Abstract： A particle filter based on differential evolution with improved fitness function and search strategy was proposed to solve the problem of the low precision and slow convergence rate of particle filters based on intelligent optimization algorithms. The algorithm defined a new fitness function based on adaptive fusion of particle weight and its measurement error. The function was used to evaluate the credibility of the particles and move them to positions with larger values of posterior density function. Synchronously, a new search strategy was introduced to differential evolution which could maintain the diversity of the particles and accelerate the convergence rate of the particle filter. Experiment results show that the proposed algorithm effectively improves the accuracy and realtime performance of the intelligent optimization particle filter for nonlinear system state estimation.