Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm View Full Text


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Article Info

DATE

2009-06

AUTHORS

Yann Cooren, Maurice Clerc, Patrick Siarry

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. More... »

PAGES

149-178

References to SciGraph publications

  • 2006. Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms in PRINCIPLES AND PRACTICE OF CONSTRAINT PROGRAMMING - CP 2006
  • 2004. TRIBES application to the flow shop scheduling problem in NEW OPTIMIZATION TECHNIQUES IN ENGINEERING
  • 2008. Magnetic Resonance Image Segmentation Based on Two-Dimensional Exponential Entropy and a Parameter Free PSO in ARTIFICIAL EVOLUTION
  • 2007-06. Particle swarm optimization in SWARM INTELLIGENCE
  • 1996. An adaptive parallel Genetic Algorithm for VLSI-layout optimization in PARALLEL PROBLEM SOLVING FROM NATURE — PPSN IV
  • 1998. Parameter selection in particle swarm optimization in EVOLUTIONARY PROGRAMMING VII
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    http://scigraph.springernature.com/pub.10.1007/s11721-009-0026-8

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    http://dx.doi.org/10.1007/s11721-009-0026-8

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