Particle swarm optimization (PSO) is a metaheuristic algorithm that is easy to implement and performs well on various optimization problems. However, PSO is sensitive to initialization due to its rapid convergence which leads to the lack of population diversity and premature convergence. To solve this problem, a jumping-out strategy named crown jewel defense (CJD) is introduced in this paper. CJD is used to relocate the global best position and reinitializes all particles' personal best position when the swarm is trapped in local optima. Taking the advantage of CJD strategy, the swarm can jump out of the local optimal region without being dragged back and the performance of PSO becomes more robust to the initialization. Experimental results on benchmark functions show that the CJD-based PSO are comparable to or better than the other representative state-of-the-art PSO.