on Neural Networks (Perth, Australia), IEEE Service
Center, Piscataway, NJ, IV: 19421948

FPGA Implementation of Particle Swarm Optimization for Inversion of Large
Neural Networks
Paul D. Reynolds, Russell W. Duren, Matthew L. Trumbo & Robert J. Marks II
Proceedings 2005 IEEE Swarm Intelligence Symposium. SIS 2005. June 810,
Pasadena, pp. 389  392
http://web.ecs.baylor.edu/faculty/marks/REPRINTS/2005FPGAImplementationOfParticleSwarm.pdf
IEEE Transactions on Evolutionary Computation

The particle swarm  Explosion, stability, and convergence in a
multidimensional complex space
M. Clerc and J.Kennedy
IEEE Trans. Evol. Comput., vol. 6, pp. 58–73, Feb. 2002.
IEEE Transactions on Power Systems
IEEE Transactions on Magnetics

Particle Swarm Optimization and FiniteElement Based Approach for
Microwave Filter Design
Wen Wang1, Yilong Lu1, Jeffrey S. Fu1, and Yong Zhong Xiong
IEEE Transactions on Magnetics, Volume 41, Issue 5, May 2005.
http://ieeexplore.ieee.org/iel5/20/30863/01430969.pdf

Pareto optimality and particle swarm optimization
Baumgartner, U.; Magele, Ch.; Renhart, W.;
IEEE Transactions on Magnetics, Volume 40, Issue 2, Part 2, March 2004
Page(s):1172  1175
http://ieeexplore.ieee.org/iel5/20/28670/01284627.pdf
Kernels
Journals

Particle swarm based data mining algorithms for classiffication
tasks.
Sousa T, Silva A, Neves A (2004)
Parallel Computing, 30:767783.

Particle swarm based data mining algorithms for classiffication
tasks
Sousa T, Silva A, Neves A (2004)
Parallel Computing, 30:767783.
Misc.

Swarm Intelligence: Foundations, Perspectives and Applications
http://www.softcomputing.net/swarmchapter.pdf

Particle swarm optimization for image clustering (2005)
Omran M, Engelbrecht P A and Salman A
International Journal of Pattern Recognition and Artifficial Intelligence,
19(3):297321.

Fuzzy discrete particle swarm optimization for solving traveling
salesman problem.
Pang W, Wang K P, Zhou C G, at el. (2004)
Proceedings of the 4th International Conference on Computer and Information
Technology, IEEE CS Press.

Fuzzy adaptive particle swarm optimization.
Shi Y H and Eberhart R C (2001)
Proceedings of IEEE International Conference on Evolutionary Computation,
101106.

Popularity: A book [Kennedy and Eberhart, 2001]

Recent special issue
IEEE Transaction on Evolutionary Computation [Vol. 8, June 2004]
http://ieeexplore.ieee.org/xpl/tocresult.jsp?isYear=2004&isnumber=28981&Submit32=Go+To+Issue
Papers

References from chapter at
http://www.softcomputing.net/swarmchapter.pdf

Particle swarm optimization for image clustering (2005)
Omran M, Engelbrecht P A and Salman A
International Journal of Pattern Recognition and Artifficial Intelligence,
19(3):297321.

Fuzzy discrete particle swarm optimization for solving traveling
salesman problem.
Pang W, Wang K P, Zhou C G, at el. (2004)
Proceedings of the 4th International Conference on Computer and Information
Technology, IEEE CS Press.

Fuzzy adaptive particle swarm optimization.
Shi Y H and Eberhart R C (2001)
Proceedings of IEEE International Conference on Evolutionary Computation,
101106.

