Research Output

On the comparison of initialisation strategies in differential evolution for large scale optimisation

  Differential Evolution (DE) has shown to be a promising global opimisation solver for continuous problems, even for those with a large dimensionality. Different previous works have studied the effects that a population initialisation strategy has on the performance of DE when solving large scale continuous problems, and several contradictions have appeared with respect to the benefits that a particular initialisation scheme might provide. Some works have claimed that by applying a particular approach to a given problem, the performance of DE is going to be better than using others. In other cases however , researchers have stated that the overall performance of DE is not going to be affected by the use of a particular initialisation method. In this work, we study a wide range of well-known initialisation techniques for DE. Taking into account the best and worst results, statistically significant differences among considered initialisation strategies appeared. Thus, with the aim of increasing the probability of appearance of high-quality results and/or reducing the probability of appearance of low-quality ones, a suitable initialisation strategy, which depends on the large scale problem being solved, should be selected.

  • Type:

    Article

  • Date:

    19 January 2017

  • Publication Status:

    Published

  • DOI:

    10.1007/s11590-017-1107-z

  • ISSN:

    1862-4472

  • Library of Congress:

    QA Mathematics

  • Dewey Decimal Classification:

    519 Probabilities & applied mathematics

Citation

Segredo, E., Paechter, B., Segura, C. & González-Vila, C. I. (2017). On the comparison of initialisation strategies in differential evolution for large scale optimisation. Optimization Letters. , 1-13. doi:10.1007/s11590-017-1107-z. ISSN 1862-4472

Authors

Copyright

The final publication is available at Springer via http://dx.doi.org/10.1007/s11590-017-1107-z

Keywords

Differential evolution, initialisation strategy, large scale continuous optimisation

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