Abstract

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 2007-9737.  https://doi.org/10.13053/CyS-18-1-2014-020.

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.

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