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An improved particle swarm optimization (IPSO) algorithm was proposed. In the basic particle swarm optimization (PSO) algorithm, the tentative behavior of individuals and the mutation of velocity have been introduced, according to the law of evolutionary process. Using the single node adjustment algorithm, each particle searches the neighbor area by itself at every generation after general steps. In the evolution, the particles can escape from the local optimum with the mutation of velocity. This kind of enhanced study behavior accords with the biological natural law even more, and helps to find the global optimum solution with great chance. For solving traveling salesman problem, numerical simulation results for the benchmark TSP problems shows the effectiveness and efficiency of the proposed method.