An improved Particle Swarm Optimization (PSO) algorithm based on natural selection and nonlinear decreasing inertial weights is proposed to solve the problem of premature convergence and nonconvergence in PSO.In the process of algorithm iteration，the particle boundary velocity is limited by the nonlinear decreasing strategy of maximum velocity，and the nonlinear decreasing of inertia weight is used to balance the global search ability in the early stage and local optimization ability in the later stage.In order to keep the diversity of the population in the evolutionary process，the second order oscillation strategy is used in the standard particle swarm optimization to keep the diversity of the population in the evolutionary process.On this basis，the selection mechanism of genetic algorithm and particle swarm optimization are combined to improve the applicability of the algorithm.The proposed algorithm is verified by several benchmark functions and compared with other existing algorithms.Experimental results illustrated that this algorithm has more obvious advantages in search accuracy and optimization ability，especially in complex nonlinear optimization problems such as multidimensional and multipeak optimization，the proposed algorithm has a strong competitiveness.