Call for Papers


Journal of Memetic Computing
Special Issue on
Nature Inspired Cooperative Strategies for Optimization
- Dr. Fernando Otero, University of Kent, UK (F.E.B.Otero@kent.ac.uk)
- Dr. Antonio D. Masegosa, University of Granada, Spain (admase@decsai.ugr.es)
- Dr. German Terrazas, University of Nottingham, UK (German.Terrazas@nottingham.ac.uk)

Submission deadline: 1st March 2014
Journal in Modelling in Management (JM2) Special Issue on Applications of Evolutionary Algorithms to Management Problems
Submission deadline: July 1st, 2014
Call for Papers - Special Issue of Networks
Metaheuristics in Network Optimization
Articles are to be submitted directly to the Guest Editors:
Fred Glover, OptTek, Inc., Boulder, CO
glover@opttek.com
Gary Kochenberger, University of Colorado at Denver
Gary.kochenberger@ucdenver.edu
Submission deadline: August 1st, 2014


International Journal of Swarm Intelligence Research (IJSIR)
Yuhui Shi, yuhui.shi@xjtlu.edu.cn
Editor-in-Chief
(No deadline)
The Editor-in-Chief of the International Journal of Swarm Intelligence Research (IJSIR) would like to invite you to consider submitting a manuscript for inclusion in this scholarly journal. Also if you are interested in editing a special issue for the IJSIR, please kindly let me know.


Bibliography
Métaheuristiques, 2014
ouvrage coordonné par Patrick Siarry
Advances in Heuristic Signal Processing and Applications, 2013
Chatterjee, Amitava; Nobahari, Hadi; Siarry, Patrick (Eds.)
Evolutionary Optimization Algorithms, 2013
Dan Simon
Heuristics: Theory and Applications, 2013
edited by Patrick Siarry
Book: Hybrid metaheuristics, 2013
edited by El-Ghazali Talbi
Book: Metaheuristics for Dynamic Optimization, 2012
edited by Alba Enrique, Amir Nakib, & Patrick Siarry
Book: Integration of Swarm Intelligence and Artificial Neural Network, 2011
edited by Satchidananda Dehuri, Susmita Ghosh, & Sung-Bae Cho
Book: Theory of Randomized Search Heuristics, 2011
Foundations and Recent Developments
edited by Anne Auger (INRIA, France) & Benjamin Doerr (Max-Planck-Institut fur Informatik, Germany)
Book: Swarm Stability and Optimization, 2011
Gazi, Veysel, Passino, Kevin M.
Book: Particle Swarm Optimization: Theory, Techniques and Applications,  2010 - 3rd Quarter Andrea E. Olsson (Ed.)
Book: Particle Swarm Optimization and Intelligence: Advances and Applications, January 2010
Konstantinos E. Parsopoulos, and Michael N. Vrahatis
Biologically-inspired Optimisation Methods: Parallel Algorithms, Systems and Applications, August 2009 Andrew Lewis et al. (Ed.)
Optimization in Signal and Image Processing, June 2009 Patrick Siarry (Ed.)
A comparative study on PSO and GA for TSP, pp. 490-507, August 2009 Mehmet Cunkas; M. Yasin Özsag lam
Book: Metaheuristics: from design to implementation, May 2009 El-Ghazali Talbi
Book:  Particle Swarm Optimization, March 2009 The book is edited by Aleksandar Lazinica. It contains 29 chapters. Can be downloaded for free
Book: Multi-Objective Optimization in Computational Intelligence, March 2008 Information Science Reference
Two chapters about MOPSO:
Chapter 2 - Multi-objective particle swarm optimization, K.E. Parsopoulos  and  M.N. Vrahatis
Chapter 4 - Towards a More Efficient Multi-Objective Particle Swarm Optimizer, Luis V. Santana-Quintero, Noel Ramírez-Santiago and Carlos A. Coello Coello
XPS project technical reports eXtended Particle Swarm project research papers (2004-2007)
Building Virtual Reality Spaces for Visual Data Mining with Hybrid Evolutionary-Classical Optimization: Application to Microarray Gene Expression Data. Proceedings of the IASTED International Joint Conference on Artificial Intelligence and Soft Computing, ASC'2004, September 1-3, 2004, Marbella, Spain. ACTA Press, Anaheim, USA, pp 161-166.
Julio Valdés
Book: Swarm Intelligence and Data Mining (Foreword by James Kennedy), Springer Verlag, 2006 Ajith Abraham, Crina Grosan and Vitorino Ramos
Book: Fundamentals of Computational Swarm Intelligence, publishec by Wiley. Available on Amazon Andries Angelbrecht
Book: Particle Swarm Optimization, published by ISTE, (direct link here) available also on Amazon. 2006 translation of the 2005 French book, with a few updates. Clerc M.
"Repel the swarm to the optimum", Ed. Elsevier, Applied Mathematics and Computation 173 (2006) 265–272. available on ScienceDirect A. Leontitsis, D. Kontogiorgos, J. Pagge 
2005 IEEE TEC award for a PSO paper!
Particle Swarm Optimization for economic dispatch with security constraints, FLAIRS'04 Abstract
ROHIT KUMAR PANCHOLI , K.S.SWARUP


