Abstract: To solve the local-optima convergence problem of particle swarm optimization，two new methods are introduced to modify the Particle Swarm Optimization inertia weight in parallel：When particles’ fitness values are better than or equal to the average，the introduced dynamic nonlinear equations are employed to modify the inertia weight，which can make particles retain favorable conditions and converge to the global optima continually；on the contrary，when fitness values are worse than the average，the dynamic Logistic chaotic map formula is introduced to modify the inertia weight，which can make particles break away from the local optima and search the global optima dynamically.Two methods coordinate dynamically，and make two dynamic sub-swarms cooperate to evolve.Experimental results demonstrate that the new introduced algorithm outperforms many other famous improved Particle Swarm Optimization algorithms on many well-known benchmark problems.