A Social- And Knowledge-Based Coalition Formation Using Modified Combinatorial Particle Swarm Optimization
The 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 for...
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| フォーマット: | 学位論文 |
| 言語: | 英語 |
| 出版事項: |
2017
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| 主題: | |
| オンライン･アクセス: | http://eprints.usm.my/47901/ |
| 要約: | The 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. |
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