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A new human-based metaheuristic algorithm for solving optimization problems based on preschool education

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F23%3A50021144" target="_blank" >RIV/62690094:18470/23:50021144 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.nature.com/articles/s41598-023-48462-1" target="_blank" >https://www.nature.com/articles/s41598-023-48462-1</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1038/s41598-023-48462-1" target="_blank" >10.1038/s41598-023-48462-1</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    A new human-based metaheuristic algorithm for solving optimization problems based on preschool education

  • Original language description

    In this paper, with motivation from the No Free Lunch theorem, a new human-based metaheuristic algorithm named Preschool Education Optimization Algorithm (PEOA) is introduced for solving optimization problems. Human activities in the preschool education process are the fundamental inspiration in the design of PEOA. Hence, PEOA is mathematically modeled in three phases: (i) the gradual growth of the preschool teacher&apos;s educational influence, (ii) individual knowledge development guided by the teacher, and (iii) individual increase of knowledge and self-awareness. The PEOA&apos;s performance in optimization is evaluated using fifty-two standard benchmark functions encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, as well as the CEC 2017 test suite. The optimization results show that PEOA has a high ability in exploration–exploitation and can balance them during the search process. To provide a comprehensive analysis, the performance of PEOA is compared against ten well-known metaheuristic algorithms. The simulation results show that the proposed PEOA approach performs better than competing algorithms by providing effective solutions for the benchmark functions and overall ranking as the first-best optimizer. Presenting a statistical analysis of the Wilcoxon signed-rank test shows that PEOA has significant statistical superiority in competition with compared algorithms. Furthermore, the implementation of PEOA in solving twenty-two optimization problems from the CEC 2011 test suite and four engineering design problems illustrates its efficacy in real-world optimization applications.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database

  • CEP classification

  • OECD FORD branch

    50302 - Education, special (to gifted persons, those with learning disabilities)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2023

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Scientific reports

  • ISSN

    2045-2322

  • e-ISSN

    2045-2322

  • Volume of the periodical

    13

  • Issue of the periodical within the volume

    1

  • Country of publishing house

    DE - GERMANY

  • Number of pages

    31

  • Pages from-to

    "Article number: 21472"

  • UT code for WoS article

  • EID of the result in the Scopus database

    2-s2.0-85178850260