Optimal parameter decision method under ship longitudinal motion multi-objective strategy

Abstract

The invention relates to an optimal parameter decision method under a ship longitudinal motion multi-objective strategy. If a customer wishes that the pitching parameter identification accuracy is high, an entity corresponding to the minimum value of a pitching fitting error objective function is output to serve as a parameter optimal solution; if the customer wishes that the heaving parameter identification accuracy is high, an entity corresponding to the minimum value of a heaving fitting error objective function is output to serve as a parameter optimal solution; if the customer wishes thatpitching and heaving need high-precision output simultaneously, an objective function matrix is treated as points on a two-dimensional plane to be arranged from small to large according to certain one dimension of data, and data on the other dimension follow to change positions; any point is selected, and all the points on the left side of the point are subjected to least square linear fitting toobtain the fitting error; and all the points on the right side of the point are also subjected to least square linear fitting to obtain the fitting error, the total error of the point is recorded asthe sum of the two fitting errors, an entity corresponding to the minimum point of the total error is taken as an optimal solution, and different optimal parameter output is selected according to different needs.

Description

technical field [0001] The invention relates to a method for decision-making of longitudinal motion parameters of a ship, in particular to a method for decision-making of optimal parameters under a multi-objective strategy for longitudinal motion of a ship, and relates to two major fields of multi-objective optimization and parameter fitting of ship motion. Background technique [0002] In multi-objective optimization problems, the optimization objectives are interconnected and constrained by each other, and the performance improvement of one optimization objective usually leads to the performance degradation of at least one other optimization objective, that is, it is difficult for multiple optimization objectives to obtain optimal results at the same time. Therefore, a better approach is to coordinate among multiple goals and find a compromise solution. [0003] Evolutionary algorithm is a kind of heuristic search algorithm that simulates the behavior of "survival of the f...

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Problems solved by technology

Therefore, evolutionary algorithms are especially suitable for solving optimization problems that cannot calculate gradient information or require a lot of overhead to calculate gradient information, the objective function is discontinuous, and cannot be solved by traditional optimization algorithms.

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