Proceedings of the International Conference on Soft - download pdf or read online

By Ashish Kumar Jain, Lini Mathew (auth.), Kusum Deep, Atulya Nagar, Millie Pant, Jagdish Chand Bansal (eds.)

ISBN-10: 8132204867

ISBN-13: 9788132204862

ISBN-10: 8132204875

ISBN-13: 9788132204879

The aim is to supply the newest advancements within the quarter of soppy computing. those are the innovative applied sciences that experience enormous program in a number of fields. the entire papers will endure the peer assessment strategy to take care of the standard of work.

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Read or Download Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011: Volume 1 PDF

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Extra info for Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011: Volume 1

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The memory allocation, objectswise, thread dump also gives a very clear view about the memory consumption for algorithm implementation. The Fig. 3 and 4 shows that, the SDPGA uses more CPU (~90 %) with single thread of execution, the MPDPGA uses CPU (~55 %) with multithreaded execution. This reduction in CPU usage is because of threaded implementation of DPGA by 35%, hence the utilization of CPU is optimal in case of MPDPGA over SDPGA. 93 Gigabytes out of maximum heap size of 64 Gigabytes. The MPDPGA usage of heap in percentage is 78% more than the heap utilization of SPDPGA.

3 and 4 shows that, the SDPGA uses more CPU (~90 %) with single thread of execution, the MPDPGA uses CPU (~55 %) with multithreaded execution. This reduction in CPU usage is because of threaded implementation of DPGA by 35%, hence the utilization of CPU is optimal in case of MPDPGA over SDPGA. 93 Gigabytes out of maximum heap size of 64 Gigabytes. The MPDPGA usage of heap in percentage is 78% more than the heap utilization of SPDPGA. 57 % of maximum heap size. J. S. Joshi Fig. 3. CPU usage, Heap usage, Classes loaeded and live threads of SDPGA It is observed that, MPDPGA uses heap more symmetric and systematic in comparision with SDPGA.

971–980. Springer, Heidelberg (2008) 9. : A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems. 2030331 10. : Evolutionary Computation. com Abstract. Particle Swarm optimization (PSO), one of the most popular natural computing paradigms, usually requires a large number of fitness evaluations to reach the global optimal solution when applied to some real world problems. Recently a new inertia weight strategy namely Globally Adaptive Inertia Weight (GAIW) has been introduced in PSO which deals with this problem to some extent.

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Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011: Volume 1 by Ashish Kumar Jain, Lini Mathew (auth.), Kusum Deep, Atulya Nagar, Millie Pant, Jagdish Chand Bansal (eds.)


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