Method for optimally solving weapon-target allocation based on particle swarm optimization

Abstract

A method for optimally solving weapon-target distribution based on particle swarm optimization uses a particle swarm optimization algorithm, and is characterized by comprising the following steps: S1,determining a weapon target distribution model; S2, generating a large number of feasible solutions, and using a genetic algorithm and an affinity propagation algorithm to detect a local optimal region of the feasible solutions; S3, initializing a particle swarm optimization algorithm population; and S4, carrying out particle swarm optimization algorithm optimization. The method for solving weapon target allocation based on particle swarm optimization provided by the invention has strong global search capability, can effectively avoid falling into local optimization, can quickly search an optimal solution, can further improve the quality of the solution, and can effectively solve the problem of overlong optimization time consumption in an algorithm in the prior art.

Description

technical field [0001] The invention belongs to the field of computer simulation and method optimization, and relates to a method for optimizing weapon-target allocation based on particle swarm algorithm. Background technique [0002] Weapon-Target Allocation (WTA) is a key link in combat command, which directly affects the process of combat and victory or defeat. It is an important military issue that all military powers are competing to study. Weapon target assignment mainly includes two stages. The first stage: projectile-target matching, that is, for a certain type of target, analyze the damage mechanism of weapons and ammunition, whether the guidance method is applicable, whether the range can cover the target when launching from a preset position or projecting outside the defense zone, and whether the environment around the target meets the requirements of the weapon. The attack conditions of the ammunition, etc., select the type of weapon and ammunition suitable for ...

AI Technical Summary

Problems solved by technology

Although these improved algorithms have accelerated the speed of jumping out of local optimization to a certain extent, they still cannot effectively solve the problem that the optimization time is too long when the WTA problem is large.

The objective function of the WTA problem is complex, and the search space grows exponentially with the number of targets, the number of weapon types, and the number of various types of weapons. The space range is huge, and it is an NP-complete problem. It is difficult to obtain the location of its local optimal area through arithmetic operations.

In the case of various targets and many types of weapons that can be selected, the existing algorithm is easy to fall into the local optimum or the calculation of the local optimum takes too long, and it is difficult to obtain a better solution.

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