By Hamid Sarbazi-Azad, Behrooz Parhami, Seyed-Ghasem Miremadi, Shaahin Hessabi

This e-book constitutes the revised chosen papers of the thirteenth foreign CSI laptop convention, CSICC 2008 hung on Kish Island, Iran, in March 2008. The eighty four standard papers awarded including sixty eight poster displays have been conscientiously reviewed and chosen from a complete of 426 submissions.

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

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Extra resources 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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Improved Genetic and Ant Colony Optimization Algorithm for Regional Air Defense WTA Problem. In: First International Conference on Innovative Computing, Information and Control (ICICIC 2006), pp. 226–229 (2006) 6. : Revisiting Elitism in Ant Colony Optimization. K. ) GECCO 2003. LNCS, vol. 2723, pp. 122–133. Springer, Heidelberg (2003) 7. : A new rank-based version of the Ant System: A computational study. Central European Journal for Operations Research and Economics 7, 25–38 (1999) 8. : The MAX–MIN Ant System and local search for the traveling salesman problem.

H. Hajimirsadeghi, M. N. Araabi AS Local Best Tour (AS-LBT) [6], is another improved kind of AS, in which only local information is used to reinforce trails. It means that each ant updates its trail by the best tour it has found to date. This approach shows more diversity than AS. Some other improvements in the field of ACO are the Multiple Ant Colonies Algorithms [9], which exploits interactions between colonies, Population-based ACO (PACO), which makes up a special population of good solutions, and Omicron ACO (OA), which is inspired by MMAS and elitist strategy.

N. Araabi the problem. A genetic inspired random-restart is added to the ACO algorithm as a survivor approach to refrain from local maxima. In section 2, Multiplicative Square is introduced, while optimization algorithms, including ACO and local search methods are described in section 3. Next in section 4, our methodology is explained and the results are summarized in section 5. Finally, conclusions are drawn in section 6. 2 Problem Description of Multiplicative Squares The Multiplicative Squares are a class of squares filled by the numbers 1 to n that the products of their rows, columns diagonals, and broken diagonals have a special feature.

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