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Evolutionary algorithms for large-scale global optimisation: a snapshot, trends and challenges

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Abstract

In the last years, several real-world problems that require to optimise an increasing number of variables have appeared. This type of optimisation, called large-scale global optimisation, is hard due to the huge increase of the domain search due to the dimensionality. Large-scale global optimisation is a research area getting more attention in the last years, thus many algorithms, mainly evolutionary algorithms, have been specially designed to tackle it. In this paper, we give a brief introduction of several of them and their techniques, remarking techniques based on grouping of variables and memetic algorithms, because they are two promising approaches. Also, we have reviewed the winners of the different competitions in the area, to give a snapshot of the algorithms that have obtained the best results in this area. Finally, several interesting trends in the research area have been pointed out, and some future trends and challenges have been suggested.

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Acknowledgments

This work was supported by the Spanish Ministry of Education and Science under Grants TIN2012-37930-C02-01, TIN2014-57251-P and Research Regional Projects P10-TIC-6858, P12-TIC-2958.

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  1. Department of Computer Science, University of Cádiz, Cádiz, Spain

    Daniel Molina Cabrera

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  1. Daniel Molina Cabrera
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Correspondence to Daniel Molina Cabrera.

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Cabrera, D.M. Evolutionary algorithms for large-scale global optimisation: a snapshot, trends and challenges. Prog Artif Intell 5, 85–89 (2016). https://doi.org/10.1007/s13748-016-0082-4

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