Parallel dynamic search fireworks algorithm with linearly decreased dimension number strategy for solving conditional nonlinear optimal perturbation

Abstract

Intelligent algorithms have been applied extensively to solving conditional nonlinear optimal perturbation (CNOP), which is considered as an effective approach in the study of weather and climate predictability. Recent studies reveal that single particle intelligent algorithms could solve CNOP efficiently while their results are unstable. Conversely, swarm intelligent algorithms can get similar CNOP to adjoint method stably but still show lower time efficiency. For the purpose of balancing between efficiency and stability, in this paper, we introduce a linearly decreased dimension number strategy for dynamic search fireworks algorithm (dynFWA), called ld-dynFWA, and apply it to solving CNOP. Meanwhile, to accelerate the computation speed, we parallelize the ld-dynFWA with MPI. To demonstrate the validity, we take Zebiak-Cane (ZC) numerical model as a case to study ENSO event. Compared with CTS-SS and MABC, which are the latest research on single particle intelligent algorithm and swarm intelligent algorithm for solving CNOP respectively, experimental results show that the proposed parallel ld-dynFWA can obtain a better CNOP efficiently and stably.