A Novel Context-Free Grammar to Guide the Construction of Particle Swarm Optimization Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Pericles B. C. Miranda and Ricardo B. C. Prudencio",
  booktitle =    "2016 5th Brazilian Conference on Intelligent Systems
  title =        "A Novel Context-Free Grammar to Guide the Construction
                 of Particle Swarm Optimization Algorithms",
  year =         "2016",
  pages =        "295--300",
  abstract =     "Particle Swarm Optimisation algorithm (PSO) has been
                 largely studied over the years due to its flexibility
                 and competitive results in different applications.
                 Nevertheless, its performance depends on different
                 aspects of design (e.g., inertia factor, velocity
                 equation, topology). The task of deciding which is the
                 best algorithm design to solve a particular problem is
                 challenging due to the great number of possible
                 variations and parameters to take into account. This
                 work proposes a novel context-free grammar for
                 Grammar-Guided Genetic Programming (GGGP) algorithms to
                 guide the construction of Particle Swarm Optimizers.
                 The proposed grammar addresses four aspects of the PSO
                 algorithm that may strongly influence on its
                 convergence: swarm initialization, neighbourhood
                 topology, velocity update equation and mutation
                 operator. To evaluate this approach, a GGGP algorithm
                 was set with the proposed grammar and applied to
                 optimise the PSO algorithm in 32 unconstrained
                 continuous optimisation problems. In the experiments,
                 we compared the designs generated considering the
                 proposed grammar with the designs produced by other
                 grammars proposed in the literature to automate PSO
                 designs. The results obtained by the proposed grammar
                 were better than the counterparts. Besides, we also
                 compared the generated algorithms to state-of-art
                 algorithms. The results have shown that the algorithms
                 produced from the grammar achieved competitive
  keywords =     "genetic algorithms, genetic programming, PSO",
  DOI =          "doi:10.1109/BRACIS.2016.061",
  month =        oct,
  notes =        "Also known as \cite{7839602}",

Genetic Programming entries for Pericles Barbosa Miranda Ricardo Bastos Cavalcante Prudencio