%0 Journal Article %A M. Farshbaf and M. R. Feizi-Derakhshi %D 2009 %J International Journal of Computer and Information Engineering %B World Academy of Science, Engineering and Technology %I Open Science Index 29, 2009 %T Multi-objective Optimization of Graph Partitioning using Genetic Algorithm %U https://publications.waset.org/pdf/11319 %V 29 %X Graph partitioning is a NP-hard problem with multiple conflicting objectives. The graph partitioning should minimize the inter-partition relationship while maximizing the intra-partition relationship. Furthermore, the partition load should be evenly distributed over the respective partitions. Therefore this is a multiobjective optimization problem (MOO). One of the approaches to MOO is Pareto optimization which has been used in this paper. The proposed methods of this paper used to improve the performance are injecting best solutions of previous runs into the first generation of next runs and also storing the non-dominated set of previous generations to combine with later generation's non-dominated set. These improvements prevent the GA from getting stuck in the local optima and increase the probability of finding more optimal solutions. Finally, a simulation research is carried out to investigate the effectiveness of the proposed algorithm. The simulation results confirm the effectiveness of the proposed method. %P 1444 - 1449