Multi-Swarm Particle Swarm Optimization Co-Evolution Algorithm based on Principal Component Analysis for Solving Conditional Nonlinear Optimal Perturbation

Conditional nonlinear optimal perturbation (CNOP) is an initial perturbation evolving into the largest nonlinear evolution at the prediction time. It has played an important role in predictability and sensitivity studies of nonlinear numerical models. Generally, the solution for CNOP is the spectral projecting gradient algorithm which is based on the adjoint model. However, many numercial models have no corresponding adjoint models and new implementations of these adjoint models cost tremendous engineering work. In this paper, we propose a multi-swarm PSO co-evolution algorithm base on principal component analysis to solve CNOP. To demonstrate the validity, the Zebiak-Cane model is utilized as a case to verify the proposed method. Experimental results show that the proposed method can be treated as an approximate solution to CNOP.