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In this paper, an algorithm, a particle filter algorithm optimized by combination of particle swarm and differential evolution, is proposed. Cooperative evolution model helps to produce the reasonable problem decompositions by exploiting any correlation, interdependency between components of the problem. Particle swarm optimization and differential evolution are used to evolve interactively to drive all the particles to the neighborhood regions where the likelihoods are high. The experiments demonstrate the novel particle filter is more effective.