Sea skimming aircraft tail end flight parameter design method

Abstract

The invention discloses a sea skimming aircraft tail end flight parameter design method. The method comprises the steps of obtaining sea skimming aircraft tail end flight scheme parameters and a target function; a quantum seagull algorithm is adopted, the tail end flight parameters are set and optimized by solving the maximum value of the target function, and the optimal flight parameters are determined; the determination of the quantum seagull algorithm comprises the following steps: on the basis of a standard seagull algorithm, initializing a seagull population by adopting Tent chaotic mapping, and determining the state of the quantum seagull by adopting a quantum bit probability amplitude coding mode. According to the method, the Tent chaotic mapping and the quantum theory are fused to improve the sea gull algorithm, each quantum sea gull state represents two positions in a parameter optimization space and correspondingly optimizes two solutions of an objective function, the global search efficiency of the algorithm is improved, and the convergence speed of the algorithm is increased.

Description

technical field [0001] The invention belongs to the technical field of aviation, and in particular relates to a design method for flight parameters of an aircraft terminal. Background technique [0002] The terminal flight safety of sea-skimming aircraft is affected by the environment, enemy detection system, interception system, etc. The parameters of the terminal flight plan will affect the enemy's interception effect and its own flight safety at the same time. The terminal survival probability of sea-skimming aircraft is nonlinear and strongly coupled. , multiple local extremum and other problems, it is difficult to design through the traditional gradient search algorithm. Therefore, an intelligent cluster algorithm with global optimization capability is used for optimal parameter design. [0003] Seagull Optimization Algorithm (SOA) was proposed by Dhiman in 2019 based on the gregarious lifestyle of seagulls. It is an intelligent optimization algorithm constructed by si...

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Problems solved by technology

[0004] However, the standard seagull algorithm still has some defects: although it has global search capabilities, it also has problems such as poor late convergence efficiency, poor population diversity, and easy to fall into local convergence.

The above studies have improved the performance of the sea-gull algorithm to a certain extent, but there are still problems such as the decline of convergence performance in the later period, the low precision of the global optimization results, and the tendency to fall into local optimum. From the perspective of terminal survivability, it is necessary to greatly improve the seagull algorithm to obtain the optimal flight parameter design scheme

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