Deep Q-Network-Enhanced Self-Tuning Control of Particle Swarm Optimization
Particle Swarm Optimization (PSO) is a widespread evolutionary technique that has successfully solved diverse optimization problems across various application fields. However, when dealing with more complex optimization problems, PSO can suffer from premature convergence and may become stuck in loca...
Saved in:
| Main Author: | Oussama Aoun |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Modelling |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-3951/5/4/89 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhancing Autonomous Driving With Spatial Memory and Attention in Reinforcement Learning
by: Matvey Gerasyov, et al.
Published: (2024-01-01) -
Visual tracking using interactive factorial hidden Markov models
by: Jin Wook Paeng, et al.
Published: (2021-08-01) -
When to monitor or control: Informed invasive species management using a partially observable Markov decision process (POMDP) framework
by: Thomas K. Waring, et al.
Published: (2024-09-01) -
Soft Actor-Critic Approach to Self-Adaptive Particle Swarm Optimisation
by: Daniel von Eschwege, et al.
Published: (2024-11-01) -
Modelling and Intelligent Decision of Partially Observable Penetration Testing for System Security Verification
by: Xiaojian Liu, et al.
Published: (2024-12-01)