Abstract
In recent years many real-world optimization problems have had to deal with growing dimensionality. Optimization problems with many hundreds or thousands of variables are called large-scale global optimization (LGSO) problems. The most advanced algorithms for LSGO are proposed for continuous problems and are based on cooperative coevolution schemes using the problem decomposition. In this paper a novel technique is proposed. A genetic algorithm is used as the core technique. The estimation of distribution algorithm is used for collecting statistical data based on the past search experience to provide the problem decomposition by fixing genes in chromosomes. Such an EDA-based decomposition technique has the benefits of the random grouping methods and the dynamic learning methods. The results of numerical experiments for benchmark problems from the CEC’13 competition are presented. The experiments show that the approach demonstrates efficiency comparable to other advanced algorithms.
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Acknowledgements
The research was supported by the President of the Russian Federation grant (MK-3285.2015.9).
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Sopov, E. (2016). Large-Scale Global Optimization Using a Binary Genetic Algorithm with EDA-Based Decomposition. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_62
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DOI: https://doi.org/10.1007/978-3-319-41000-5_62
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