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Using Computational Intelligence Algorithms to Solve the Coalition Structure Generation Problem in coalitional skill games

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Journal of Computer Science & Technology 2016年第6期31卷 1136-1150页

作者： Yang Liu Guo-Fu Zhang Zhao-Pin Su Feng Yue Jian-Guo Jiang School of Computer and Information Hefei University of Technology Hefei 230009 China InteUigent Manufacturing Institute Hefei University of Technology Hefei 230009 China Department of Science and Technology Management Hefei University of Technology Hefei 230009 China

coalitional skill games （CSGs） are a simple model of cooperation in an uncertain environment where each agent has a set of skills that are required to accomplish a variety of tasks and each task requires a set of skills to be completed, but each skill is very hard to be quantified and can only be qualitatively expressed. Thus far, many computational questions surrounding CSGs have been studied. However, to the best of our knowledge, the coalition structure generation problem （CSGP）, as a central issue of CSGs, is extremely challenging and has not been well solved. To this end, two different computational intelligence algorithms are herein evaluated： binary particle swarm optimization （BPSO） and binary differential evolution （BDE）. In particular, we develop the two stochastic search algorithms with two-dimensional binary encoding and corresponding heuristic for individual repairs. After that, we discuss some fundamental properties of the proposed heuristic. Finally, we compare the improved BPSO and BDE with the state-of-the-art algorithms for solving CSGP in CSGs. The experimental results show that our algorithms can find the same near optimal solutions with the existing approaches but take extremely short time, especially under the large problem size.

关键词： coalitional skill game coalitional structure generation two-dimensional binary encoding heuristic individual repair

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