Download PDF by Mohammad Taheri, Elham Chitsaz, Seraj D. Katebi, Mansoor Z.: Advances in Computer Science and Engineering: 13th

By Mohammad Taheri, Elham Chitsaz, Seraj D. Katebi, Mansoor Z. Jahromi (auth.), Hamid Sarbazi-Azad, Behrooz Parhami, Seyed-Ghassem Miremadi, Shaahin Hessabi (eds.)

ISBN-10: 3540899847

ISBN-13: 9783540899846

ISBN-10: 3540899855

ISBN-13: 9783540899853

This ebook constitutes the revised chosen papers of the thirteenth foreign CSI machine convention, CSICC 2008 hung on Kish Island, Iran, in March 2008. The eighty four normal papers provided including sixty eight poster displays have been rigorously reviewed and chosen from a complete of 426 submissions.

The papers are geared up in topical sections on learning/soft computing, set of rules idea, SoC and NoC, wireless/sensor networks, video processing and similar subject matters, processor structure, AI/robotics/control, scientific picture processing, p2p/cluster/grid structures, cellular advert hoc networks, net, sign processing/speech processing, misc, safety, snapshot processing purposes in addition to VLSI.

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Extra info for Advances in Computer Science and Engineering: 13th International CSI Computer Conference, CSICC 2008 Kish Island, Iran, March 9-11, 2008 Revised Selected Papers

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IEEE Transactions on Systems, Man, and Cybernetics – Part B 26, 29–41 (1996) 3. : Solving symmetric and asymmetric TSPs by ant colonies. In: Proceedings of the 1996 IEEE International Conference on Evolutionary Computation (ICEC 1996), pp. 622–627. IEEE Press, Piscataway (1996) 4. : A hybrid algorithm applied to travelling salesman problem. In: 2004 IEEE International Conference on Networking, Sensing and Control, vol. 1, pp. 237–242 (2004) 5. : Improved Genetic and Ant Colony Optimization Algorithm for Regional Air Defense WTA Problem.

Some methods, like GK, have a dissimilarity function with limited capability to change it metrics but when the clusters have different sizes, it doesn't work out very well. In this section an algorithm has been proposed, which has flexible dissimilarity function. This function is a weighted sum of conventional dissimilarity functions of Mass, linear, and shell type fuzzy clustering. During the clustering, the weights of these functions change with respect to how much a cluster belongs to each type.

Dev. Std. % Avg. % Method Best Avg. 784 ACO with a Genetic Restart Approach toward Global Optimization 15 Table 2. Genetic G Semi-Random-Restart Performance Method Fixed heurisstic Flexible heurristic Avg. 3 Avg. 3 SF Survivor semi-random-restart iteration Fig. 5. Successful operaation of the posed restart algorithm to evade local optimums heuristic is the same as the first one without β modulation, and finally, No GA resstart represents the algorithm witthout any restart process. Table 1 shows a good performance p of our algorithm specially compared with the same procedure without restart algorithm.

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Advances in Computer Science and Engineering: 13th International CSI Computer Conference, CSICC 2008 Kish Island, Iran, March 9-11, 2008 Revised Selected Papers by Mohammad Taheri, Elham Chitsaz, Seraj D. Katebi, Mansoor Z. Jahromi (auth.), Hamid Sarbazi-Azad, Behrooz Parhami, Seyed-Ghassem Miremadi, Shaahin Hessabi (eds.)


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