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

DOI

10.2991/icecee-15.2015.116How to use a DOI?

Keywords

PSO; Co-Evolution; CNOP; ZC Model

Abstract

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.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Li Zhao
PY  - 2015/06
DA  - 2015/06
TI  - Multi-Swarm Particle Swarm Optimization Co-Evolution Algorithm based on Principal Component Analysis for Solving Conditional Nonlinear Optimal Perturbation
BT  - Proceedings of the 2015 International Conference on Electrical, Computer Engineering and Electronics
PB  - Atlantis Press
SP  - 567
EP  - 572
SN  - 2352-538X
UR  - https://doi.org/10.2991/icecee-15.2015.116
DO  - 10.2991/icecee-15.2015.116
ID  - Zhao2015/06
ER  -