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Topic: Particle swarm optimization

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	Particle swarm optimization - Wikipedia, the free encyclopedia
		Particle swarm optimization (PSO) is a form of swarm intelligence.
		This is modeled by particles in multidimensional space that have a position and a velocity.
		The particles "communicate" information they find about each other by updating their velocities in terms of local and global bests; when a new best is found, the particles will change their positions accordingly so that the new information is "broadcast" to the swarm.
	en.wikipedia.org /wiki/Particle_swarm_optimization (869 words)

	particle physics - Hutchinson encyclopedia article about particle physics (Site not responding. Last check: 2007-10-23)
		Subatomic particles include the elementary particles (quarks, leptons, and gauge bosons), which are indivisible, so far as is known, and so may be considered the fundamental units of matter; and the hadrons (baryons, such as the proton and neutron, and mesons), which are composite particles, made up of two or three quarks.
		The electromagnetic force (1) acts between all particles with electric charge, and is related to the exchange between these particles of gauge bosons called photons, packets of electromagnetic radiation.
		The unknown particle, which the researchers classified as X(3872), was detected in the decay products of beauty meson particles; it weighed as much as a single atom of helium and lasted a billionth of a trillionth of a second, an unusually long lifetime for a subatomic particle of this size.
	encyclopedia.farlex.com /particle+physics (882 words)

	Swarm intelligence - Wikipedia, the free encyclopedia
		Ant colony optimization or ACO is a metaheuristic optimization algorithm that can be used to find approximate solutions to difficult combinatorial optimization problems.
		Particle swarm optimization or PSO is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space.
		Particles then move through the solution space, and are evaluated according to some fitness criterion after each timestep.
	en.wikipedia.org /wiki/Swarm_intelligence (1121 words)

	Particle Swarm Optimization
		3 PRECURSORS: THE ETIOLOGY OF PARTICLE SWARM OPTIMIZATION
		The particle swarm optimizer was compared to a benchmark for genetic algorithms in Davis [1]: the extremely nonlinear Schaffer f6 function.
		The adjustment toward pbest and gbest by the particle swarm optimizer is conceptually similar to the crossover operation utilized by genetic algorithms.
	www.engr.iupui.edu /~shi/Coference/psopap4.html (3447 words)

	Pensive Pondering: Niching Particle Swarm Optimization (Site not responding. Last check: 2007-10-23)
		Particle Swarm Optimisation (PSO) is a search technique inspired by the group feeding and communication behaviour of schools of fish, flocks of birds, and plagues of insects (so called swarm intelligence).
		Schoeman and Engelbrecht proposed a niching approach in a paper "Using vector operations to identify niches for particle swarm optimization" (2004), which was revisited and refined in "A parallel vector based particle swarm optimizer" (2005).
		The species approach SPSO was proposed to overcome limitations by Kennedy "Stereotyping: improving particle swarm performance with cluster analysis" (2000) that attempted to improve performance of the PSO algorithm in a similar manner, using a k-Means clustering algorithm.
	pensive-pondering.blogspot.com /2005/09/niching-particle-swarm-optimization.html (842 words)

	Particle Swarm Optimization
		Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling.
		Another "best" value that is tracked by the particle swarm optimizer is the best value, obtained so far by any particle in the neighbors of the particle.
		Particle swarm optimization has been used for approaches that can be used across a wide range of applications, as well as for specific applications focused on a specific requirement.
	www.swarmintelligence.org (311 words)

	AdaptiveView.com - An Introduction to Particle Swarm Optimization
		PSO was originally developed by a social-psychologist (James Kennedy) and an electrical engineer (Russell Eberhart) in 1995 and emerged from earlier experiments with algorithms that modeled the "flocking behavior" seen in many species of birds.
		As a particle moves, it sends its coordinates to a function that applies them to the problem and measures their "fitness" – how close to a "best solution" for the problem is the result produced by the coordinates.
		If a particle could make any claim to being intelligent (and it can't) it would be that it remembers its current coordinates, its velocity (how fast it's moving along dimensions of the solution space), the best fitness value it's received so far and the coordinates that value was computed from.
	www.adaptiveview.com /articles/ipsoprnt.html (2082 words)

	BioMed Central \| Full text \| Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural ...
		The concept of the Optimized Particle Swarm Optimization (OPSO) is to optimize the free parameters of the PSO by having swarms within a swarm.
		They also tried to have the PSO heuristics optimized by another PSO running in parallel, but were not satisfied with preliminary results and discarded this concept in favor of the DE algorithm [13].
		The PSO with optimized parameters was able to outperform the standard PSO method in all five test functions in terms of "epochs needed" and "least failures".
	www.biomedcentral.com /1471-2105/7/125 (5508 words)

	Particle Swarm Optimization < Programming Kung Fu (Site not responding. Last check: 2007-10-23)
		My friend implemented a plane fitting algorithm with the PSO and used the exact same parameters as I have used for a completely different problem.
		When animating the particles this looks really life like: At first the particles swarm out in every direction, then suddenly they more and more cluster at good solutions.
		PSO is a relatively new technique with only few research (google scholar finds 2,720 papers about “Particle Swarm Optimization”, and 176,000 about “Genetic Algorithms”).
	martin.ankerl.org /2006/05/01/particle-swarm-optimization (663 words)

