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

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dc.rights.license CC BY eng
dc.contributor.author Matoušová, Ivana cze
dc.contributor.author Trojovský, Pavel cze
dc.contributor.author Dehghani, Mohammad cze
dc.contributor.author Trojovská, Eva cze
dc.contributor.author Kostra, Juraj cze
dc.date.accessioned 2025-12-05T13:02:13Z
dc.date.available 2025-12-05T13:02:13Z
dc.date.issued 2023 eng
dc.identifier.issn 2045-2322 eng
dc.identifier.uri http://hdl.handle.net/20.500.12603/1870
dc.description.abstract 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. eng
dc.format p. "Article Number: 10312" eng
dc.language.iso eng eng
dc.publisher NATURE PORTFOLIO eng
dc.relation.ispartof Scientific reports, volume 13, issue: 1 eng
dc.subject child eng
dc.subject controlled study eng
dc.subject education eng
dc.subject exploration exploitation tradeoff eng
dc.subject human eng
dc.subject mathematical model eng
dc.subject metaheuristics eng
dc.subject simulation eng
dc.title Mother optimization algorithm: a new human-based metaheuristic approach for solving engineering optimization eng
dc.type article eng
dc.identifier.obd 43880255 eng
dc.identifier.doi 10.1038/s41598-023-37537-8 eng
dc.publicationstatus postprint eng
dc.peerreviewed yes eng
dc.source.url https://www.nature.com/articles/s41598-023-37537-8 cze
dc.relation.publisherversion https://www.nature.com/articles/s41598-023-37537-8 eng
dc.rights.access Open Access eng


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