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Automated search of an optimal configuration of FETI-based algorithms with the swarm and evolutionary algorithms

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10256344" target="_blank" >RIV/61989100:27240/24:10256344 - isvavai.cz</a>

  • Alternative codes found

    RIV/61989592:15410/24:73628329 RIV/61989100:27740/24:10256344

  • Result on the web

    <a href="https://www.sciencedirect.com/science/article/pii/S1568494624012110?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1568494624012110?via%3Dihub</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.asoc.2024.112437" target="_blank" >10.1016/j.asoc.2024.112437</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Automated search of an optimal configuration of FETI-based algorithms with the swarm and evolutionary algorithms

  • Original language description

    Finite Element Tearing and Interconnecting (FETI) methods are used in the engineering community to solve extremely large engineering simulations on clusters and supercomputers with thousands of computational nodes. This paper focuses on minimizing the execution time of these methods by searching for an optimal configuration with swarm and evolutionary algorithms (SEAs). It incorporates optimization of an expensive cost function (taking tens to hundreds of seconds) of discrete search space with up to hundreds of thousands of combinations constrained by its boundaries and incompatible individuals (invalid combination of FETI solver parameters). In addition, the optimization occurs in real time, i.e., during a simulation. Hence, the number of objective function evaluations must remain low (tens to lower hundreds). The paper compares the performance of 3 basic SEAs with a reduced population size (Differential Evolution, Particle Swarm Optimization, and Self-Organizing Migrating Algorithm), 4 micro SEAs (Micro Differential Evolution Ray, Improved Micro-Particle Swarm Optimization, Micro-Particle Swarm Optimization, and Micro-Genetic Algorithm), and a random search on 4 distinct time-dependent simulations of a heat transfer. The experiments show that basic SEAs with a small population (about 5 individuals) and a penalty system as protection against incompatible configurations represent the most effective solution. One can use them to find the optimal configuration of FETI-based methods in approximately 130 evaluations. It can improve the utilization of expensive hardware resources of modern computational clusters.

  • 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

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2024

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Applied Soft Computing

  • ISSN

    1568-4946

  • e-ISSN

    1872-9681

  • Volume of the periodical

    167

  • Issue of the periodical within the volume

    December

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    16

  • Pages from-to

  • UT code for WoS article

    001361443400001

  • EID of the result in the Scopus database

    2-s2.0-85209370135