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  1. Swarm Intelligence
  2. Swarm Intelligence : Volume 6
  3. Swarm Intelligence : Volume 6, Issue 3, September 2012
  4. A competitive clustering particle swarm optimizer for dynamic optimization problems
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Swarm Intelligence : Volume 11
Swarm Intelligence : Volume 10
Swarm Intelligence : Volume 9
Swarm Intelligence : Volume 8
Swarm Intelligence : Volume 7
Swarm Intelligence : Volume 6
Swarm Intelligence : Volume 6, Issue 4, December 2012
Swarm Intelligence : Volume 6, Issue 3, September 2012
A competitive clustering particle swarm optimizer for dynamic optimization problems
An experimental analysis of design choices of multi-objective ant colony optimization algorithms
Training feedforward neural networks with dynamic particle swarm optimisation
Swarm Intelligence : Volume 6, Issue 2, June 2012
Swarm Intelligence : Volume 6, Issue 1, March 2012
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 competitive clustering particle swarm optimizer for dynamic optimization problems

Content Provider SpringerLink
Author Nickabadi, Ahmad Ebadzadeh, Mohammad Mehdi Safabakhsh, Reza
Copyright Year 2012
Abstract Optimization in dynamic optimization problems (DOPs) requires the optimization algorithms not only to locate, but also to continuously track the moving optima. Particle swarm optimization (PSO) is a population-based optimization algorithm, originally developed for static problems. Recently, several researchers have proposed variants of PSO for optimization in DOPs. This paper presents a novel multi-swarm PSO algorithm, namely competitive clustering PSO (CCPSO), designed specially for DOPs. Employing a multi-stage clustering procedure, CCPSO splits the particles of the main swarm over a number of sub-swarms based on the particles positions and on their objective function values. The algorithm automatically adjusts the number of sub-swarms and the corresponding region of each sub-swarm. In addition to the sub-swarms, there is also a group of free particles that explore the environment to locate new emerging optima or exploit the current optima which are not followed by any sub-swarm. The adaptive search strategy adopted by the sub-swarms improves both the exploitation and tracking characteristics of CCPSO. A set of experiments is conducted to study the behavior of the proposed algorithm in different DOPs and to provide guidelines for setting the algorithm’s parameters in different problems. The results of CCPSO on a variety of moving peaks benchmark (MPB) functions are compared with those of several state-of-the-art PSO algorithms, indicating the efficiency of the proposed model.
Starting Page 177
Ending Page 206
Page Count 30
File Format PDF
ISSN 19353812
Journal Swarm Intelligence
Volume Number 6
Issue Number 3
e-ISSN 19353820
Language English
Publisher Springer US
Publisher Date 2012-06-30
Publisher Place Boston
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Particle swarm optimization PSO Multi-swarm Dynamic optimization Local search Artificial Intelligence (incl. Robotics) Computer Communication Networks Communications Engineering, Networks ApplicationMathematics/Computational Methods of Engineering Computer Systems Organization and Communication Networks
Content Type Text
Resource Type Article
Subject Artificial Intelligence
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