Hybrid Particle Swarm Optimiser with Breeding and Subpopulations
Morten Løvbjerg and Thomas Kiel Rasmussen and Thiemo Krink
http://www.evalife.dk/publications/ML_GECCO2001_PSO_with_breeding.pdf

Multiexemplars Particle Swarm Optimization Algorithm
J. J. Liang, A. K. Qin, P. N. Suganthan and S. Baskar
11th Int. Conf. on Neural Information Processing (ICONIP2004), Calcutta, India,
Vol. 3316, pp. 230235, Nov., 2004.
http://www.ntu.edu.sg/home/epnsugan/index_files/papers/iconip04CLPSO.pdf

Particle Swarm Optimization based predictive control of Proton
Exchange Membrane Fuel Cell (PEMFC)
REN Yuan, CAO Guangyi, ZHU Xinjian
http://www.zju.edu.cn/jzus/2006/A0603/A060328.pdf

PSO Bibliography  Theoretical Studies
http://tracer.uc3m.es/tws/pso/full_bibliography.html

Binary PSO
PDF: http://clerc.maurice.free.fr/pso/binary_pso/Binary_PSO.pdf
Code: http://clerc.maurice.free.fr/pso/binary_pso/binary_pso_c.zip

Comparing PSO Structures to Learn the Game Checkers from Zero
Knowledge
Franken, N. Engelbrecht, AP. 2003.
IEEE Congress on Evolutionary Computation, Canberra, Australia, 2003, 234241,
IEEE
http://cirg.cs.up.ac.za/publications/CEC2003a.zip

Evaluation of the Particle Swarm Algorithm for Biomechanical Optimization
Jaco F. Schutte, ByungIl Koh2, Jeffrey A. Reinbolt, Benjamin J. Fregly1,3,
Raphael T. Haftka1, and Alan D. George

Parallel Global Optimization with the Particle Swarm Algorithm
J.F. Schutte1, J.A. Reinbolt2, B.J. Fregly1,2, R.T. Haftka1, A.D.
George

PSOt, A Particle Swarm Optimization Toolbox for Matlab (2003)
Birge, B.,
IEEE Swarm Intelligence Symposium Proceedings, April 2426

Computational Intelligence
Eberhart, R., Simpson, P., Dobbins, R., 1996,
PC Tools, Academic Press, Inc., pp. 212223.

A Genetic Algorithm for Function Optimization: A Matlab Implementation
Houck, C., Joines, J., and Kay M.
ACM Transactions on Mathematical Software

Kennedy, J., Eberhart, R., Shi, Y., 2001, Swarm Intelligence
Academic Press, Inc.

Particle swarm optimization
J. Kennedy and R. Eberhart
Proc. IEEE Int. Conf. Neural Networks, 1995, pp. 1942–1948.

The particle swarm: Social adaptation of knowledge
J. Kennedy
Proc. IEEE Int. Conf. Evolutionary Computation, 1997, pp. 303–308.

Behavior of particles
Lecture Notes in Computer Science, vol. 1447, Proc. 7th Int. Conf.
Evolutionary Programming—Evolutionary Programming VII, Mar. 1998, pp. 581–589.

A discrete binary version of the particle swarm algorithm
J. Kennedy and R. Eberhart
Proc. IEEE Int. Conf. Systems, Man, Cybernetics, Computational Cybernetics,
Simulation, vol. 5, 1997, pp. 4104–4108.

Stereotyping: Improving particle swarm performance with cluster analysis
J. Kennedy
Proc. IEEE Int. Conf. Evolutionary Computation, vol. 2, 2000, pp. 303–308.

Matching algorithms to problems: An experimental test of the particle swarm and
some genetic algorithms on the multimodal problem generator
J. Kennedy and W. M. Spears
Proc. IEEE World Congr. Computational Intelligence, May 1998, pp. 78–83.

Using selection to improve particle swarm optimization
P. J. Angeline
Proc. IEEE Int. Conf. Computational Intelligence, 1998, pp. 84–89.

Evolutionary optimization verses particle swarm optimization: Philosophy and
the performance difference
P. J. Angeline
Lecture Notes in Computer Science, vol. 1447, Proc. 7th Int. Conf. Evolutionary
Programming  Evolutionary Programming VII, Mar. 1998, pp. 600–610.

The swarm and the queen: Toward a deterministic and adaptive particle swarm
optimization
M. Clerc
Proc. IEEE Int. Congr. Evolutionary Computation, vol. 3, 1999, p. 1957.

Particle swarm optimization: Developments, applications and resources
R. C. Eberhart and Y. Shi
Proc. IEEE Int. Conf. Evolutionary Computation, vol. 1, 2001, pp. 81–86.

