Parameter Identification Of Roll Motion Equation Of Ship In Regular Wave Using Opposition Based Learning Gaussian Bare Bone Imperialist Competition Algorithm

In this paper, a new method is proposed to identify the parameters of roll motion equation using the response data of ship in regular waves. The proposed method is based on the fact that if the estimated parameters are close to the true parameters, then, its response are also close to the true response. Therefore, the parameter identification is achieved via minimizing the mean square error between the true response and the estimated response. To effectively solving the parameter minimization problem, an improved imperialist competition algorithm (ICA), called opposition based learning Gaussian bare bone imperialist competition algorithm (OBL-GBBICA) is proposed. The proposed OBL-GBBICA integrates the opposition learning and Gaussian sampling technique into ICA to enhance its exploration ability and speed up its convergence. Experimental results show that the proposed method can accurately identify the parameters of roll motion equation. (c) 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.