A Continuous Reinforcement Learning Approach to Self-Adaptive Particle Swarm Optimisation
| dc.contributor.author | Tilley, Duncan | |
| dc.contributor.supervisor | Cleghorn, Christopher | |
| dc.date.accessioned | 2024-11-15T20:49:38Z | |
| dc.date.available | 2024-11-15T20:49:38Z | |
| dc.date.issued | 2023-08 | |
| dc.description | A dissertation submitted in fulfilment of the requirements for the degree of Master of Science (in Computer Science), to the Faculty of Science, School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, 2023. | |
| dc.description.abstract | Particle Swarm Optimisation (PSO) is a popular black-box optimisation technique due to its simple implementation and surprising ability to perform well on various problems. Unfortunately, PSO is fairly sensitive to the choice of hyper-parameters. For this reason, many self-adaptive techniques have been proposed that attempt to both simplify hyper-parameter selection and improve the performance of PSO. Surveys however show that many self-adaptive techniques are still outperformed by time-varying techniques where the value of coefficients are simply increased or decreased over time. More recent works have shown the successful application of Reinforcement Learning (RL) to learn self-adaptive control policies for optimisers such as differential evolution, genetic algorithms, and PSO. However, many of these applications were limited to only discrete state and action spaces, which severely limits the choices available to a control policy, given that the PSO coefficients are continuous variables. This dissertation therefore investigates the application of continuous RL techniques to learn a self-adaptive control policy that can make full use of the continuous nature of the PSO coefficients. The dissertation first introduces the RL framework used to learn a continuous control policy by defining the environment, action-space, state-space, and a number of possible reward functions. An effective learning environment that is able to overcome the difficulties of continuous RL is then derived through a series of experiments, culminating in a successfully learned continuous control policy. The policy is then shown to perform well on the benchmark problems used during training when compared to other self-adaptive PSO algorithms. Further testing on benchmark problems not seen during training suggest that the learned policy may however not generalise well to other functions, but this is shown to also be a problem in other PSO algorithms. Finally, the dissertation performs a number of experiments to provide insights into the behaviours learned by the continuous control policy. | |
| dc.description.submitter | MMM2024 | |
| dc.faculty | Faculty of Science | |
| dc.identifier | 0000-0001-6041-0464 | |
| dc.identifier.citation | Tilley, Duncan. (2023). A Continuous Reinforcement Learning Approach to Self-Adaptive Particle Swarm Optimisation. [Master's dissertation, University of the Witwatersrand, Johannesburg]. https://hdl.handle.net/10539/42617 | |
| dc.identifier.uri | https://hdl.handle.net/10539/42617 | |
| dc.language.iso | en | |
| dc.publisher | University of the Witwatersrand, Johannesburg | |
| dc.rights | ©2023 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg. | |
| dc.rights.holder | University of the Witwatersrand, Johannesburg | |
| dc.school | School of Computer Science and Applied Mathematics | |
| dc.subject | Particle Swarm Optimisation | |
| dc.subject | Self-Adaptive PSO | |
| dc.subject | Reinforcement Learning | |
| dc.subject | UCTD | |
| dc.subject.other | SDG-4: Quality education | |
| dc.title | A Continuous Reinforcement Learning Approach to Self-Adaptive Particle Swarm Optimisation | |
| dc.type | Dissertation |