Soft Actor-Critic Approach to Self-Adaptive Particle Swarm Optimisation

Daniel von Eschwege and Andries Engelbrecht ()
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Daniel von Eschwege: Department of Industrial Engineering, Stellenbosch University, Stellenbosch 7600, South Africa
Andries Engelbrecht: Department of Industrial Engineering, Stellenbosch University, Stellenbosch 7600, South Africa

Mathematics, 2024, vol. 12, issue 22, 1-34

Abstract: Particle swarm optimisation (PSO) is a swarm intelligence algorithm that finds candidate solutions by iteratively updating the positions of particles in a swarm. The decentralised optimisation methodology of PSO is ideally suited to problems with multiple local minima and deceptive fitness landscapes, where traditional gradient-based algorithms fail. PSO performance depends on the use of a suitable control parameter (CP) configuration, which governs the trade-off between exploration and exploitation in the swarm. CPs that ensure good performance are problem-dependent. Unfortunately, CPs tuning is computationally expensive and inefficient. Self-adaptive particle swarm optimisation (SAPSO) algorithms aim to adaptively adjust CPs during the optimisation process to improve performance, ideally while reducing the number of performance-sensitive parameters. This paper proposes a reinforcement learning (RL) approach to SAPSO by utilising a velocity-clamped soft actor-critic (SAC) that autonomously adapts the PSO CPs. The proposed SAC-SAPSO obtains a 50% to 80% improvement in solution quality compared to various baselines, has either one or zero runtime parameters, is time-invariant, and does not result in divergent particles.

Keywords: particle swarm optimisation; reinforcement learning; soft actor-critic; self-adaptive; swarm intelligence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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