By Kusum Deep, Jagdish Chand Bansal, Kedar Nath Das, Arvind Kumar Lal, Harish Garg, Atulya K. Nagar, Millie Pant
This two-volume e-book gathers the court cases of the 6th foreign convention on smooth Computing for challenge fixing (SocProS 2016), supplying a set of study papers provided in the course of the convention at Thapar college, Patiala, India. delivering a veritable treasure trove for scientists and researchers operating within the box of sentimental computing, it highlights the newest advancements within the vast zone of “Computational Intelligence” and explores either theoretical and useful points utilizing fuzzy good judgment, synthetic neural networks, evolutionary algorithms, swarm intelligence, gentle computing, computational intelligence, etc.
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Extra info for Proceedings of Sixth International Conference on Soft Computing for Problem Solving: SocProS 2016, Volume 1
Pal et al. The equation of motion of system equipped with a TMD is given in Eq. 3, where the parameters md , cd , kd were optimized by setting the range of the TMD parameters of the structure and then ﬁnding the best optimum solution of TMD parameters with the help of SOMGA and SOMAQI. 04 × 103 3 Case Studies Two examples from the existing literature are selected and the use of Hybrid SOMA method is applied on them for ﬁnding the optimum TMD parameters for them. 1 Case Study-1 Case Study 1 is on example taken from Singh et al.
While Termination condition is not met do (i) To hit target, new locations for group population is formulated with the help of self experience as well as local and group population experience, using Local Leader Phase (LLP). (ii) Relied on ﬁtness value of group members, employ greedy selection strategy. (iii) Assess probabilities P ri for all companions using equation (4). (iv) Generate new locations for each group companions, chosen by P ri , by self experience, global leader experience also consider experience of group member using Global Leader Phase (GLP).
33(9), 2716–2723 (2011) 2. : Device for damping of bodies. US Patent No 989, 958 (1911) 3. : Mechanical Vibrations, 3rd edn. McGraw-Hill, New York (1947) 4. : A method of estimating the parameters of tuned mass dampers for seismic applications. Earthq. Eng. Struct. Dyn. 26(6), 617–635 (1997) 5. : Tuned mass dampers for response control of torsional buildings. Earthq. Eng. Struct. Dyn. C. com Abstract. Spider Monkey Optimization (SMO) is a recent optimization method, which has drawn interest of researchers in diﬀerent areas because of its simplicity and eﬃciency.
Proceedings of Sixth International Conference on Soft Computing for Problem Solving: SocProS 2016, Volume 1 by Kusum Deep, Jagdish Chand Bansal, Kedar Nath Das, Arvind Kumar Lal, Harish Garg, Atulya K. Nagar, Millie Pant