	Project Computing - PSO Visualisation
		Particle Swarm Optimization is an approach to problems whose solutions can be represented as a point in an n-dimensional solution space.
		PSO was developed by James Kennedy and Russell Eberhart in 1995 after being inspired by the study of bird flocking behaviour by biologist Frank Heppner.
		The previous location of the particles is marked with a yellow marker, and a green line is drawn from the previous to the current location, "visualising" the movement.
	www.projectcomputing.com /resources/psovis/index.html (1032 words)

	Particle Swarm Optimization
		The term PSO refers to a relatively new family of algorithms that may be used to find optimal or near to optimal solutions to numerical and qualitative problems.
		PSO is mainly inspired by social behaviour patterns of organisms that live and interact within large groups.
		In particular, PSO incorporates swarming behaviours observed in flocks of birds, schools of fish, or swarms of bees.
	pages.cpsc.ucalgary.ca /~khemka/pso/model.html (445 words)

	PSO - Dr. Ender ÖZCAN (Site not responding. Last check: 2007-10-23)
		Particle Swarm Optimization (PSO) is a recently proposed algorithm by James Kennedy and R.
		PSO algorithm is not only a tool for optimization, but also a tool for representing sociocognition of human and artificial agents, based on principles of social psychology.
		PSO as an optimization tool, provides a population-based search procedure in which individuals called particles change their position (state) with time.
	cse.yeditepe.edu.tr /~eozcan/research/pso (192 words)

	Main
		The PSO TOOLBOX is a collection of Matlab (.m) files that can be used to implement the Particle Swarm Optimization Algorithm (PSO) to optimize your system.
		PSO algorithm was introduced by Russel Ebenhart (an Electrical Engineer) and James Kennedy(a Social Psychologist) in 1995 (both associated with IUPUI at that time).
		The representation of the optimization problem is similar to the encoding methods used in GAs.
	psotoolbox.sourceforge.net (331 words)

	Gamasutra - Feature - "Using Particle Swarm Optimization for Offline Training in a Racing Game"
		Reynolds was intrigued by the behavior of swarms, such as a flock of birds or school of fish, which seem to move in a synchronized manner without any central control.
		Fitness refers too how well a particle performs: in a flock of birds this might be how close a bird is to a food source, in an optimization algorithm this refers to the proximity of the particle to an optima.
		Each particle's location is given by the parameters of the given optimization problem, and a particle moves around in search space by adapting and changing these parameter values.
	www.gamasutra.com /features/20051213/villiers_01.shtml (992 words)

	Particle Swarm Optimization for The Design of Trusses (Site not responding. Last check: 2007-10-23)
		Particle swarm optimization (PSO) is applied to the low-weight design of trusses.
		Traditionally, PSO has been applied to unconstrained problems; in this application, a hybrid PSO procedure incorporates a penalty function to account for stress and displacement constraints.
		The effectiveness of the hybrid PSO design procedure is demonstrated with several examples and compared with other classical optimization methods.
	www.pubs.asce.org /WWWdisplay.cgi?0400026 (140 words)

	Particle Swarm Optimization: Bibliography
		Clerc, M. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization.
		Silva, A., Neves, A., and Costa, E. An empirical comparison of particle swarm and predator prey optimisation.
		Stacey, A., Jancic, M., and Grundy, I. Particle swarm optimization with mutation.
	www.swarmintelligence.org /bibliography.php (8351 words)

	pso
		It is not the case with the present version thanks to two techniques: hyperspheres instead of hyperparallelepipeds for proximity areas, and adaptation of the swarm size as well as the relationships between the particles.
		Because of the confinement, PSO performance is depending on where the solution point is (near the center of the search space, near the bound, etc.).
		However it may happen that all particles are then moving outside the search space, and the algorithm never stops.
	clerc.maurice.free.fr /pso (1453 words)

	Particle Swarm Optimization: An Exploration Kit for Evolutionary Optimization -- from Mathematica Information Center
		Particle Swarm Optimization (PSO) is a relatively new, evolution-based search and optimization technique.
		PSO is mainly inspired by social behaviour patterns of organisms that live and interact within large groups, such as flocks, swarms, or herds.
		The connection to search and optimization problems is made by assigning direction vectors and velocities to each point in a multi-dimensional search space, where the 'individuals' interact locally with their neighbours, which leads to global dynamic behaviour (= search) patterns within the overall 'population'.
	library.wolfram.com /infocenter/Conferences/6039 (182 words)

	Particle Swarm Optimization
		This is the first book devoted entirely to Particle Swarm Optimization (PSO), which is a non-specific algorithm, similar to evolutionary algorithms, such as taboo search and ant colonies.
		Since its original development in 1995, PSO has mainly been applied to continuous-discrete heterogeneous strongly non-linear numerical optimization and it is thus used almost everywhere in the world.
		Particle Swarm Optimization explains the basic principles of the subject, particularly the concepts of particles, information link, memory and cooperation.
	www.iste.co.uk /index.php?isbn=1905209045 (182 words)

