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Particle swarm optimization (PSO) is a stochastic search technique for solving optimization problems, which has been proven to be efficient and effective in wide applications. However, the PSO can easily fly into the local optima and lack the ability to jump out of the local optima. A novel adaptive PSO is proposed by evaluating convergence of the fitness value. The novel mechanism is to ensure the diversity of particles. Simulations for benchmark test functions have illustrated that the proposed algorithm possesses better ability to find the global optima than other variants and is an effective global optimization tool.