According to the shortage that particle swarm optimization (PSO) algorithm easily falls into local optimum and premature convergence, a fuzzy self-correction particle swarm optimization algorithm is proposed. By using the fuzzy reasoning mechanism, a particle fitness membership function is established, which makes the particles base on their current fitness membership function values to modify the value of inertia weight in the process of optimization, instead of seeing the inertia weight as a global variable, then a generation of particles use the same inertia weight. This optimization fully considers the features of the particle itself, which can further improve the defect of prematurity, enhance the global search ability and get a better target value. The algorithm is used to solve the economic load distribution problems in power system, both considering fuel cost and environmental cost. In solving this problem, to exactly deal with power balance constraints, it uses the size of the deviation value of equality constraint in the optimization process to constantly adjust the value of penalty coefficients, and then establishes corresponding penalty function. Numerical example results show that the proposed algorithm has strong global search ability and more reliable optimization calculation results compared to the standard particle swarm algorithm, which shows the effectiveness and superiority of this method. This work is supported by National Natural Science Foundation of China (No. 51277016).