Abstract
Parallel computing, network partitioning, and VLSI circuit placement are fundamental challenges in computer science. These problems can be modeled as graph partitioning problems. A new Similarity carrying Ant Model (SCAM) is used in the ant-based clustering algorithm to solve graph partitioning problem. In the proposed model, the ant will be able to collect similar items while it moves around. The flexible template mechanism had been used integrated with the proposed model to obtain the partitioning constrains. Random graph has been used to compare the new model with the original ant model and the model with short-term memory. The result of the experiments proves the impact of the SCAM compared with other models. This performance improvement for ant clustering algorithm makes it is feasible to be used in graph portioning problem.
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Soliman, M.S., Tan, G. (2010). Graph Partitioning Using Improved Ant Clustering. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13495-1_29
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DOI: https://doi.org/10.1007/978-3-642-13495-1_29
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