A Hybrid Chaotic Zebra Optimization Algorithm for Cost-Effective Healthcare Team Formation.
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| Title: | A Hybrid Chaotic Zebra Optimization Algorithm for Cost-Effective Healthcare Team Formation. |
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| Authors: | Aris, Nurul Aisyah1, Ikram, Raja Rina Raja1 raja.rina@utem.edu.my, Zamli, Kamal Z.2, Shair, Ezreen Farina3, Salahuddin, Lizawati1, Dzakiyullah, Nur Rachman4 nurrachmandzakiyullah@almaata.ac.id |
| Source: | International Journal of Online & Biomedical Engineering. 2025, Vol. 21 Issue 8, p56-74. 19p. |
| Subjects: | Optimization algorithms, Random sets, Resource allocation, Zebras, Problem solving |
| Abstract: | This paper presents a hybrid approach to enhancing the zebra optimization algorithm (ZOA) by integrating the chaotic map for cost-effective healthcare team formation. Healthcare team formation is one of the complex optimization problems that is essential in resource allocation, cost efficiency, and skill diversity. Traditional methods struggle to find optimal solutions, which makes the metaheuristic algorithm a valuable approach to solving complex challenges. Metaheuristic algorithms are inspired by natural and evolutionary processes and have been widely implemented in optimization problems due to their ability to explore large solution spaces and bring optimal solutions. Among these, ZOA has shown the ability to solve optimization problems where it is inspired by zebra natural behaviors, which face some limitations on diversity, exploration, and resource allocation, particularly in finding the best team formation by random skill set. The standard ZOA's randomization of data lacks strategic diversity, which leads to inefficient solutions and slower convergence. To overcome these limitations, the chaotic tent-map will be integrated with ZOA to improve the algorithm's exploration and heterogeneity or solution capabilities. The enhanced ZOA performance will be compared with the original ZOA and other metaheuristic algorithms. The performance of the improved algorithm is endorsed using real data information from expert doctors in Malaysia, displaying improved outcomes in terms of both cost efficiency and team formation size. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Online & Biomedical Engineering is the property of International Journal of Online Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 186266150 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Hybrid Chaotic Zebra Optimization Algorithm for Cost-Effective Healthcare Team Formation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Aris%2C+Nurul+Aisyah%22">Aris, Nurul Aisyah</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ikram%2C+Raja+Rina+Raja%22">Ikram, Raja Rina Raja</searchLink><relatesTo>1</relatesTo><i> raja.rina@utem.edu.my</i><br /><searchLink fieldCode="AR" term="%22Zamli%2C+Kamal+Z%2E%22">Zamli, Kamal Z.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Shair%2C+Ezreen+Farina%22">Shair, Ezreen Farina</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Salahuddin%2C+Lizawati%22">Salahuddin, Lizawati</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Dzakiyullah%2C+Nur+Rachman%22">Dzakiyullah, Nur Rachman</searchLink><relatesTo>4</relatesTo><i> nurrachmandzakiyullah@almaata.ac.id</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Online+%26+Biomedical+Engineering%22">International Journal of Online & Biomedical Engineering</searchLink>. 2025, Vol. 21 Issue 8, p56-74. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Random+sets%22">Random sets</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Zebras%22">Zebras</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper presents a hybrid approach to enhancing the zebra optimization algorithm (ZOA) by integrating the chaotic map for cost-effective healthcare team formation. Healthcare team formation is one of the complex optimization problems that is essential in resource allocation, cost efficiency, and skill diversity. Traditional methods struggle to find optimal solutions, which makes the metaheuristic algorithm a valuable approach to solving complex challenges. Metaheuristic algorithms are inspired by natural and evolutionary processes and have been widely implemented in optimization problems due to their ability to explore large solution spaces and bring optimal solutions. Among these, ZOA has shown the ability to solve optimization problems where it is inspired by zebra natural behaviors, which face some limitations on diversity, exploration, and resource allocation, particularly in finding the best team formation by random skill set. The standard ZOA's randomization of data lacks strategic diversity, which leads to inefficient solutions and slower convergence. To overcome these limitations, the chaotic tent-map will be integrated with ZOA to improve the algorithm's exploration and heterogeneity or solution capabilities. The enhanced ZOA performance will be compared with the original ZOA and other metaheuristic algorithms. The performance of the improved algorithm is endorsed using real data information from expert doctors in Malaysia, displaying improved outcomes in terms of both cost efficiency and team formation size. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Online & Biomedical Engineering is the property of International Journal of Online Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3991/ijoe.v21i08.54697 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 56 Subjects: – SubjectFull: Optimization algorithms Type: general – SubjectFull: Random sets Type: general – SubjectFull: Resource allocation Type: general – SubjectFull: Zebras Type: general – SubjectFull: Problem solving Type: general Titles: – TitleFull: A Hybrid Chaotic Zebra Optimization Algorithm for Cost-Effective Healthcare Team Formation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Aris, Nurul Aisyah – PersonEntity: Name: NameFull: Ikram, Raja Rina Raja – PersonEntity: Name: NameFull: Zamli, Kamal Z. – PersonEntity: Name: NameFull: Shair, Ezreen Farina – PersonEntity: Name: NameFull: Salahuddin, Lizawati – PersonEntity: Name: NameFull: Dzakiyullah, Nur Rachman IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 26268493 Numbering: – Type: volume Value: 21 – Type: issue Value: 8 Titles: – TitleFull: International Journal of Online & Biomedical Engineering Type: main |
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