.png){kind=icon}
.png){kind=icon}
Return
Multi Actors-Critic based particle swarm optimization algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2025.129460.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper introduces the Multi-Actor-Critic-based Particle Swarm Optimization Algorithm (MACPSO), an innovative approach to optimizing particle performance through dynamic parameter control. MACPSO applies a multi-actor-critic framework to enhance exploration and maintain population diversity. It features multiple actor networks for adaptive parameter adjustment, coupled with a single critic network to guide the shared optimization goal. The algorithm incorporates particle grouping for intra-group updates and an inter-group updating scheme to facilitate the exchange of optimal information. Furthermore, MACPSO integrates a mutation mechanism aimed at improving the performance of the least effective particles, thereby ensuring sustained diversity within the population. To validate the superior performance of the proposed algorithm, a comparative study was conducted with nine advanced PSO variants using the CEC2017 benchmark suite. The experimental results demonstrate that MACPSO achieved the highest overall ranking in terms of average error across twenty-nine different benchmark functions. Additionally, numerical, graphical, and statistical analyses confirm that MACPSO outperforms the other nine PSO variants in terms of both effectiveness and robustness.
Keywords:
Particle swarm optimization
Parameter control
Reinforcement learning
Multi-agent
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W