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Collaborative team formation using brain drain optimization: a practical and effective solution

机译:利用人才外流进行协作的团队组建:实用有效的解决方案

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In the area of professional social networks, collaborative team formation with its NP-hard nature, has attracted the attention of many researchers. The purpose of this study is to find a collaborative team which covers required skills and minimizes the communication cost among team members. To solve this problem, BRADO (BRAin Drain Optimization), a recently-proposed meta-heuristic swarm-based algorithm which simulates the brain drain phenomenon, has been utilized. In order to evaluate BRADO, it has been applied in extensive experiments to the DBLP and IMDb datasets. Results demonstrate the effectiveness and superiority of the BRADO algorithm in comparison with PSO, GA, ICA, RarestFirst and EnhancedSteiner algorithms. Our findings lead us to believe that the BRADO algorithm can be a promising method in the context of team formation problem.
机译:在专业社交网络领域,具有NP硬性的协作团队的形成引起了许多研究人员的关注。这项研究的目的是要找到一个协作团队,该团队涵盖所需技能并最大程度地减少团队成员之间的沟通成本。为了解决这个问题,已经利用了最近提出的基于元启发式群体的BRADO(BRAin Drain Optimization)算法,该算法模拟了人才流失现象。为了评估BRADO,它已在广泛的实验中应用于DBLP和IMDb数据集。结果证明了与PSO,GA,ICA,RarestFirst和EnhancedSteiner算法相比,BRADO算法的有效性和优越性。我们的发现使我们相信,在团队形成问题的背景下,BRADO算法可能是一种有前途的方法。

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