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首页> 外文会议>International Conference on Electrical, Computer Engineering and Electronics >Multi-Swarm Particle Swarm Optimization Co-Evolution Algorithm based on Principal Component Analysis for Solving Conditional Nonlinear Optimal Perturbation
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Multi-Swarm Particle Swarm Optimization Co-Evolution Algorithm based on Principal Component Analysis for Solving Conditional Nonlinear Optimal Perturbation

机译：基于主成分分析的多群粒子群优化共进算法，用于解决条件非线性扰动的主成分分析

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摘要

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.
机译：条件非线性最佳扰动（CNOP）是在预测时间的最大非线性演化中发展的初始扰动。它在非线性数值模型的可预测性和敏感性研究中发挥了重要作用。通常，CNOP的解决方案是基于伴随模型的光谱突出梯度算法。然而，许多数字模型没有相应的伴随模型和这些伴随模型的新实现成本巨大的工程工作。在本文中，我们提出了一种关于主成分分析的多群PSO共同演进算法来解决CNOP。为了证明有效性，Zebiak-Cane模型被用作验证所提出的方法的情况。实验结果表明，该方法可作为CNOP作为近似解决方案。

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