A new population partitioning strategy is employed to PSO algorithm. In the current population, global neighborhood exploration strategy is presented to enhance the global exploration capability. A local learning mechanism is used to improve local exploitation ability in historical best population. Stochastic learning and opposition based learning operations are employed to accelerate convergence speed and improve optimization accuracy in global best population. IGPSO performs better for engineering design optimization problems.