Particle Swarm Optimization (PSO) is a stochastic and population-based adaptive optimization algorithm. Although the optimized PSO models have good search performance with moderate computational cost and accuracy, they still tend to be trapped in local minima (premature convergence) in solving multimodal optimization problems. To overcome this difficulty, we propose a new method, Particle Swarm Optimization with Diversive Curiosity (PSO/DC). A key idea of the proposed method is to introduce a mechanism of diversive curiosity into PSO for preventing premature convergence, and for managing the exploration-exploitation trade-off. Diversive curiosity is represented by an internal indicator that detects marginal improvement of a swarm of particles, and forces them to continually exploring an optimal solution to a given optimization problem. Owing to the internal indicator representing the mechanism of diversity curiosity, PSO/DC can successfully prevent premature convergence, and manage the exploration-exploitation trade-off. Empirically, PSO/DC is very effective in enhancing the search performance of PSO.