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{"title":"Comparative Study of Ant Colony and Genetic Algorithms for VLSI Circuit Partitioning","authors":"Sandeep Singh Gill, Rajeevan Chandel, Ashwani Chandel","volume":28,"journal":"International Journal of Electrical and Computer Engineering","pagesStart":1149,"pagesEnd":1154,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/14013","abstract":"<p>This paper presents a comparative study of Ant Colony and Genetic Algorithms for VLSI circuit bi-partitioning. Ant colony optimization is an optimization method based on behaviour of social insects [27] whereas Genetic algorithm is an evolutionary optimization technique based on Darwinian Theory of natural evolution and its concept of survival of the fittest [19]. Both the methods are stochastic in nature and have been successfully applied to solve many Non Polynomial hard problems. Results obtained show that Genetic algorithms out perform Ant Colony optimization technique when tested on the VLSI circuit bi-partitioning problem.<\/p>\r\n","references":"[1] Eugene L. Lawler, Karl N. Levitt, and James Turner, \"Module\r\nClustering to minimize delay in Digital Networks\", IEEE Transactions\r\non Computers, Vol. C-18 , No.1, pp. 47-57,Jan, 1969.\r\n[2] B.W. Kerhinghan, S. Lin, \"An efficient heuristic procedure for\r\npartitioning graphs\", Bell System Tech. Journal, Vol. 49, pp. 291 - 307,\r\nFeb,1970.\r\n[3] D.G. Schweikert and B.W. Kernighan, \"A proper model for the\r\npartitioning of electrical circuits,\" Proc. ACM\/IEEE Design Automation\r\nWorkshop, pp. 57-62, 1972.\r\n[4] C.M. Fiduccia and Mattheyses, \"A Linear time heuristic for improving\r\nnetwork partitions\", Proc. 19th IEEE Design and Automation\r\nConference, IEEE Press, Piscataway, NJ, USA , pp. 175-182, 1982..\r\n[5] B. 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