Swarm, Evolutionary, and Memetic Computing: 6th by Bijaya Ketan Panigrahi, Ponnuthurai Nagaratnam Suganthan,

By Bijaya Ketan Panigrahi, Ponnuthurai Nagaratnam Suganthan, Swagatam Das, Suresh Chandra Satapathy

This quantity constitutes the completely refereed post-conference complaints of the sixth foreign convention on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2015, held in Hyderabad, India, in December 2015.
The 23 complete papers awarded during this quantity have been conscientiously reviewed and chosen from forty submissions for inclusion within the lawsuits. The papers disguise a variety of subject matters in swarm, evolutionary, memetic and different clever computing algorithms and their actual global purposes in difficulties chosen from various domain names of technology and engineering.

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Extra resources for Swarm, Evolutionary, and Memetic Computing: 6th International Conference, SEMCCO 2015, Hyderabad, India, December 18-19, 2015, Revised Selected Papers

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25(4), 481–494 (2004) 12. : Dual watermarking scheme for copyright protection. Int. J. Comput. Sci. Eng. Syst. 3(2), 99–106 (2009) 13. : A study of visual cryptography and its applications to copyright protection based on goal programming and statistics. D. Dissertation, National Central University, Department of Information Management, Taiwan (2004) 14. : A spatial domain public image watermarking. Int. J. Secur. Appl. 5(1), 1–11 (2011) 15. : Lossless watermarking technique for copyright protection of high resolution images.

It is to be observed that results of subjective analysis always vary with objective analysis. It is also noted that, selecting a watermark with more details improves successful subjective tracing and results poor performance. Comparative Analysis. To compare the practical performances of spatial domain VCLW schemes seven methods are implemented on 200 target images [25]. Figure 3 shows three different watermarks which are used in the implementations. Note that, for schemes with nonzero pixel expansion, NC values were calculated after resizing the extracted watermark to original size.

Example-based learning particle swarm optimization for continuous optimization. Inf. Sci. 182(1), 125–138 (2012) 10. : Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995) 11. : Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006) 12. : Teaching and peer-learning particle swarm optimization. Appl. Soft Comput. 18, 39–58 (2014) 13. : Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation.

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