	Particle Swarm Optimization Bibliography
		The swarm and the queen: towards a deterministic and adaptive particle swarm optimization.
		C., and Kennedy, J. A new optimizer using particle swarm theory.
		Kennedy, J. Stereotyping: improving particle swarm performance with cluster analysis.
	www.engr.iupui.edu /~shi/PSO/bibliography.html (719 words)

	Overlay network
		The expression "swarm intelligence" was introduced by Beni & Wang in 1989, in the context of cellular robotic systems (see also Cellular automata, Evolutionary computation).
		Two of the most successful swarm intelligence techniques currently in existence are Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).
		ACO is a metaheuristic optimization algorithm that can be used to find approximate solutions to difficult combinatorial optimization problems.
	www.solyrich.com /p2p-articles.asp (607 words)

	Particle Swarm Optimisation (Site not responding. Last check: 2007-10-23)
		Particle Swarm Optimisation is a new natural algorithm developed by Russell Eberhart and James Kennedy.
		Constant c1 influences how much each particle is attracted to its previous best position, constant c2 how much it is attracted by the best ever position.
		When the applet displays, each particle is shown with a tail indicating its velocity (once they start to move!).
	uk.geocities.com /markcsinclair/pso.html (269 words)

	2005 IEEE Swarm Intelligence Symposium - Technical Program
		Particle swarm optimization with area of influence: increasing the effectiveness of the swarm - K.J. Binkley and M. Hagiwara
		Cognitive swarms for rapid detection of objects and associations in visual imagery - Y.
		A Hybrid particle swarm/Ant colony algorithm for the classification of hierarchical biological data - N.
	www.its.caltech.edu /~payman/sis/program.html (1142 words)

	Table of contents for Particle swarm optimization
		Swarm: Memory and Graphs of Influence 87 7.1.
		Circular neighborhood of the historical PSO 87 7.2.
		Canonical representation of a problem of optimization 169 13.4.
	www.loc.gov /catdir/toc/ecip065/2005037211.html (288 words)

	Particle Swarm People
		Note: if the words "particle swarm" are not in your home page, you may not be here.
		I'm interested in exploring how PSO can be used for unsupervised learning in robotics and in finding commonalities between the virtual search in PSO and real search tasks using real-world robots.
		Evolutionary and learning algorithms, such as genetic algorithms (GAs), particle swarm optimizer (PSO), differential evolution (DE), hybrid algorithms, etc., for nonlinear programming problems and the relations to self-organization of complex system Framework for the Technology CAD (TCAD) approach for ultra-small transistor design and optimization.
	www.particleswarm.info /people.html (3476 words)

	Open Directory - Computers: Artificial Life: Particle Swarm: People (Site not responding. Last check: 2007-10-23)
		Eberhart, Russell C. - One of the founders of particle swarm optimization.
		Hu, Xiaohui - Research focus is biomedical data analysis and computational intelligence, especially particle swarm optimization.
		Xie, Xiao-Feng - Particle swarm optimization (PSO), differential evolution (DE) and hybrid algorithms, for optimization problems.
	dmoz.org /Computers/Artificial_Life/Particle_Swarm/People (314 words)

	Some math about Particle Swarm Optimization
		In Particle Swarm Optimization, the usual iterative form is the following one:
		In the second one we obtain real numbers if (and only if) t is an integer, but nothing prevent us to give any real positive value to t, and then v(t) and y(t) are "true" complex numbers.
		Remember v is in fact the velocity of the particle, so it has indeed to be equal to zero in a convergence point.
	clerc.maurice.free.fr /pso/PSO_math_stuff/PSO_math_stuff.htm (1338 words)

	Real (Site not responding. Last check: 2007-10-23)
		Moore P, Venayagamoorthy GK, "Evolving Digital Circuits Using Hybrid Particle Swarm Optimization and Differential Evolution", Conference on Neuro-Computing and Evolving Intelligence, Auckland, New Zealand, December 13-15, 2004.
		Singhal G, Venayagamoorthy GK, "Comparison of Quantum-Inspired Evolutionary Algorithm and Particle Swarm Optimization for Neural Network Training", Conference on Neuro-Computing and Evolving Intelligence, Auckland, New Zealand, December 13-15, 2004.
		Gudise VG, Venayagamoorthy GK, “Comparison of Particle Swarm Optimization and Backpropagation as Training Algorithms for Neural Networks”, IEEE Swarm Intelligence Symposium, Indianapolis, IN, USA, April 24 -26, 2003, pp.110 - 117.
	www.ece.umr.edu /RTPIS/particleswarm.html (363 words)

	Xiao-Feng Xie's research page
		Handling boundary constraints for numerical optimization by particle swarm flying in periodic search space.
		DEPSO: hybrid particle swarm with differential evolution operator.
		Simulation optimization with multiple-demes genetic algorithms in master-slave parallel mode.
	www.adaptivebox.net /research (613 words)

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