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Soft Computing

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01.11.2011 | Focus

Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems

verfasst von: Hui Wang, Zhijian Wu, Shahryar Rahnamayan

Erschienen in: Soft Computing | Ausgabe 11/2011

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Abstract

This paper presents a novel algorithm based on generalized opposition-based learning (GOBL) to improve the performance of differential evolution (DE) to solve high-dimensional optimization problems efficiently. The proposed approach, namely GODE, employs similar schemes of opposition-based DE (ODE) for opposition-based population initialization and generation jumping with GOBL. Experiments are conducted to verify the performance of GODE on 19 high-dimensional problems with D = 50, 100, 200, 500, 1,000. The results confirm that GODE outperforms classical DE, real-coded CHC (crossgenerational elitist selection, heterogeneous recombination, and cataclysmic mutation) and G-CMA-ES (restart covariant matrix evolutionary strategy) on the majority of test problems.

Vorheriger Artikel Role differentiation and malleable mating for differential evolution: an analysis on large-scale optimisation

Nächster Artikel Scalability of generalized adaptive differential evolution for large-scale continuous optimization

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Titel: Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems
verfasst von: Hui Wang
Zhijian Wu
Shahryar Rahnamayan
Publikationsdatum: 01.11.2011
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 11/2011
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-010-0642-7

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