Abstract: To overcome the premature convergence and low searching accuracy of the particle swarm optimization (PSO) algorithm, we propose a population diversity-based particle swarm optimization algorithm (PDPSO). First, we introduce the nonlinear characteristics of particles by the population diversity to describe the distribution state in the searching process. Second, we develop an adaptive inertia weight adjustment strategy to balance the global exploration ability and the local exploitation ability based on the population diversity of particles. Finally, we test the performance of PDPSO by using the standard test functions. We use the proposed PDPSO algorithm to optimize the energy consumption of the wastewater treatment process. Unlike the standard PSO algorithm and other improved PSO algorithms, the proposed PDPSO algorithm can avoid being trapped in the local optimum and achieve high accuracy, as demonstrated by simulation results. The proposed PDPSO algorithm can optimize the wastewater treatment process to reduce energy consumption within effluent water qualities.