An adaptive PSO algorithm for reactive power optimization, APSCOM (Advances in Power System Control Operation and Management), S6: Application of Artificial Intelligence Technique (part I), 11-14 November 2003, Hong Kong Zhang, W., Liu, Y., Clerc M.,
Optimal operating path for automated drilling operations by a new heuristic approach using PSO. International Journal of Production Research, Vol. 42, n°3, p.473-491, 2004

Onwubolu, G. Clerc M.


OLDER REFERENCES

Bibliography By Yuhui Shi
Bibliography on Particle Swarm Optimization By Ender Özcan
CS&E Fall '95 Seminar Abstracts By Russell Eberhart. A concept for the optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared. Benchmark testing of both paradigms is described, and applications, including nonlinear function optimization and neural network training, are discussed. Relationships between particle swarm optimization and both artificial life and genetic algorithms are reviewed. (not on line)
Computational Intelligence Developments and Applications By James Kennedy and Russell Eberhart. Volume 4, Page 1942. 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. Postscript file.
   
Computational Intelligence PC Tools By Russell C. Eberhart, Roy W. Dobbins and Patrick K. Simpson Copyright 1996 Academic Press Professional ISBN 0-1222-8630-8 Chapter 6: Evolutionary Computation Implementations Includes implementations of a Genesis-like genetic algorithm and a particle swarm optimizer. (Software is on the diskette.)
NEL Seminars: Spring Semester 1996 Wednesday, January 31: Cognition and Social Interaction: The Particle Swarm Algorithm. Discusses the hypothesis that precise reporting of individual and group performance, accomplished in human societies through the use of notation, is fundamental to the optimal adaptation of groups. Includes a PC demo. Speaker: Dr. Jim Kennedy, Social Psychologist, U.S. Bureau of Labor Statistics.
Socially Intelligent Agents Papers from the 1997 Fall Symposium Minds and Cultures: Particle Swarm Implications, James Kennedy (not on line)
The particle swarm: Social adaptation of knowledge. Proceedings of the 1997 International Conference on Evolutionary Computation, Indianapolis, Indiana, IEEE Serice Center,  Piscataway, NJ, 303-308  By James Kennedy
9th International Conference on Tools with Artificial Intelligence (ICTAI'97) Using the Particle Swarm Optimization Technique to Train a Recurrent Neural Model , J. Salerno (not on line. Abstract)
Evolutionary Programming Conference 1998 The Behavior of Particles 581, J. Kennedy 
Parameter Selection in Particle Swarm Optimization 591, Y. Shi and R.C. Eberhart Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences 601, P.J. Angeline 
Comparison between Genetic Algorithms and Particle Swarm Optimization 611, R.C. Eberhart and Y. Shi .
An Experimental Test of the Particle Swarm and Some Genetic Algorithms on the Multimodal Problem Generator By James Kennedy and William M. Spears. In IEEE International Conference on Evolutionary Computation, pages 78-83, 1998. [The author warns that the PostScript for this paper was generated by Microsoft Word and may not be printable and/or displayable on some machines.]. 
Compressed PS file (53K)  http://www.aic.nrl.navy.mil/~spears/papers/wcci98.ps.gz
PDF file  wcci98.pdf
Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences. In V. W. Porto, Saravanan, N., Waagen, D., and Eiben, A. E. (Eds), Evolutionary Programming VII, 601, 610, Berlin, Springer By Angeline, P. (1998)
Math stuff about Particle Swarm Optimization  By Maurice Clerc. On line  papers and drafts of published/submitted papers and technical reports. Non classical PSO (adaptive, discrete/granular, combinatorialFuzzy). Contains also some presentations (HTML and PowerPoint) and C programs (quite often updated)
Parameter Selection in Particle Swarm Optimization By Yuhui Shi and Russell C. Eberhart. In this paper, the authors first analyze the impact that the inertia weight and maximum velocity have on the performance of the particle swarm optimizer, and then provide guidelines for selecting these two parameters. Analysis of experiments demonstrates the validity of these guidelines.
Particle Swarm Optimization (From Proc. IEEE Int'l. Conf. on Neural Networks (Perth, Australia)) By James Kennedy &Russell Eberhart. 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.
Particle Swarm Optimiser with Neighbourhood Operator. Proceedings of the Congress on Evolutionary Computation 1999, p. 1958-1962 By P. N. Suganthan
Training Product Units in Feedforward Neural Networks using Particle Swarm Optimization. Proceedings of the International Conference on Artificial Intelligence, Durban, South Africa, 1999, pp 36-40 By A. Ismail, A. P. Engelbrecht
Application of Particle Swarm to Multiobjective Optimization, Department of Computer Science, Auburn University, 1999. PS file.  Moore J., Chapman R.
Adapting Particle Swarm Optimization to Dynamic Environments. International Conference on Artificial Intelligence, Monte Carlo Resort, Las Vegas, Nevada, USA By Anthony Carlisle, Gerry Dozier


Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings of the 2000 International Congress on Evolutionary Computation, San Diego, California, IEEE Service Center, Piscataway, NJ, 84-88 By Eberhart, R.C., and Shi Y.


A Particle Swarm Optimization for Reactive Power and Voltage Control considering Voltage Security Assessment. IEEE Trans. on Power Systems, Vol. 15,No 4, pp. 1232-1239, November 2001, presented at IEEE PES Winter Meeting Columbus, Jan. 28-Feb. 1, 2001 By H.Yoshida, K.Kawata, Y.Fukuyama, S.Takayama,&Y.Nakanishi 
Practical Distribution State Estimation
Using Hybrid Particle Swarm Optimization. Proceeding of IEEE Power Engineering Society Winter Meeting. January 28-February 1st, 2001. Columbus, Ohio, USA.
By Shigenori Naka, Takamu Genji, Toshiki Yura, and Yoshikazu Fukuyama
Hybrid Particle Swarm Optimiser with Breeding and subpopulations. Proceedings of the third Genetic and Evolutionary Computation Conference (GECCO-2001). By Løvbjerg, M., Rasmussen, T., K. and Krink, T.
Swarm Intelligence (book). Publisher Morgan Kaufmann Academic Press. 2001 By James Kennedy and Russel Eberhart, with Yuhui Shi
Communication in Particle Swarm Optimization Illustrated by the Travelling Salesman Problem. Air Force Institute of Technology, 2001 B. R. Secrest and G. B. Lamont 
Engineering Design Optimization using a Swarm with an Intelligent Information Sharing among Individuals, Engineering Optimization (2001) Ray, T., and Saini, P.
Several papers to download (IJCNN 2001, GECCO 2001, etc.) Frans van den Bergh, and A.P Engelbrecht
Improving the fermentation medium for Echinocandin B production. Part II: Particle swarm optimization, in Process Biochemistry, vol. 36, 2001, pp. 661-669. A. R. Cockshott and B. E. Hartman
Feature Selection for QSAR and QSPR Using Binary Particle Swarms, in Journal of Medicinal Chemistry , in press (2001-12). Abstract Dimitris K. Agrafiotis and Walter Cedeño
Use of Intelligent-Particle Swarm Optimization in Electromagnetics, COMPUMAG Evian, July 2-5 2001, poster session B. Gabriela Ciuprina, Daniel Ioan, Irina Muteanu


L'optimisation par essaim particulaire. Principes, modèles et usages. Hermès Science, Techniques et science informatiques, vol. 21, n° 7/2002, p. 941-964. Maurice Clerc
"The Particle Swarm: Explosion, Stability, and Convergence in a Multi-Dimensional Complex Space", IEEE Transactions on Evolutionary Computation, 2002, vol. 6, p. 58-73. Abstract Maurice Clerc, James Kennedy

2005 IEEE TEC award
"MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization", Congress on Evolutionary Computation (CEC'2002), Piscataway, New Jersey, 2002, p. 1051-1056.  Coello Coello C. A., Salazar Lechuga M 
"Multiobjective Optimization Using Dynamic Neighborhood Particle Swarm Optimization", Congress on Evolutionary Computation (CEC'2002), Piscataway, New Jersey, 2002, p. 1677-1681.  Hu X., Eberhart R. C.
 "A swarm metaphor for Multiobjective Optimization", Engineering Optimization, vol. 2, iss. 34, 2002, p. 141-153. Ray T., Liew K. M.
Adaptive particle swarm optimization: detection and response to dynamic systems. Proceedings of Congress on Evolutionary Computation, 2002. pp.1666-1670. Hawaii, USA. X. Hu, and R. Eberhart
New Optimization Techniques in Enginering, in print, Springer-Verlag, 2003. Preface and Content.
Onwubolu G. C., Babu B. V.
 Chaotic dynamic characteristics in swarm intelligence • Article
Applied Soft Computing, Volume 7, Issue 3, 1 June 2007, Pages 1019-1026
Liu, H.; Abraham, A.; Clerc, M.