In this paper, we propose a method for using particle swarm optimization (PSO) to compute optimal guidance paths for various crowd densities in an agent-based crowd simulation. The inputs of our system are guidance paths that provide hints for the movement directions of agents. Input guidance paths may not be located correctly (e.g., leading to congestion or high traveling cost); therefore, our method adjusts the guidance paths by using PSO. We consider several factors for evaluating the quality of a guidance path, including the average traveling time and interaction distance between agents. We apply our method in several examples. Experimental results show that our method can compute adaptive guidance paths for various crowd densities. Our system can simulate organized crowds that move in directions specified by the guidance paths.

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