Parameters’ Identification of Vessel Based on Ant Colony Optimization Algorithm

Abstract

In this paper, the ant colony optimization (ACO) method is used to identify the parameters of a 3-DOF nonlinear vessel model. Identifying the parameters is abstracted as a nonlinear optimization problem to solve through the ant colony optimization algorithm. The identification procedure is divided into two parts. The first part of the identification procedure is to identify the parameters related to surge motion. The second part of the identification procedure is to identify the rest parameters of the vessel’s kinetics model. In the surge model identification procedure, the transient motor speed is used to generate the training data, and in the sway and yaw motion identification procedure, the zigzag maneuvering with different motor speeds is used to generate the training data. All the parameters are identified by the ACO method and the least-square (LS) method based on the training data and then validated on the validation data. The prediction performance of parameters identified by different methods is compared in the simulation to demonstrate the effectiveness of the ACO algorithm.

Integration of Green Innovation Capabilities of Enterprises Based on Ant Colony Optimization Algorithm

“…Based on the process of the ant colony algorithm solving optimization problems, compared with other intelligent optimization algorithms, it is not difficult to see some advantages of the ant colony algorithm: Information positive feedback mechanism [ 12 , 13 ]: this mechanism can make the ant's search process converge continuously and obtain the best solution after repeated cycles [ 14 ]. The release and volatilization of pheromones [ 15 ]: each ant can change the surrounding environment by releasing pheromone and indirectly convey information to other companions, and the volatilization of pheromones can speed up the determination of the optimal path [ 16 , 17 ].…”

“…Information positive feedback mechanism [ 12 , 13 ]: this mechanism can make the ant's search process converge continuously and obtain the best solution after repeated cycles [ 14 ].…”

Research on the application of artificial intelligence algorithm in radar signal processing of unmanned boat

“…Therefore, when countering the detection of anti-submarine patrol aircraft, on the one hand, it is necessary to reduce the radar cross-section of unmanned boats as much as possible, and at the same time, to carry out combat missions with high sea conditions (above Level 4). On the other hand, it is necessary to control the distribution of radar wave scattering sources throughout the ship, weaken the imaging characteristics of the inverse synthetic aperture of unmanned boats, and reduce the probability of being recognized remotely [7][8] .…”