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  1. Swarm Intelligence
  2. Swarm Intelligence : Volume 8
  3. Swarm Intelligence : Volume 8, Issue 1, March 2014
  4. A generalized theoretical deterministic particle swarm model
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Swarm Intelligence : Volume 11
Swarm Intelligence : Volume 10
Swarm Intelligence : Volume 9
Swarm Intelligence : Volume 8
Swarm Intelligence : Volume 8, Issue 4, December 2014
Swarm Intelligence : Volume 8, Issue 3, September 2014
Swarm Intelligence : Volume 8, Issue 2, June 2014
Swarm Intelligence : Volume 8, Issue 1, March 2014
Cooperative navigation in robotic swarms
A generalized theoretical deterministic particle swarm model
Cache consensus: rapid object sorting by a robotic swarm
Swarm Intelligence : Volume 7
Swarm Intelligence : Volume 6
Swarm Intelligence : Volume 5
Swarm Intelligence : Volume 4
Swarm Intelligence : Volume 3
Swarm Intelligence : Volume 2
Swarm Intelligence : Volume 1

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A generalized theoretical deterministic particle swarm model

Content Provider SpringerLink
Author Cleghorn, Christopher W. Engelbrecht, Andries P.
Copyright Year 2014
Abstract A number of theoretical studies of particle swarm optimization (PSO) have been done to gain a better understanding of the dynamics of the algorithm and the behavior of the particles under different conditions. These theoretical analyses have been performed for both the deterministic PSO model and more recently for the stochastic model. However, all current theoretical analyses of the PSO algorithm were based on the stagnation assumption, in some form or another. The analysis done under the stagnation assumption is one where the personal best and neighborhood best positions are assumed to be non-changing. While analysis under the stagnation assumption is very informative, it could never provide a complete description of a PSO’s behavior. Furthermore, the assumption implicitly removes the notion of a social network structure from the analysis. This paper presents a generalization to the theoretical deterministic PSO model. Under the generalized model, conditions for particle convergence to a point are derived. The model used in this paper greatly weakens the stagnation assumption, by instead assuming that each particle’s personal best and neighborhood best can occupy an arbitrarily large number of unique positions. It was found that the conditions derived in previous theoretical deterministic PSO research could be obtained as a specialization of the new generalized model proposed. Empirical results are presented to support the theoretical findings.
Starting Page 35
Ending Page 59
Page Count 25
File Format PDF
ISSN 19353812
Journal Swarm Intelligence
Volume Number 8
Issue Number 1
e-ISSN 19353820
Language English
Publisher Springer US
Publisher Date 2014-01-14
Publisher Place Boston
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Deterministic particle swarm optimization Theoretical analysis Particle convergence Artificial Intelligence (incl. Robotics) Computer Systems Organization and Communication Networks ApplicationMathematics/Computational Methods of Engineering Communications Engineering, Networks Computer Communication Networks
Content Type Text
Resource Type Article
Subject Artificial Intelligence
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