NDLI: Bare Bones Particle Swarm Optimization with Gaussian or Cauchy jumps

Content Provider	IEEE Xplore Digital Library
Author	Krohling, R.A. Mendel, E.
Copyright Year	2009
Description	Author affiliation: Departmento de Informática, PPGI, Universidade Federal do Espírito Santo, CEP 29060-270, Vitória, Brazil (Krohling, R.A.; Mendel, E.)
Abstract	Bare Bones Particle Swarm Optimization (BBPSO) is a powerful algorithm, which has shown potential to solving multimodal optimization problems. Unfortunately, BBPSO may also get stuck into local optima when optimizing functions with many local optima in high dimensional search space. In previous attempts an approach was developed which consists of a jump strategy combined with PSO in order to escape from local optima and promising results have been obtained. In this paper, we combine BBPSO with a jump strategy when no fitness improvement is observed. The jump strategy is implemented based on the Gaussian or the Cauchy probability distribution. The algorithm was tested on a suite of well-known benchmark multimodal functions and the results were compared with those obtained by the standard BBPSO algorithm and with BBPSO with re-initialization. Simulation results show that the BBPSO with the jump strategy performs well in all functions investigated. We also notice that the improved performance is due to a successful number of Gaussian or Cauchy jumps.
Starting Page	3285
Ending Page	3291
File Size	275123
Page Count	7
File Format	PDF
ISBN	9781424429585
DOI	10.1109/CEC.2009.4983361
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-18
Publisher Place	Norway
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bones Particle swarm optimization Probability distribution Benchmark testing Random number generation Acceleration Sampling methods Gaussian distribution Stability Equations jumps Bare Bones Particle Swarm Gaussian and Cauchy probability distribution nonlinear optimization
Content Type	Text
Resource Type	Article

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