A Comparative Study of Three GPU-Based Metaheuristics

Youssef S.G. Nashed¹, Pablo Mesejo¹, Roberto Ugolotti¹, Jérémie Dubois-Lacoste², and Stefano Cagnoni¹

¹Department of Information Engineering, University of Parma, Italy
nashed@ce.unipr.it
pmesejo@ce.unipr.it
rob_ugo@ce.unipr.it
cagnoni@ce.unipr.it

²IRIDIA, CoDE, Université Libre de Bruxelles, Belgium
jeremie.dubois-lacoste@ulb.ac.be

Abstract. In this paper we compare GPU-based implementations of three metaheuristics: Particle Swarm Optimization, Differential Evolution, and Scatter Search. A GPU-based implementation, obviously, does not change the general properties of the algorithms. As well, we give for granted that GPU-based implementation of both algorithm and fitness function produces a significant speed-up with respect to a sequential implementation. Accordingly, the main goal of this work has been to fairly assess the efficiency of the GPU-based implementations of the three metaheuristics, based on the statistical analysis of the results they obtain in optimizing a benchmark of twenty functions within a prefixed limited time.

Keywords: Global Continuous Optimization, Particle Swarm Optimization, Differential Evolution, Scatter Search, GPGPU

LNCS 7492, p. 398 ff.

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