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's educational influence, (ii) individual knowledge development guided by the teacher, and (iii) individual increase of knowledge and self-awareness. The PEOA'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
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
50302 - Education, special (to gifted persons, those with learning disabilities)
Result continuities
Project
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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
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EID of the result in the Scopus database
2-s2.0-85178850260