[Objective] To effectively solve the environmental economic dispatch problem in power systems, this paper proposed an improved particle swarm optimization (PSO) algorithm based on grey wolf optimization (GWO) to optimize both the fuel costs and pollutant emissions. [Methods]First, the refracted opposition-based learning of refraction was applied to the initial particle swarm to generate inverse solutions, hereby enhancing population diversity. During the algorithm’s iteration process, the GWO algorithm was combined with PSO to guide the elite individuals in the particle swarm to conduct optimal searches, improving PSO’s optimization capability and convergence accuracy. In the later stages of the algorithm, to address the drawback where the particle swarm easily fell into local optima, Tent chaotic mapping was used to perturb the optimal particles. The individual best and global best positions of the particle swarm were then updated based on the fitness values. [Results] The improved algorithm was applied to 6-unit and 40-unit generator systems with different load demands. The convergence curves of the proposed algorithm, PSO algorithm. And GWO algorithm were compared for solving the power system, and the results showed that the proposed improved algorithm converged to the optimal value more quickly and resulted in the lowest fuel cost. [Conclusion] The improved algorithm proposed in this paper effectively solves complex constrained optimization problems and performs well in optimization accuracy and stability.