Comparing inertia weights and constriction factors in particle swarm
optimization
Y. Shi and R. C. Eberhart
Proc. IEEE

Comparison between genetic algorithms and particle swarm optimization
Y. Shi and R. C. Eberhart
Lecture Notes in Computer Science  Evolutionary Programming VII, vol. 1447,
Proc. 7th Int. Conf. Evolutionary Programming, Mar. 1998, pp. 611–616.

Parameter selection in particle swarm optimization
Y. Shi and R. C. Eberhart
Lecture Notes in Computer Science—Evolutionary Programming VII, vol. 1447,
Proc. 7th Int. Conf. Evolutionary Programming, Mar. 1998, pp. 591–600.

A modified particle swarm optimizer
Y. Shi and R. C. Eberhart
Proc. IEEE Int. Conf. Evolutionary Computation, 1998, pp. 69–73.

Fuzzy adaptive particle swarm optimization
Y. Shi and R. C. Eberhart
Proc. IEEE Int. Congr. Evolutionary Computation, vol. 1, 2001, pp. 101–106.

Empirical study of particle swarm optimization
Y. Shi and R. C. Eberhart
Proc. IEEE Int. Congr. Evolutionary Computation, vol. 3, 1999, pp. 101–106.

Hybrid particle swarm optimizer with breeding and subpopulation
M. Lovbjerg, T. K. Rasmussen, and T. Krink
Proc. 3rd Genetic Evolutionary Computation Conf. (GECCO2001), San Francisco,
CA, July 2001, pp. 469–476.

Particle swarm optimizer with neighborhood operator
P. N. Suganthan
Proc. IEEE Int. Congr. Evolutionary Computation, vol. 3, 1999, pp. 1958–1962.

Effect of swarm size on cooperative particle swarm optimizers
F. van den Bergh and A. P. Engelbrecht
Proc. Genetic Evolutionary Computation Conf. (GECCO2001), San Francisco, CA,
July 2001, pp. 892–899.

Improvised music with swarms
T. M. Blackwell and P. bentley
in Proc. IEEE Congr. Evolutionary Computation 2002, vol. 2, Honolulu, HI, May
2002, pp. 1462–1467.

Extending particle swarm optimizers with selforganized critically
M. Lovbjerg and T. Krink
Proc. IEEE Int. Congr. Evolutionary Computation, vol. 2, Honolulu, HI, May
2002, pp. 1588–1593.

Division of labor in particle swarm optimization
J. S. Vesterstrom, J. Riget, and T. Krink
Proc. IEEE Int. Congr. Evolutionary Computation 2002, vol. 2, Honolulu, HI, May
2002, pp. 1570–1575.

Particle swarm optimization with spatial particle extension
T. Krink, J. S. Vesterstrom, and J. Riget
Proc. IEEE Congr. Evolutionary Computation 2002, vol. 2, Honolulu, HI, May
2002, pp. 1474–1479.

A dissipative particle swarm optimization
X. F. Xie, W. J. Zhang, and Z.L.ZhiLian Yang
Proc. IEEE Congr. Evolutionary Computation 2002, vol. 2, Honolulu, HI, May
2002, pp. 1456–1461.

Tracking changing extrema with particle swarm optimizer
A. Carlisle and G. Dozier
Auburn Univ., Auburn, AL, Tech. Rep. CSSE0108, 2001.

Particle swarm optimization method in multiobjective problems
K. E. Parsopoulos and M. N. Vrahatis
Proc. ACMSymp. Applied Computing 2002 (SAC 2002), 2002, pp. 603–607.

Particle swarm optimization for minimax problems
E. C. Laskari, K. E. Parsopoulos, and M. N. Vrahatis
Proc. IEEE Int. Congr. Evolutionary Computation, vol. 2, Honolulu, HI, May
2002, pp. 1576–1581.

Particle swarm optimization for integer programming
E. C. Laskari, K. E. Parsopoulos, and M. N. Vrahatis
in Proc. IEEE Int. Congr. Evolutionary Computation, vol. 2, Honolulu, HI, May
2002, pp. 1582–1587

Genetic Algorithms in Search, Optimization, and Machine Learning
D. E. Goldberg
Reading, MA: AddisonWesley, 1989.

