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https://elib.vku.udn.vn/handle/123456789/3997Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Dang, Dai Tho | - |
| dc.contributor.author | Truong, Hai Bang | - |
| dc.contributor.author | Nguyen, Ngoc Thanh | - |
| dc.date.accessioned | 2024-07-30T01:53:21Z | - |
| dc.date.available | 2024-07-30T01:53:21Z | - |
| dc.date.issued | 2023-09 | - |
| dc.identifier.isbn | 978-3-031-41456-5 | - |
| dc.identifier.uri | https://doi.org/10.1007/978-3-031-41456-5_1 | - |
| dc.identifier.uri | https://elib.vku.udn.vn/handle/123456789/3997 | - |
| dc.description | International Conference on Computational Collective Intelligence (ICCCI 2023); Lecture Notes in Computer Science (LNAI,volume 14162); pp: 3-15. | vi_VN |
| dc.description.abstract | Determining consensus for a set of ordered partitions (or a collective) is used for making decisions. Ordered partitions are a helpful structure for representing the opinions of experts or agents. Algorithms to determine 1-Optimality consensus were introduced in the literature. However, no algorithm has yet to be proposed for determining the 2-Optimality consensus. Determining 2-Optimality consensus for a collective of ordered partitions is an NP-hard problem. In this study, first, we present a mathematical formula for determining such a collective. Then, three hybrid genetic algorithms are proposed to solve this problem. The simulation results show that the HG3 algorithm finds the best quality consensus in an acceptable time. | vi_VN |
| dc.language.iso | en | vi_VN |
| dc.publisher | Springer Nature | vi_VN |
| dc.subject | Hybrid Genetic Algorithms | vi_VN |
| dc.title | Hybrid Genetic Algorithms to Determine 2-Optimality Consensus for a Collective of Ordered Partitions | vi_VN |
| dc.type | Working Paper | vi_VN |
| Appears in Collections: | NĂM 2023 | |
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