Article

Article title GLOBAL ROUTING ON THE BASIS OF ANT ALGORITHM
Authors O.B. Lebedev
Section SECTION II. AUTOMATION OF DESIGNING
Month, Year 07, 2011 @en
Index UDC 681.325
DOI
Abstract In work the method of the decision of a problem of global routing on the basis of ant algorithm is considered. Taking into account features of a problem of global routing the modified mechanisms of behaviour of ants and structure of space of decisions in which frameworks the search process which is based on modeling of adaptive behaviour of an ant colony is organized are developed. Distinctive feature of the presented algorithm of global trace, that is, the ant colony is broken on clusters and search of the concrete decision of a problem of a covering is carried out by collective of ants clusters. The basis of behaviour of an ant colony is made by the self-organizing providing achievements of overall aims of a colony on a basis of interaction inside clusters and between clusters. Experimental researches were spent on IBM PC. In comparison with existing algorithms improvement of results is reached.

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Keywords Swarm intelligence; ant colony; adaptive behaviour; self-organizing; global routing; opti- mization.
References 1. Деньдобренко Б.П., Малика А.С. Автоматизация проектирования радиоэлектронной аппаратуры. – М.: Высшая школа, 2002.
2. Alpert C.J., Mehta D.P., and Sapatnekar S.S. Handbook of Algorithms for Physical Design Automation. – Boston, MA: Auerbach, 2009.
3. Cho M., Xiang H., Puri R., and Pan D.Z. “Wire Density Driven Global Routing for CMP Variation and Timing,” in Proc. Int. Conf. on ComputerAided Design, Nov 2006.
4. Di Caro G., Ducatelle F., Gambardella L.M. AntHocNet: An adaptive nature-inspired algorithm for routing in mobile ad hoc networks // European Transactions on Telecommunications. – 2005. – № 16 (5). – C. 443-455.
5. Курейчик В.М. Биоинспирированный поиск с использованием сценарного подхода // Известия ЮФУ. Технические науки. – 2010. – № 7 (108). – C. 7-33.
6. Курейчик В.М., Лебедев Б.К., Лебедев О.Б. Поисковая адаптация: теория и практика. – М.: Физматлит, 2006.
7. Лебедев Б.К., Лебедев В.Б., Лебедев О.Б. Эволюционные механизмы трассировки в канале // Известия ЮФУ. Технические науки. – 2008. – № 9 (86). – С. 12-18.
8. Лебедев Б.К., Лебедев В.Б. Глобальная трассировка на основе роевого интеллекта // Известия ЮФУ. Технические нгауки. – 2010. – № 7 (108). – С. 32-39.
9. Курейчик В.В., Полупанова Е.Е. Эволюционная оптимизация на основе алгоритма колонии пчел // Известия ЮФУ. Технические науки. – 2009. – № 12 (101). – С. 41-46.
10. M. Dorigo and T. Stьtzle. Ant Colony Optimization. MIT Press, Cambridge, MA, 2004.
11. Курейчик В.В., Курейчик В.М., Родзин С.И. Концепция эволюционных вычислений, инспирированных природными системами // Известия ЮФУ. Технические науки. – 2009. – № 4 (93). – С. 16-25.

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