Tracking and optimizing dynamic systems with particle swarms
R. C. Eberhart and Y. Shi
Proc. IEEE Congr. Evolutionary Computation 2001, Seoul, Korea, 2001, pp. 94–97.

Particle swarm optimization for reactive power and voltage control in electric
power systems
Y. Fukuyama and H. A. Yoshida
Proc. IEEE Congr. Evolutionary Computation 2001 (CEC 2001), vol. 1, May 2001,
pp. 87–93.

A discrete binary version of the particle swarm algorithm
J. Kennedy and R. C Eberhart
Proc. World Multiconf. Systemics, Cybernetics, Informatics, Orlando, FL, 1997,
pp. 4104–4109.

Particle swarm optimization: Surfing the waves
E. Ozcanand and C. K. Mohan
Proc. IEEE Congr. Evolutionary Computation 1999, vol. 3, Washington, DC, 1999,
pp. 1944–1999.

A particle swarm optimization for reactive power and voltage control considering
voltage stability
H. Yoshida, K. Kawata, Y. Fukuyama, and Y. Nakanishi
Proc. Int. Conf. Intelligent System Application to Power System, Rio de
Janeiro, Brazil, 1999, pp. 117–121.

NC end milling optimization using evolutionary computation
V. Tandon, H. E. Mounayri, and H. Kishawy
Int. J. Mach. Tools Manuf., vol. 42, no. 5, pp. 595–605, 2002.

Use of intelligentparticle swarm optimization in electromagnetics
G. Ciuprina, D. Ioan, and I. Munteanu
IEEE Trans. Magn., vol. 38, pp. 1037–1040, Mar. 2002.
http://ieeexplore.ieee.org/iel5/20/21500/00996266.pdf

Particle swarm optimization with Gaussian mutation
N. Higashi and H. Iba
Proceedings of the IEEE Swarm Intelligence Symposium 2003 (SIS 2003),
Indianapolis, IN, 2003, pp. 72–79.

Optimization of valve timing events of internal combustion engines with particle
swarm optimization
A. Ratnaweera, H. C. Watson, and S. K. Halgamuge
Proc. 2003 Congr. Evolutionary Computation (CEC 2003), vol. 4, Canberra,
Australia, Dec. 2003, pp. 2411–2418.

A Multiparent Version of the ParentCentric Normal Crossover for Multimodal
Optimization
P. J. Ballester and W. Graham Richard
Proc. CEC 2006. (pdf
available form Dr Pedro Ballester Aristin [pedro.ballester@chem.ox.ac.uk])

Recent approaches to global optimization problems through Particle Swarm
Optimization (2002)
K. E. PARSOPOULOS and M. N. VRAHATIS
Natural Computing 1: 235306, 2002 http://www.math.upatras.gr/~kostasp/papers/NatComp.pdf
with MATLAB code

Multidisciplinary Optimization of a Transport Aircraft Wing using Particle Swarm
Optimization (2002)
Gerhard Venter and Jaroslaw Sobieszczanski
http://www.vrand.com/pub/vs_mdo_2002.pdf

Computing Periodic Orbits of Nonlinear Mappings Through Particle Swarm
Optimization (2002)
Parsopoulos, K.E., Vrahatis, M.N.
Proceedings of the 4th GRACM Congress on Computational Mechanics, Patras,
Greece, June 2729, 2002
http://www.math.upatras.gr/~kostasp/papers/gracm.pdf

Particle Swarm Optimization (PSO): A Novel Paradigm for Antenna Designs
Yahya RahmatSamii, Dennis Gies, and Jacob Robinson
http://www.ee.ucla.edu/~dgies/files/gies_ursi_2003.pdf
http://www.ee.ucla.edu/~dgies/files/gies_aps_2003.pdf
http://www.ee.ucla.edu/~dgies/files/gies_motl_2003.pdf

Design of Linear Phase FIR Filters using Particle Swarm Optimization
J.M.P. Langlois
http://www.ece.queensu.ca/symposium/papers/3D_3.pdf

Human Tremor Analysis using Particle Swarm Optimization (1999)
R. Eberhart and X. Hu.
PSO is used to train artificial neural network to classify tremor patients from
normal subjects
http://icdweb.cc.purdue.edu/~hux/cgibin/download.cgi?CEC1999Human.pdf

