SmartSwarm - A Multi-Agent Reinforcement Learning based Particle Swarm Optimization Algorithm
Master thesis
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https://hdl.handle.net/11250/3075428Utgivelsesdato
2023-06-01Metadata
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- Master theses [217]
Sammendrag
Particle Swarm Optimization is a renowned continuous optimization method that utilizes Swarm Intelligence to find solutions to complex non-linear optimization problems efficiently. Since its proposal, many developments have been put forward to improve its capabilities by enhancing the stochastic and tunable component of the algorithm. This thesis introduces SmartSwarm, a variant of Particle Swarm Optimization that utilizes Multi-Agent Reinforcement Learning to control the velocity of a swarm of particles. This framework has the capability of incorporating domain-specific information in the optimization process, as well as adapting a self-taught velocity function. We show how this framework has the ability to discover a velocity function to maximize the performance of the algorithm.
Utgiver
The University of BergenOpphavsrett
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