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  1. Swarm Intelligence
  2. Swarm Intelligence : Volume 3
  3. Swarm Intelligence : Volume 3, Issue 2, June 2009
  4. Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm
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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 5
Swarm Intelligence : Volume 4
Swarm Intelligence : Volume 3
Swarm Intelligence : Volume 3, Issue 4, December 2009
Swarm Intelligence : Volume 3, Issue 3, September 2009
Swarm Intelligence : Volume 3, Issue 2, June 2009
Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions
Ant colony optimization for multicasting in static wireless ad-hoc networks
Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm
Swarm Intelligence : Volume 3, Issue 1, March 2009
Swarm Intelligence : Volume 2
Swarm Intelligence : Volume 1

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Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm

Content Provider SpringerLink
Author Cooren, Yann Clerc, Maurice Siarry, Patrick
Copyright Year 2009
Abstract This paper presents a study of the performance of TRIBES, an adaptive particle swarm optimization algorithm. Particle Swarm Optimization (PSO) is a biologically-inspired optimization method. Recently, researchers have used it effectively in solving various optimization problems. However, like most optimization heuristics, PSO suffers from the drawback of being greatly influenced by the selection of its parameter values. Thus, the common belief is that the performance of a PSO algorithm is directly related to the tuning of such parameters. Usually, such tuning is a lengthy, time consuming and delicate process. A new adaptive PSO algorithm called TRIBES avoids manual tuning by defining adaptation rules which aim at automatically changing the particles’ behaviors as well as the topology of the swarm. In TRIBES, the topology is changed according to the swarm behavior and the strategies of displacement are chosen according to the performances of the particles. A comparative study carried out on a large set of benchmark functions shows that the performance of TRIBES is quite competitive compared to most other similar PSO algorithms that need manual tuning of parameters. The performance evaluation of TRIBES follows the testing procedure introduced during the 2005 IEEE Conference on Evolutionary Computation. The main objective of the present paper is to perform a global study of the behavior of TRIBES under several conditions, in order to determine strengths and drawbacks of this adaptive algorithm.
Starting Page 149
Ending Page 178
Page Count 30
File Format PDF
ISSN 19353812
Journal Swarm Intelligence
Volume Number 3
Issue Number 2
e-ISSN 19353820
Language English
Publisher Springer US
Publisher Date 2009-03-07
Publisher Place Boston
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Particle swarm optimization Tribes Adaptive algorithm Computer Communication Networks Communications Engineering, Networks ApplicationMathematics/Computational Methods of Engineering Computer Systems Organization and Communication Networks Artificial Intelligence (incl. Robotics)
Content Type Text
Resource Type Article
Subject Artificial Intelligence
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