Limiting the Velocity in the Particle Swarm Optimization Algorithm

Authors

  • Julio Barrera Universidad Michoacana de San Nicolás de Hidalgo; Coordinación General de Educación a Distancia, Morelia
  • Osiris Álvarez Bajo CIAD, A.C., Grupo de Investigación en Biopolímeros, Hermosillo, Sonora
  • Juan J. Flores Universidad Michoacana de San Nicolás de Hidalgo; División de Estudios de Posgrado, Facultad de Ingeniería Eléctrica, Morelia
  • Carlos A. Coello Coello CINVESTAV IPN, Departamento de Computación, Ciudad de México

DOI:

https://doi.org/10.13053/cys-20-4-2505

Keywords:

Particle swarm, velocity, limits.

Abstract

Velocity in the Particle Swarm Optimization algorithm (PSO) is one of its major features, as it is the mechanism used to move (evolve) the position of a particle to search for optimal solutions. The velocity is commonly regulated, by multiplying a factor to the particle’s velocity. This velocity regulation aims to achieve a balance between exploration and exploitation. The most common methods to regulate the velocity are the inertia weight and constriction factor. Here, we present a different method to regulate the velocity by changing the maximum limit of the velocity at each iteration, thus eliminating the use of a factor. Wego further and present a simpler version of the PSO algorithm that achieves competitive and, in some cases, even better results than the original PSO algorithm.

Author Biographies

Julio Barrera, Universidad Michoacana de San Nicolás de Hidalgo; Coordinación General de Educación a Distancia, Morelia

Received a PhD in Electrical Engineering with specialization in Computer Science from División de Estudios de Posgrado, Facultad de Ingeniería Eléctrica, Universidad Michoacana de San Nicolás de Hidalgo in 2008. His main research interest is numerical optimization using metaheuristics, especially with evolutionary algorithms.

Osiris Álvarez Bajo, CIAD, A.C., Grupo de Investigación en Biopolímeros, Hermosillo, Sonora

Has a PhD degree in science by the Nuclear Science Institute of UNAM (México, 2008). National Researcher System member (Level 1). CONACYT Research Fellow at CIAD A.C since 2015. Interested in mathematical and computational modeling of molecular systems.

Juan J. Flores, Universidad Michoacana de San Nicolás de Hidalgo; División de Estudios de Posgrado, Facultad de Ingeniería Eléctrica, Morelia

Got a B. Eng. degree in Electrical Engineering from the Universidad Michoacana in 1984. In 1986 he got a M.Sc. degree in computer science from Centro de Investigación y Estudios Avanzados, of the Instituto Politécnico Nacional. In 1997 he got a Ph.D. degree in Computer Science from the University of Oregon, USA. He is a full time professor at the Universidad Michoacana since 1986. His research work deals with Evolutionary Computation, Machine Learning, Soft Computing, and in general Artificial Intelligence  and its applications to Electrical Engineering, Computer Security, and Financial Analysis.

Carlos A. Coello Coello, CINVESTAV IPN, Departamento de Computación, Ciudad de México

Received a PhD in Computer Science from Tulane University (USA) in 1996. Since 2001, he works at CINVESTAV IPN. He is a member of the mexican National System of Researchers (Level 3). His main research interests are on single- and multi-objective optimization using metaheuristics (mainly evolutionary algorithms).

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Published

2016-12-18

Issue

Vol. 20 No. 4 (2016): Thematic Issue: Research Advances and Applications of Evolutionary Computation (Guest Editors: H.J. Escalante, M. Graff)

Section

Articles of the Thematic Issue

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