Soft Actor-Critic Approach to Self-Adaptive Particle Swarm Optimisation
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 landscape...
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MDPI AG
2024-11-01
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| author | Daniel von Eschwege Andries Engelbrecht |
| author_facet | Daniel von Eschwege Andries Engelbrecht |
| author_sort | Daniel von Eschwege |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d976a010a3d74f0587790990e67a1da2 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj-art-d976a010a3d74f0587790990e67a1da22024-11-26T18:11:34ZengMDPI AGMathematics2227-73902024-11-011222348110.3390/math12223481Soft Actor-Critic Approach to Self-Adaptive Particle Swarm OptimisationDaniel von Eschwege0Andries Engelbrecht1Department of Industrial Engineering, Stellenbosch University, Stellenbosch 7600, South AfricaDepartment of Industrial Engineering, Stellenbosch University, Stellenbosch 7600, South AfricaParticle 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.https://www.mdpi.com/2227-7390/12/22/3481particle swarm optimisationreinforcement learningsoft actor-criticself-adaptiveswarm intelligence |
| spellingShingle | Daniel von Eschwege Andries Engelbrecht Soft Actor-Critic Approach to Self-Adaptive Particle Swarm Optimisation Mathematics particle swarm optimisation reinforcement learning soft actor-critic self-adaptive swarm intelligence |
| title | Soft Actor-Critic Approach to Self-Adaptive Particle Swarm Optimisation |
| title_full | Soft Actor-Critic Approach to Self-Adaptive Particle Swarm Optimisation |
| title_fullStr | Soft Actor-Critic Approach to Self-Adaptive Particle Swarm Optimisation |
| title_full_unstemmed | Soft Actor-Critic Approach to Self-Adaptive Particle Swarm Optimisation |
| title_short | Soft Actor-Critic Approach to Self-Adaptive Particle Swarm Optimisation |
| title_sort | soft actor critic approach to self adaptive particle swarm optimisation |
| topic | particle swarm optimisation reinforcement learning soft actor-critic self-adaptive swarm intelligence |
| url | https://www.mdpi.com/2227-7390/12/22/3481 |
| work_keys_str_mv | AT danielvoneschwege softactorcriticapproachtoselfadaptiveparticleswarmoptimisation AT andriesengelbrecht softactorcriticapproachtoselfadaptiveparticleswarmoptimisation |