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.
A comparative study of three GPU-based metaheuristics / Youssef SG, Nashed; Pablo, Mesejo; Roberto, Ugolotti; Jeremie Dubois, Lacoste; Cagnoni, Stefano. - STAMPA. - 7492:2(2012), pp. 398-407. (Intervento presentato al convegno XII Parallel Problem Solving from Nature Conference tenutosi a Taormina nel Settembre 2012) [10.1007/978-3-642-32964-7].
A comparative study of three GPU-based metaheuristics
CAGNONI, Stefano
2012-01-01
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.File | Dimensione | Formato | |
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