A new human-inspired metaheuristic algorithm for solving optimization problems based on mimicking sewing training

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019634" target="_blank" >RIV/62690094:18470/22:50019634 - isvavai.cz</a>
Result on the web
<a href="https://www.nature.com/articles/s41598-022-22458-9" target="_blank" >https://www.nature.com/articles/s41598-022-22458-9</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41598-022-22458-9" target="_blank" >10.1038/s41598-022-22458-9</a>

Result language
angličtina
Original language name
A new human-inspired metaheuristic algorithm for solving optimization problems based on mimicking sewing training
Original language description
This paper introduces a new human-based metaheuristic algorithm called Sewing Training-Based Optimization (STBO), which has applications in handling optimization tasks. The fundamental inspiration of STBO is teaching the process of sewing to beginner tailors. The theory of the proposed STBO approach is described and then mathematically modeled in three phases: (i) training, (ii) imitation of the instructor's skills, and (iii) practice. STBO performance is evaluated on fifty-two benchmark functions consisting of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and the CEC 2017 test suite. The optimization results show that STBO, with its high power of exploration and exploitation, has provided suitable solutions for benchmark functions. The performance of STBO is compared with eleven well-known metaheuristic algorithms. The simulation results show that STBO, with its high ability to balance exploration and exploitation, has provided far more competitive performance in solving benchmark functions than competitor algorithms. Finally, the implementation of STBO in solving four engineering design problems demonstrates the capability of the proposed STBO in dealing with real-world applications.
Czech name
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Czech description
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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
50301 - Education, general; including training, pedagogy, didactics [and education systems]

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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