<p>This paper proposes the progressive learning particle swarm optimization algorithm by leveraging the strengths of the social learning particle swarm optimization algorithm and designing three novel strategies. First, a golden section-based progressive learning strategy is designed to enhance local exploitation capabilities while maintaining population diversity. Second, a greedy strategy is presented to balance exploration and exploitation by utilizing the strong exploratory nature of the social learning particle swarm optimization in the early stage and the local exploitation of the proposed golden section-based strategy. Third, a jump-out strategy is developed to mine the guide particles with superior progress to help stagnant particles jump out of the local optimal region. Finally, experimental studies are conducted, and the results show that the proposed algorithm has strong competitiveness.</p>