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In this paper we present a combinatorial optimization method based on particle swarm optimization and stochastic local search concept. Under this method, in order to balance between exploration and exploitation, at each iteration step a local exploration performed around particles. The stochastic local search encourages the particle to explore local region beyond that defined by the search algorithm to achieve better solutions. The proposed method is assessed using a set of multimodal functions. Experimental results show that the proposed method outperforms other algorithms.