The traditional particle swarm algorithm has shortcomings, such as premature convergence and the inability to find the optimal solution when the problem is complex. Thus this paper analyzes the meanings of the terms in the position and velocity state transition formula and the absolute values of the terms as the search process progresses. On this basis, the transfer formula and initialization process in the standard particle swarm optimization algorithm are improved. Compared with the existing active target improvement methods, a particle swarm optimization algorithm based on a random attenuation factor is proposed. The methods used in the calculation of the optimal power flow of the power system are analyzed, including the N1 particle dimension setting method and the thought combined fitness function with the penalty function to control the possible out-of-limit situations in the optimization process. Then a more complete calculation process and optimization model is obtained. The IEEE-7, an actual 37-node network and IEEE-118-node network are used to verify the validity of the calculation method. This work is supported by the Science and Technology Project of State Grid Corporation of China “Research and Application of Key Technologies of Large Power Grid Pre-dispatch” (No. 5211UZ18006K).