Dynamic ε-multilevel hierarchy constraint optimization with adaptive boundary constraint handling technology

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dc.contributor.authorLiu, Jinze
dc.contributor.authorFeng, Jian
dc.contributor.authorYang, Shengxiang
dc.contributor.authorZhang, Huaguang
dc.contributor.authorLiu, Shaoning
dc.date.acceptance2023-12-15
dc.date.accessioned2024-01-24T13:57:02Z
dc.date.available2024-01-24T13:57:02Z
dc.date.issued2023-12-20
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
dc.description.abstractReal-world optimization problems are often difficult to solve because of the complexity of the objective function and the large number of constraints that accompany it. To solve such problems, we propose Adaptive Dynamic ε-Multilevel Hierarchy Constraint Optimization (εMHCO). Firstly, we propose the dynamic constraint tolerance factor ε which can change dynamically with the feasible ratio and the number of iterations in the current population. This ensures a reasonable proportion of virtual feasible solutions in the population. Secondly, we propose adaptive boundary constraint handling technology (ABCHT). It can reshape the current individual position adaptively according to the size of constraint violation and increase the diversity of the population. Finally, we propose multi-level hierarchy optimization, whose multiple population structure is beneficial to solve real-world constraint optimization problems (COPs). To validate and analyze the performance of εMHCO, numerical experiments are conducted on the latest real-world test suite CEC’2020, which contains a set of 57 real-world COPs, and compared with four state-of-the-art algorithms. The results show that εMHCO is significantly superior to, or at least comparable to the state-of-the-art algorithms in solving real-world COPs. Meanwhile, the effectiveness and feasibility of εMHCO are verified on the real-world problem of the pipeline inner detector speed control.
dc.funderOther external funder (please detail below)
dc.funder.otherNational Natural Science Foundation of China
dc.funder.otherLiaoNing Revitalization Talents Program
dc.identifier.citationLiu, J., Feng, J., Yang, S., Zhang, H. and Liu, S. (2024) Dynamic ε-multilevel hierarchy constraint optimization with adaptive boundary constraint handling technology. Applied Soft Computing, 152, 111172
dc.identifier.doihttps://doi.org/10.1016/j.asoc.2023.111172
dc.identifier.urihttps://hdl.handle.net/2086/23489
dc.language.isoen
dc.peerreviewedYes
dc.projectidU22A2055, 62173081
dc.projectidXLYC2002032
dc.publisherElsevier
dc.researchinstituteInstitute of Artificial Intelligence (IAI)
dc.rightsAttribution-NonCommercial-NoDerivs 2.0 UK: England & Walesen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.0/uk/
dc.subjectMetaheuristic algorithm
dc.subjectAdaptive dynamic programming
dc.subjectConstraint optimization
dc.subjectSwarm intelligence
dc.subjectPipeline inner detector
dc.subjectSpeed control
dc.titleDynamic ε-multilevel hierarchy constraint optimization with adaptive boundary constraint handling technology
dc.typeArticle

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