Special issue on Metaheuristic Algorithms in Modeling and Optimization 
Deadline: 31 August 2016 
Special Issue on "Particle Swarm Optimization and Genetic Algorithm" 
Submission Deadline: April 6th, 2016 
Bibliography  
Convergence Analysis for Particle Swarm Optimization  Berthold Immanuel Schmitt 
Applications of Metaheuristics in Process Engineering (August 2014)  Jayaraman Valadi and Patrick Siarry (Eds) 
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 ElGhazali 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, & SungBae Cho 
Book: Theory
of Randomized Search Heuristics, 2011 
Foundations and Recent Developments edited by Anne Auger (INRIA, France) & Benjamin Doerr (MaxPlanckInstitut 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 
Biologicallyinspired 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. 490507, August 2009  Mehmet Cunkas; M. Yasin Özsag lam 
Book: Metaheuristics: from design to implementation, May 2009  ElGhazali Talbi 
Book: Particle Swarm Optimization, March 2009  The book is edited by Aleksandar Lazinica. It contains 29 chapters. Can be downloaded for free 
Book: MultiObjective Optimization in Computational Intelligence, March 2008  Information Science Reference Two chapters about MOPSO: Chapter 2  Multiobjective particle swarm optimization, K.E. Parsopoulos and M.N. Vrahatis Chapter 4  Towards a More Efficient MultiObjective Particle Swarm Optimizer, Luis V. SantanaQuintero, Noel RamírezSantiago and Carlos A. Coello Coello 
XPS project technical reports  eXtended Particle Swarm project research papers (20042007) 
Building Virtual Reality Spaces for Visual Data Mining with Hybrid EvolutionaryClassical Optimization: Application to Microarray Gene Expression Data. Proceedings of the IASTED International Joint Conference on Artificial Intelligence and Soft Computing, ASC'2004, September 13, 2004, Marbella, Spain. ACTA Press, Anaheim, USA, pp 161166. 
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), 1114 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.473491, 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 0122286308 Chapter 6: Evolutionary Computation Implementations Includes implementations of a Genesislike 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, 303308  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 7883,
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. 19581962  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 3640  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, 8488  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. 12321239, November 2001, presented at IEEE PES Winter Meeting Columbus, Jan. 28Feb. 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 28February 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 (GECCO2001).  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. 661669.  A. R. Cockshott and B. E. Hartman 
Feature Selection for QSAR and QSPR Using Binary Particle Swarms, in Journal of Medicinal Chemistry , in press (200112). Abstract  Dimitris K. Agrafiotis and Walter Cedeño 
Use of IntelligentParticle Swarm Optimization in Electromagnetics, COMPUMAG Evian, July 25 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. 941964.  Maurice Clerc 
"The Particle Swarm: Explosion, Stability, and Convergence in a MultiDimensional Complex Space", IEEE Transactions on Evolutionary Computation, 2002, vol. 6, p. 5873. 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. 10511056.  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. 16771681.  Hu X., Eberhart R. C. 
"A swarm metaphor for Multiobjective Optimization", Engineering Optimization, vol. 2, iss. 34, 2002, p. 141153.  Ray T., Liew K. M. 
Adaptive particle swarm optimization: detection and response to dynamic systems. Proceedings of Congress on Evolutionary Computation, 2002. pp.16661670. Hawaii, USA.  X. Hu, and R. Eberhart 
New Optimization Techniques in Enginering, in print,
SpringerVerlag, 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 10191026 
Liu, H.; Abraham, A.; Clerc, M. 