Introducing the Fractional Order Darwinian Particle Swarm Optimization

M. S. Couceiro, R. P. Rocha, N. M. Ferreira and J. T. Machado

Signal, Image and Video Processing, Special Issue on Fractional Signals and Systems, 6(3), pp. 343-350, Springer, ISSN 1863-1711 (Online) / 1863-1703 (Print), Sep. 2012.

Abstract

One of the most well-known bio-inspired algorithms used in optimization problems is the Particle Swarm Optimization (PSO) which basically consists on a machine-learning technique loosely inspired by birds flocking in search of food. More specifically, it consists of a number of particles that collectively move on the search space in search of the global optimum. The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the PSO using natural selection, or survival-of-the-fittest, to enhance the ability to escape from local optima. This paper firstly presents a survey on particle swarm optimization algorithms mainly focusing on the DPSO. Afterwards, a method for controlling the convergence rate of the DPSO using fractional calculus (FC) concepts is proposed. The fractional order (FO) optimization algorithm, denoted as FO-DPSO, is tested using several well-known functions and the relationship between the fractional order velocity and the convergence of the algorithm is observed.

Keywords ─ Fractional calculus, DPSO, evolutionary algorithm.

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BibTeX

@ARTICLE(Couceiro_et_al_12d,

AUTHOR = "Couceiro, M. S. and Rocha, R. P. and Ferreira, N. M. and Machado, J. T.",

TITLE = "Introducing the Fractional Order Darwinian Particle Swarm Optimization",

JOURNAL = "Signal, Image and Video Processing, Special Issue on Fractional Signals and Systems",

YEAR = "2012",

MONTH = "Sep.",

VOLUME = "6",

NUMBER = "3",

PAGES = "343-350"

)

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Last update: 21/12/2012