SciELO - Scientific Electronic Library Online

vol.18 issue1Eyelid Detection Method Based on a Fuzzy Multi-Objective OptimizationEffects of Interpolation on Segmentation in Cell Imaging author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand




Related links

  • Have no similar articlesSimilars in SciELO


Computación y Sistemas

Print version ISSN 1405-5546


NAPOLES, Gonzalo; GRAU, Isel; BELLO, Marilyn  and  BELLO, Rafael. Towards Swarm Diversity: Random Sampling in Variable Neighborhoods Procedure Using a Lévy Distribution. Comp. y Sist. [online]. 2014, vol.18, n.1, pp.79-95. ISSN 1405-5546.

Particle Swarm Optimization (PSO) is a non-direct search method for numerical optimization. The key advantages of this metaheuristic are principally associated to its simplicity, few parameters and high convergence rate. In the canonical PSO using a fully connected topology, a particle adjusts its position by using two attractors: the best record stored for the current agent, and the best point discovered for the entire swarm. It leads to a high convergence rate, but also progressively deteriorates the swarm diversity. As a result, the particle swarm frequently gets attracted by sub-optimal points. Once the particles have been attracted to a local optimum, they continue the search process within a small region of the solution space, thus reducing the algorithm exploration. To deal with this issue, this paper presents a variant of the Random Sampling in Variable Neighborhoods (RSVN) procedure using a Lévy distribution, which is able to notably improve the PSO search ability in multimodal problems.

Keywords : Swarm diversity; local optima; premature convergence; RSVN procedure; Lévy distribution.

        · abstract in Spanish     · text in English     · English ( pdf )


Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License