Uninhabited combat air vehicle route path determining method based on PGSO (Particle-Glowworm Swarm Optimization) algorithm

Abstract

The invention discloses an uninhabited combat air vehicle route path determining method based on a PGSO (Particle-Glowworm Swarm Optimization) algorithm. The PGSO algorithm is designed by integrating a particle swarm optimization algorithm and a glowworm swarm optimization algorithm; a high-efficiency PGSO algorithm is designed by simulating a behavioural process of glowworms and establishing a swarm intelligence algorithm model under the inspiration of foraging or companion attracting courtship behaviors of the glowworms in nature through luminescence (fluorescein) and a bird swarm foraging behavior on the basis of a biological principle of a glowworm swarm in the nature; the high-efficiency PGSO algorithm is applied to determining of an uninhabited combat air vehicle route path; the uninhabited combat air vehicle route path determining method which is more excellent in performance is provided; a parallel hybrid mutation strategy and a strategy for performing local search close to the position of a global optimal individual are introduced into the PGSO, so that higher flight speed and positioning accuracy are achieved by finding the uninhabited combat air vehicle route path by utilizing the PGSO.

Description

technical field [0001] The invention belongs to the field of UAV path determination, in particular to a UAV route path determination method based on particle firefly swarm intelligent optimization algorithm. Background technique [0002] In the complex environment of modern warfare, UAVs (Uninhabited combat air vehicle; UCAV) have unparalleled advantages over conventional fighter jets. Today, drones have become a new attack method in precision air weapon attack systems, which can replace conventional fighter jets to complete a series of attack tasks in complex battlefield environments. UAV path planning is a new generation of low-altitude penetration technology for the purpose of terrain following, terrain avoidance and threat avoidance flight. UAV path planning is a key component of the mission planning system. Calculate optimal or suboptimal flight paths for UAVs that allow UAVs to break through hostile threat environments and survive on their own while accomplishing miss...

AI Technical Summary

Problems solved by technology

[0003] So far, no researchers have been found to apply the firefly swarm algorithm to the solution of UAV flight paths

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