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MRMOGA: a new parallel multi-objective evolutionary algorithm based on the use of multiple resolutions

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2007年第4期19卷 397-441页

作者： Lopez Jaimes, Antonio Coello Coello, Carlos A. CINVESTAV IPN Evolutionary Computat Grp EVOCINVDept Intn ElectSecc Computac Mexico City 07360 DF Mexico

In this paper, we introduce MRMOGA (multiple Resolution multi-objective Genetic Algorithm), a new parallel multi-objective evolutionary algorithm which is based on an injection island approach. This approach is characterized by adopting an encoding of solutions which uses a different resolution for each island. This approach allows us to divide the decision variable space into well-defined overlapped regions to achieve an efficient use of multiple processors. Also, this approach guarantees that the processors only generate solutions within their assigned region. In order to assess the performance of our proposed approach, we compare it to a parallel version of an algorithm that is representative of the state-of-the-art in the area, using standard test functions and performance measures reported in the specialized literature. Our results indicate that our proposed approach is a viable alternative to solve multi-objective optimization problems in parallel, particularly when dealing with large search spaces. Copyright (c) 2006 John Wiley & Soris, Ltd.

关键词： parallel processing multi-objective evolutionary algorithms parallel multi-objective evolutionary algorithms multi-objective optimization evolutionary algorithms

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A parallel Implementation of a multi-objective evolutionary Algorithm

A Parallel Implementation of a Multi-objective Evolutionary ...

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9th International Conference on Information Technology and Applications in Biomedicine

作者： Kannas, Christos C. Nicolaou, Christos A. Pattichis, Constantinos S. Univ Cyprus Noesis Chemoinformat CY-1678 Nicosia Cyprus Univ Cyprus Dept Comp Sci CY-1678 Nicosia Cyprus Univ Cyprus Cyprus Inst Noesis Chemoinformat CY-1678 Nicosia Cyprus

ISBN: (纸本)9781424453788

multi-objective evolutionary algorithms (MOEAs) have features that can be exploited to harness the processing power offered by modern multi-core CPUs. Modern programming languages offer the ability to use threads and processes in order to achieve parallelism that is inherent in multi-core CPUs. In this paper we present our parallel implementation of a MOEA algorithm and its application to the de novo drug design problem. The results indicate that using multiple processes that execute independent tasks of a MOEA, can reduce significantly the execution time required and maintain comparable solution quality thereby achieving improved performance.

关键词： evolutionary algorithms multi-objective evolutionary algorithms parallel evolutionary algorithms parallel multi-objective evolutionary algorithms parallel Processing

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