Improved PSO Algorithm to Defend against Acoustic Homing Torpedoes Using an Acoustic Decoy of a Submarine

HOU Wenshu; LU Minghua

doi:10.11993/j.issn.2096-3920.202205001

Volume 31 Issue 3

Jun 2023

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HOU Wenshu, LU Minghua. Improved PSO Algorithm to Defend against Acoustic Homing Torpedoes Using an Acoustic Decoy of a Submarine[J]. Journal of Unmanned Undersea Systems, 2023, 31(3): 436-441. doi: 10.11993/j.issn.2096-3920.202205001

Citation:

HOU Wenshu, LU Minghua. Improved PSO Algorithm to Defend against Acoustic Homing Torpedoes Using an Acoustic Decoy of a Submarine[J]. Journal of Unmanned Undersea Systems, 2023, 31(3): 436-441. doi: 10.11993/j.issn.2096-3920.202205001

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Improved PSO Algorithm to Defend against Acoustic Homing Torpedoes Using an Acoustic Decoy of a Submarine

doi: 10.11993/j.issn.2096-3920.202205001

Naval Submarine Academy, Qingdao 266199, China

Received Date: 2022-05-07
Accepted Date: 2023-05-18
Rev Recd Date: 2022-06-30

Available Online: 2023-05-25

Abstract

Abstract

A submarine that uses a single self-propelled acoustic decoy to defend against an S-type maneuver acoustic homing torpedo must immediately implement a defensive counterplan that achieves the maximum distance between the torpedo and itself . In this study, the effects of the number of particles in a swarm, number of iterations, upper limit of the particle velocity, acceleration factor, and initialization method of the particle swarm on a particle swarm optimization(PSO) algorithm based on a parallel calculation are analyzed to determine the improved direction. With an expanded upper limit of the particle velocity and regenerated particle swarm in the iterative procedure, the results showed that the modified PSO algorithm improved the simulation times with a fitness value greater than 7 500 m(a 95% improvement). The convergence of the algorithm was shown to be faster and the number of calculations remained the same. Thus, the overall efficiency of the solution was improved.
- submarine,
- self-propelled acoustic decoy,
- torpedo,
- particle swarm optimization algorithm

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References(15)

References

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