A Social- And Knowledge-Based Coalition Formation Using Modified Combinatorial Particle Swarm Optimization

Simple item page
dc.contributor.authorKassim, Azleena Mohd
dc.date.accessioned2020-11-16T03:09:59Z
dc.date.available2020-11-16T03:09:59Z
dc.date.issued2017-12
dc.description.abstractThe thesis main objective is to develop a new framework for social- and knowledge-based coalition formation (SKCF). The related sub-objectives are: 1) to define coalition and social factors to form a coalition formation model, 2) to develop a knowledge representation scheme to store knowledge of formed coalitions, and 3) to develop an effective algorithm to optimize the coalition which can also be treated as a clustering problem. In order to realize these objectives, the coalition factors are compiled from existing coalition formation work, whereas social factors are chosen to satisfy the coalition’s payoff to address the selfish agent approach.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/10710
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectSocial- And Knowledge-Based Coalition Formationen_US
dc.subjectModified Combinatorial Particle Swarm Optimizationen_US
dc.titleA Social- And Knowledge-Based Coalition Formation Using Modified Combinatorial Particle Swarm Optimizationen_US
dc.typeThesisen_US
Files
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Pusat Pengajian Sains Komputer - Tesis