Mother optimization algorithm: a new human-based metaheuristic approach for solving engineering optimization

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F23%3A50020645" target="_blank" >RIV/62690094:18470/23:50020645 - isvavai.cz</a>

Alternative codes found

RIV/00216275:25310/23:39920751

Result on the web

<a href="https://www.nature.com/articles/s41598-023-37537-8" target="_blank" >https://www.nature.com/articles/s41598-023-37537-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-023-37537-8" target="_blank" >10.1038/s41598-023-37537-8</a>

Result language

angličtina

Original language name

Mother optimization algorithm: a new human-based metaheuristic approach for solving engineering optimization

Original language description

This article’s innovation and novelty are introducing a new metaheuristic method called mother optimization algorithm (MOA) that mimics the human interaction between a mother and her children. The real inspiration of MOA is to simulate the mother’s care of children in three phases education, advice, and upbringing. The mathematical model of MOA used in the search process and exploration is presented. The performance of MOA is assessed on a set of 52 benchmark functions, including unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, and the CEC 2017 test suite. The findings of optimizing unimodal functions indicate MOA’s high ability in local search and exploitation. The findings of optimization of high-dimensional multimodal functions indicate the high ability of MOA in global search and exploration. The findings of optimization of fixed-dimension multi-model functions and the CEC 2017 test suite show that MOA with a high ability to balance exploration and exploitation effectively supports the search process and can generate appropriate solutions for optimization problems. The outcomes quality obtained from MOA has been compared with the performance of 12 often-used metaheuristic algorithms. Upon analysis and comparison of the simulation results, it was found that the proposed MOA outperforms competing algorithms with superior and significantly more competitive performance. Precisely, the proposed MOA delivers better results in most objective functions. Furthermore, the application of MOA on four engineering design problems demonstrates the efficacy of the proposed approach in solving real-world optimization problems. The findings of the statistical analysis from the Wilcoxon signed-rank test show that MOA has a significant statistical superiority compared to the twelve well-known metaheuristic algorithms in managing the optimization problems studied in this paper.

Czech name

—

Czech description

—

Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

—

OECD FORD branch

50301 - Education, general; including training, pedagogy, didactics [and education systems]

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

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

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

26

Pages from-to

"Article Number: 10312"

UT code for WoS article

001059061400070

EID of the result in the Scopus database

2-s2.0-85163291552