.png){kind=icon}
返回
Q-Learning-Based Multi-Strategy Topology Particle Swarm Optimization Algorithm
.png){kind=icon}
PRE
AI
DOI：10.3390/a18110672.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In response to the issues of premature convergence and insufficient parameter control in Particle Swarm Optimization (PSO) for high-dimensional complex optimization problems, this paper proposes a Multi-Strategy Topological Particle Swarm Optimization algorithm (MSTPSO). The method builds upon a reinforcement learning-driven topological switching framework, where Q-learning dynamically selects among fully informed topology, small-world topology, and exemplar-set topology to achieve an adaptive balance between global exploration and local exploitation. Furthermore, the algorithm integrates differential evolution perturbations and a global optimal restart strategy based on stagnation detection, together with a dual-layer experience replay mechanism to enhance population diversity at multiple levels and strengthen the ability to escape local optima. Experimental results on 29 CEC2017 benchmark functions, compared against various PSO variants and other advanced evolutionary algorithms, show that MSTPSO achieves superior fitness performance and exhibits stronger stability on high-dimensional and complex functions. Ablation studies further validate the critical contribution of the Q-learning-based multi-topology control and stagnation detection mechanisms to performance improvement. Overall, MSTPSO demonstrates significant advantages in convergence accuracy and global search capability.
Keyword:
particle swarm optimization
multi-topology structure
reinforcement learning
experience replay strategy
stagnation detection
期刊
IF:
2.1
论文数:
631
被引数:
5.4K
机构
引用论文
Incorporating Surprisingly Popular Algorithm and Euclidean distance-based adaptive topology into PSO
OAAI
OAAI分享收藏Heterogeneous differential evolution particle swarm optimization with local search局部搜索的异质差分进化粒子群优化算法
OAAI
OAAI分享收藏Multi Actors-Critic based particle swarm optimization algorithm基于多行动者-批评家的粒子群优化算法.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容