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Using spatial neighborhoods for parameter adaptation: An improved success history based differential evolution

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F22%3A63556499" target="_blank" >RIV/70883521:28140/22:63556499 - isvavai.cz</a>

  • Alternative codes found

    RIV/61989100:27740/22:10249925 RIV/61989100:27240/22:10249925

  • Result on the web

    <a href="https://www.sciencedirect.com/science/article/pii/S2210650222000293?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2210650222000293?via%3Dihub</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.swevo.2022.101057" target="_blank" >10.1016/j.swevo.2022.101057</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Using spatial neighborhoods for parameter adaptation: An improved success history based differential evolution

  • Original language description

    Differential Evolution (DE) has been widely appraised as a simple yet robust population-based, non-convex optimization algorithm primarily designed for continuous optimization. Two important control parameters of DE are the scale factor F, which controls the amplitude of a perturbation step on the current solutions and the crossover rate Cr, which limits the mixing of components of the parent and the mutant individuals during recombination. We propose a very simple, yet effective, nearest spatial neighborhood-based modification to the adaptation process of the aforesaid parameters in the Success-History based adaptive DE (SHADE) algorithm. SHADE uses a historical archive of the successful F and Cr values to update these parameters and stands out as a very competitive DE variant of current interest. Our proposed modifications can be extended to any SHADE-based DE algorithm like L-SHADE (SHADE with linear population size reduction), jSO (L-SHADE with modified mutation) etc. The enhanced performance of the modified SHADE algorithm is showcased on the IEEE CEC (Congress on Evolutionary Computation) 2013, 2014, 2015, and 2017 benchmark suites by comparing against the DE-based winners of the corresponding competitions. Furthermore, the effectiveness of the proposed neighborhood-based parameter adaptation strategy is demonstrated by using the real-life problems from the IEEE CEC 2011 competition on testing evolutionary algorithms on real-world numerical optimization problems. © 2022

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Swarm and Evolutionary Computation

  • ISSN

    2210-6502

  • e-ISSN

    2210-6510

  • Volume of the periodical

    71

  • Issue of the periodical within the volume

    Neuveden

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    13

  • Pages from-to

    1-13

  • UT code for WoS article

    000795579900002

  • EID of the result in the Scopus database

    2-s2.0-85129522048