By Kusum Deep, Jagdish Chand Bansal, Kedar Nath Das, Arvind Kumar Lal, Harish Garg, Atulya K. Nagar, Millie Pant
This two-volume ebook gathers the court cases of the 6th foreign convention on tender Computing for challenge fixing (SocProS 2016), providing a suite of study papers awarded throughout the convention at Thapar college, Patiala, India. supplying a veritable treasure trove for scientists and researchers operating within the box of sentimental computing, it highlights the newest advancements within the extensive sector of “Computational Intelligence” and explores either theoretical and sensible points utilizing fuzzy common sense, man made neural networks, evolutionary algorithms, swarm intelligence, delicate computing, computational intelligence, etc.
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Additional info for Proceedings of Sixth International Conference on Soft Computing for Problem Solving: SocProS 2016, Volume 1
Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. : Fast multi-scale detection of relevant communities in large-scale networks. Comput. J. : Multi-level learning based memetic algorithm for community detection. Appl. Soft Comput. : The Ant lion optimizer. Adv. Eng. Softw. : The structure and function of complex networks. SIAM Rev. : Fast algorithm for detecting community structure in networks. Phys. Rev. : Finding community structure in networks using the eigenvectors of matrices.
The algorithm used modularity criteria as objective function and Normalized Mutual Information (NMI) for measuring the performance of algorithm. The algorithm produced promising results in implementation for synthetic as well as real world data set. The authors (Amiri et al. 2013) proposed a multicriteria optimization approach that utilizes EFF algorithm Fuzzy- based grouping and mutation techniques for the detection of network communities. The implementation and testing of EFA on real world and other synthetic data networks showed its efﬁciency in ﬁnding different communities in large networks.
Eng. Manage. 6(3), 319–329 (2015) 13. : A modiﬁed particle swarm optimizer. In: The 1998 IEEE International Conference on Evolutionary Computation Proceedings, 1998, IEEE World Congress on Computational Intelligence, pp. 69–73. IEEE (1998) 14. : Parameter selection in particle swarm optimization. E. ) EP 1998. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998). 1007/BFb0040810 15. : Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008) 16. : Particle swarm optimiser with neighbourhood operator.