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The task scheduling in real-time multiprocessor systems is to map tasks onto processors and order their execution so that the precedence relationships between tasks are maintained and the minimum schedule length is obtained. This is a well-known NP-completed problem. And many heuristic methods have existed, but their performance still needs to be improved. Particle swarm optimization has received much attention as a class of robust stochastic search algorithm for various optimization problems. This paper presents a novel task scheduling algorithm for real-time multiprocessor systems, which takes task's height and particle's position as the task's priority values, and applies the list scheduling strategy to generate the feasible solutions. Simulation results demonstrate that the proposed algorithm, compared with genetic algorithm, produces encouraging results in terms of quality of solution and time complexity.