Particle　Swarm　Optimization　(PSO)　as　an　efficient　and　powerful　problem-solving　strategy　has　been　widely　used,　but　the　appropriate　adjustment　of　its　inertia　weight　usually　requires　a　lot　of　time　and　labor.　In　this　paper,　a　nonlinear　variation　strategy　to　inertia　weight　is　presented.　The　results　obtained　through　the　proposed　method　are　compared　with　existing　PSO　algorithms.　Finally,　the　simulation　results　show　that　the　proposed　method　can　provide　faster　convergence　and　optimal　solution　with　better　accuracy.