Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/315283 
Authors: 
Year of Publication: 
2024
Citation: 
[Journal:] Annals of Operations Research [ISSN:] 1572-9338 [Volume:] 341 [Issue:] 2 [Publisher:] Springer US [Place:] New York, NY [Year:] 2024 [Pages:] 1247-1265
Publisher: 
Springer US, New York, NY
Abstract: 
The Maximally Diverse Grouping Problem is one of the well-known combinatorial optimization problems with applications in the assignment of students to groups or courses. Due to its NP-hardness several (meta)heuristic solution approaches have been presented in the literature. Most of them include the insertion of an item of one group into another group and the swap of two items currently assigned to different groups as neighborhoods. The paper presents a new efficient implementation for both neighborhoods and compares it with the standard implementation, in which all inserts/swaps are evaluated, as well as the neighborhood decomposition approach. The results show that the newly presented approach is clearly superior for larger instances allowing for up to 160% more iterations in comparison to the standard implementation and up to 76% more iterations in comparison to the neighborhood decomposition approach. Moreover, the results can also be used for (meta)heuristic algorithms for other grouping or clustering problems.
Subjects: 
Combinatorial optimization
Grouping
Local search
Computational efficiency
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version

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