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

dc.contributor.authorMatoušová, Ivana
dc.contributor.authorTrojovský, Pavel
dc.contributor.authorDehghani, Mohammad
dc.contributor.authorTrojovská, Eva
dc.contributor.authorKostra, Juraj
dc.date.accessioned2025-12-05T13:02:13Z
dc.date.available2025-12-05T13:02:13Z
dc.date.issued2023
dc.description.abstractThis 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.eng
dc.formatp. &quot;Article Number: 10312&quot;
dc.identifier.doi10.1038/s41598-023-37537-8
dc.identifier.issn2045-2322
dc.identifier.obd43880255
dc.identifier.urihttp://hdl.handle.net/20.500.12603/1870
dc.language.isoeng
dc.peerreviewedyes
dc.publicationstatuspostprint
dc.publisherNATURE PORTFOLIO
dc.relation.ispartofScientific reports, volume 13, issue: 1eng
dc.relation.publisherversionhttps://www.nature.com/articles/s41598-023-37537-8
dc.rights.accessOpen Access
dc.rights.licenseCC BY
dc.source.urlhttps://www.nature.com/articles/s41598-023-37537-8
dc.subjectchildeng
dc.subjectcontrolled studyeng
dc.subjecteducationeng
dc.subjectexploration exploitation tradeoffeng
dc.subjecthumaneng
dc.subjectmathematical modeleng
dc.subjectmetaheuristicseng
dc.subjectsimulationeng
dc.titleMother optimization algorithm: a new human-based metaheuristic approach for solving engineering optimizationeng
dc.typearticle

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