Title: High performance computing for dynamic multi-objective optimisation

Authors: Mario Camara, Julio Ortega, Francisco De Toro

Addresses: Department of Computer Architecture and Technology, University of Granada, ETSIIT, C/ Daniel Saucedo, s/n, 18071, Granada, Spain. ' Department of Computer Architecture and Technology, University of Granada, ETSIIT, C/ Daniel Saucedo, s/n, 18071, Granada, Spain. ' Department of Signal Theory, Telematics and Communications, University of Granada, ETSIIT, C/ Daniel Saucedo, s/n, 18071, Granada, Spain

Abstract: In this paper a generic parallel procedure for dynamic problems using evolutionary algorithms is presented. In dynamic multi-objective problems, the objective functions, the constraints and hence, also the solutions, can change over time and usually demand to be solved online. Thus, high performance computing approaches, such as parallel processing, should be applied to these problems to meet the solution constraints and quality requirements. Taking this into account, we introduce a generic parallel procedure for multi-objective evolutionary algorithms, through a master-slave paradigm. This generic parallel procedure is used to compare the parallel processing of a few multi-objective optimisation evolutionary algorithms: our proposed algorithms, SFGA and SFGA2, in conjunction with SPEA2 and NSGA-II. We also give a model to understand the benefits of parallel processing in dynamic multi-objective problems and the speedup results observed in our experiments.

Keywords: high performance computing; dynamic multi-objective optimisation; DMO; parallel processing; evolutionary algorithms; dynamic environments; parallel optimisation.

DOI: 10.1504/IJHPSA.2008.024208

International Journal of High Performance Systems Architecture, 2008 Vol.1 No.4, pp.241 - 250

Published online: 29 Mar 2009 *

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