Data Clustering using Particle Swarm Optimization
DW van der Merwe and AP Engelbrecht
http://cirg.cs.up.ac.za/publications/CEC2003d.pdf

Particle swarm optimizationbased approach for optical finite
impulse response filter design
Ying Zhou, Guangjie Zeng, and Feihong Yu
http://lcd.creol.ucf.edu/people/yzhou/publications/JAO.pdf

A Multiparent Version of the ParentCentric Normal Crossover for
Multimodal Optimization
P. J. Ballester and W. Graham Richard
Proc. CEC 2006

A particleswarmoptimized fuzzyneural network for voicecontrolled
robot systems
Chatterjee, A.; Pulasinghe, K.; Watanabe, K.; Izumi, K.
IEEE Transactions on Industrial Electronics, Vol. 52, No. 6, Dec. 2005
Page(s):1478  1489
http://ieeexplore.ieee.org/iel5/41/33004/01546363.pdf
Misc.
Thesis

An Analysis of Particle Swarm Optimizers
Frans van den Bergh. 2001.

Training Support Vector Machines with Particle Swarms
Ulrich Paquet. 2003.

Niching Particle Swarm Optimizers
Riaan Brits. 2003.
Tutorials
Fuzzy

Design of PSObased Fuzzy Classification Systems
ChiaChong Chen
Tamkang Journal of Science and Engineering, Vol. 9, No 1, pp. 63 70 (2006)
http://www2.tku.edu.tw/~tkjse/91/917.pdf

A Framework for Identification of Fuzzy Models Through Particle
Swarm Optimization Algorithm
Khosla, A., Kumar, S., Aggarwal, K.K.
Proc. 2005 Annual IEEE INDICON Conference, Chennai, India, pp. 388391, 2005.

Fuzzy PSO: A Generalization of Particle Swarm Optimization
Abdelbar, A.M., Abdelshahid, S., Wunsch II, D.C.
IEEE 2005 International Joint Conference on Neural Networks, Montreal, Canada,
pp. 10861091, 2005.
IEEE Conferences

Particle swarm optimization (PSO) applied to fuzzy modeling in a thermalvacuum
system
Marinke, R.; Araujo, E.; Coelho, Ld.S.; Matiko, I.;
Hybrid Intelligent Systems, 2005. Fifth International Conference on 69 Nov.
2005 Page(s):6 pp.

Particle evolutionary swarm optimization algorithm
(PESO)
Zavala, A.E.M.; Aguirre, A.H.; Diharce, E.R.V.
Sixth Mexican International Conference on Computer Science (ENC 2005),
September 2630, 2005 pp. 282  289

Investigation of particle swarm optimization for
dynamic reconfiguration of fieldprogrammable analog circuits
Tawdross, P.; Konig, A.
Hybrid Intelligent Systems, 2005. Fifth International Conference on 69 Nov.
2005 Page(s):6 pp.

Fuzzy adaptive turbulent particle swarm optimization
Hongbo Liu; Abraham, A.
Fifth International Conference on Hybrid Intelligent Systems, November 69,
2005

Using PSO algorithm to evolve an optimum input
subset for a SVM in time series forecasting
Chunkai Zhang; Hong Hu
2005 IEEE International Conference on Systems, Man and Cybernetics, Volume 4,
1012 Oct. 2005 Page(s):3793  3796 Vol. 4

PSO based bit rate optimization for MPEG1/2 video
coding
Arachchi, H.K.; Fernando, W.A.C.
Image Processing, 2005. ICIP 2005. IEEE International Conference on Volume 2,
1114 Sept. 2005 Page(s):II  32932

A Modified Adaptive Particle Swarm Optimization
Algorithm
Wang Lei; Kang Qi; Xiao Hui; Wu Qidi
IEEE International Conference on Industrial Technology (ICIT2005), December
1417, 2005, pp. 209  214

A Particle Swarm OptimizationBased Approach for
Pricing VAR Providers in the Electricity Market with the Consideration of
Voltage Security
ElAraby, E.E.; Yorino, N.;
2005 International Conference on Future Power Systems, November 1618, 2005,
pp. 16

