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Ant colony optimization technique for macrocell overlap removal

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2 Author(s)
Alupoaei, S. ; Dept. of Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL, USA ; Katkoori, S.

We present a novel macrocell overlap removal algorithm, based on the ant colony optimization (ACO) metaheuristic. The algorithm generates a feasible placement from a relative placement with overlaps produced by some placement algorithms such as quadratic programming and force directed. It uses the concept of ant colonies, a set of agents that work together to improve an existing solution. Each ant in the colony will generate a placement based on the relative positions of the cells and feedback information about the best placements generated by previous colonies. The solution of each ant is improved by using a local optimization procedure.

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VLSI Design, 2004. Proceedings. 17th International Conference on

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