The paper presents an Adaptive Particle Swarm Optimization (APSO) algorithm that enhances the search efficiency and convergence speed of classical Particle Swarm Optimization (PSO). APSO introduces two main steps: evolutionary state estimation (ESE) and elitist learning strategy (ELS). ESE identifies one of four evolutionary states—exploration, exploitation, convergence, or jumping out—based on population distribution and particle fitness, allowing for adaptive control of inertia weight, acceleration coefficients, and other parameters. ELS, performed only when the state is classified as convergence, perturbs the globally best particle to escape local optima. The effectiveness of APSO is evaluated on 12 benchmark functions, demonstrating superior performance in terms of convergence speed, global optimality, solution accuracy, and algorithm reliability compared to other PSO variants. The introduction of APSO does not increase implementation complexity, making it a promising approach for improving PSO's performance.The paper presents an Adaptive Particle Swarm Optimization (APSO) algorithm that enhances the search efficiency and convergence speed of classical Particle Swarm Optimization (PSO). APSO introduces two main steps: evolutionary state estimation (ESE) and elitist learning strategy (ELS). ESE identifies one of four evolutionary states—exploration, exploitation, convergence, or jumping out—based on population distribution and particle fitness, allowing for adaptive control of inertia weight, acceleration coefficients, and other parameters. ELS, performed only when the state is classified as convergence, perturbs the globally best particle to escape local optima. The effectiveness of APSO is evaluated on 12 benchmark functions, demonstrating superior performance in terms of convergence speed, global optimality, solution accuracy, and algorithm reliability compared to other PSO variants. The introduction of APSO does not increase implementation complexity, making it a promising approach for improving PSO's performance.