A ridgelet kernel approach for regression using
particle swarm optimization algorithm
Shuyuan Yang; Min Wang; Licheng Jiao
Proceedings of the 2005 IEEE International Joint Conference on Neural Networks
(IJCNN '05), Volume 5, 31 July4 Aug. 2005, pp. 28372842 vol. 5

Lottotype competitive learning with particle swarm
features
Luk, A.; Lien, S.
Proceedings. 2005 IEEE International Joint Conference on Neural Networks (IJCNN
'05), Volume 3, 31 July4 Aug. 2005, pp. 1517  1522 vol. 3

Optimizing classrelated thresholds with particle
swarm optimization
Oliveira, L.S.; Britto, A.S., Jr.; Sabourin, R.;
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint
Conference on Volume 3, 31 July4 Aug. 2005 Page(s):1511  1516 vol. 3

Nonlinear mappings based on particle swarm
optimization
Figueroa, C.J.; Estevez, P.A.; Hernandez, R.E.
Proceedings of the 2005 IEEE International Joint Conference on Neural Networks
(IJCNN '05), Volume 3, 31 July4 Aug. 2005 Page(s):1487  1492 vol. A

Framework for Identification of Fuzzy Models through
Particle Swarm Optimization Algorithm
Khosla, A.; Kumar, S.; Aggarwal, K.K.
INDICON, 2005 Annual IEEE Proceedings of IEEE India Annual Conference (INDICON
2005), pp. 184187, 1113 December, 2005, IIT Chennai

Optimal control parameters for a UPFC in a
multimachine using PSO
Venayagamoorthy, G.K.
Intelligent Systems Application to Power Systems, 2005. Proceedings of the 13th
International Conference on 610 Nov. 2005 Page(s):6 pp

PSObased evolutionary optimization for blackbox
modeling of arbitrary shaped onchip RF inductors
Bhattacharya, R.; Joshi, A.; Bhattacharya, T.K.;
Silicon Monolithic Integrated Circuits in RF Systems, 2006. Digest of Papers.
2006 Topical Meeting on 1820 Jan. 2006 Page(s):4 pp.

A Novel Binary Particle Swarm Optimization Method
Using Artificial Immune System
Afshinmanesh, F.; Marandi, A.; RahimiKian, A.
The 2005 International Conference on Computer as a Tool (EUROCON 2005)
Volume 1, 2124 Nov. 2005 Page(s):217  220

A perturbation particle swarm optimization for the
synthesis of the radiation pattern of antenna array
Zhihao Yuan; Ronghong Jin; Junping Geng; Yu Fan; Jia Lao; Jiaqiang Li;
Xianyi Rui; Zhijiang Fang; Jing Sun
Microwave Conference Proceedings, 2005. APMC 2005. AsiaPacific Conference
Proceedings Volume 3, 47 Dec. 2005 Page(s):4 pp.

Synthesis of antenna array using particle swarm
optimization
Chen, T.B.; Chen, Y.B.; Jiao, Y.C.; Zhang, E.S.; Microwave Conference
Proceedings, 2005. APMC 2005. AsiaPacific Conference Proceedings Volume 3, 47
Dec. 2005 Page(s):4 pp.

Floorplanning based on particle swarm optimization
TsungYing Sun; ShengTa Hsieh; HsiangMin Wang; ChengWei Lin;
Emerging VLSI Technologies and Architectures, 2006. IEEE Computer Society
Annual Symposium on Volume 00, 23 March 2006 Page(s):5 pp

An adaptive diversity strategy for particle swarm
optimization
Fang Wang; Naiqin Feng; Yuhui Qiu;
Natural Language Processing and Knowledge Engineering, 2005. IEEE NLPKE '05.
Proceedings of 2005 IEEE International Conference on 30 Oct.1 Nov. 2005
Page(s):760  764

PSObased learning rate adjustment for blind source
separation
ChunLing Lin; ShengTa Hsieh; tsungYing Sun; ChanCheng Liu;
Proceedings of 2005 International Symposium on Intelligent Signal Processing
and Communication Systems, (ISPACS 2005) 1316 Dec. 2005, pp. 181184
Interesting
conferences on the topic
