Particle swarm optimization method for air combat decision

Abstract

The invention discloses a particle swarm optimization method for an air combat decision, comprising the following steps of: firstly, acquiring the current situation of a battlefield from a command control center; secondly, acquiring a threat factor among aircrafts according to the current situation of the battlefield; thirdly, setting the particle swarm scale and the maximum iterations of the particle swarm; fourthly, initializing all particles of the particle swarm; fifthly, acquiring the threat degree of an enemy party on a first party after weapon attacks of the first part according to an empirical formula; sixthly, constructing a BP (Back Propagation) neural network; seventhly, updating the historical optimal position of the particle swarm and the individual historical optimal position of the particles; eighthly, continuously searching an air combat decision scheme until the maximum iterations of the particle swarm are achieved; and ninthly, determining the historical optimal position coordinate of the particle swarm as the obtained air combat decision. By processing the input and the output of the BP neural network, the decision method can move in a set solution space and hasfavorable search capability on the optimal solution.

Description

technical field [0001] The invention relates to a particle swarm optimization method for air combat decision-making, and belongs to the technical field of computer simulation and method optimization. Background technique [0002] Coordinated multi-target attack air combat decision-making has become one of the key technologies for modern fighters to realize beyond visual range air combat fire control system, and its research is of great significance. Multiple scattered targets. When the number of air targets increases sharply, we also need to dispatch a large number of aircraft to intercept and attack them at the same time, thus forming a group of aircraft coordinated air combat. The key to air combat decision-making for coordinated multi-target attack is to allocate targets for friendly aircraft according to our resources, and air combat situation assessment and threat analysis are the basis of target allocation. The core content of target attack air combat decision-making...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the shortcomings of the particle swarm optimization method for cooperative target allocation of cooperative multi-target attack air combat decision-making, such as local convergence, slow convergence speed and poor precision in the later stage of evolution, and propose a new type of particle swarm optimization method for air combat decision-making. The basic idea of ​​the particle swarm method, but the adjustment of the particle position is not based on the speed adjustment formula, but the BP neural network is used to make the particles continuously move to the global optimal solution

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