Research Output

On the performance of the hybridisation between migrating birds optimisation variants and differential evolution for large scale continuous problems

  Migrating Birds Optimisation (mbo) is a nature-inspired approach which has been shown to be very effective when solving a variety of combinatorial optimisation problems. More recently, an adaptation of the algorithm has been proposed that enables it to deal with continuous search spaces. We extend this work in two ways.
Firstly, a novel leader replacement strategy is proposed to counter the slow convergence of the existing mbo algorithms due to low selection pressure. Secondly, mbo is hybridised with adaptive neighbourhood operators borrowed from Differential Evolution (de) that promote exploration and exploitation. The new variants are tested on two sets of continuous large scale optimisation problems. Results show that mbo variants using adaptive, exploration-based operators outperform de on the cec benchmark suite with 1000
variables. Further experiments on a second suite of 19 problems show that mbo variants outperform de on 90% of these test-cases.

  • Type:

    Article

  • Date:

    21 February 2018

  • Publication Status:

    Published

  • DOI:

    10.1016/j.eswa.2018.02.024

  • Cross Ref:

    S0957417418301106

  • ISSN:

    0957-4174

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    518 Numerical analysis

  • Funders:

    Edinburgh Napier Funded

Citation

Voß, S., Segredo, E., Lalla-Ruiz, E., Hart, E., & Voss, S. (2018). On the performance of the hybridisation between migrating birds optimisation variants and differential evolution for large scale continuous problems. Expert Systems with Applications, 102, (126-142). doi:10.1016/j.eswa.2018.02.024. ISSN 0957-4174

Authors

Keywords

migrating birds optimization; differential evolution; large scale continuous problem; global optimization; leader replacement strategy; continuous neighborhood search

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