.png){kind=icon}
返回
Dynamic multi-strategy integrated differential evolution algorithm based on reinforcement learning for optimization problems
.png){kind=icon}
OA
AI
DOI：10.1007/s40747-023-01243-9.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The introduction of a multi-population structure in differential evolution (DE) algorithm has been proven to be an effective way to achieve algorithm adaptation and multi-strategy integration. However, in existing studies, the mutation strategy selection of each subpopulation during execution is fixed, resulting in poor self-adaptation of subpopulations. To solve this problem, a dynamic multi-strategy integrated differential evolution algorithm based on reinforcement learning (RLDMDE) is proposed in this paper. By employing reinforcement learning, each subpopulation can adaptively select the mutation strategy according to the current environmental state (population diversity). Based on the population state, this paper proposes an individual dynamic migration strategy to reward or punish the population to avoid wasting individual computing resources. Furthermore, this paper applies two methods of good point set and random opposition-based learning (ROBL) in the population initialization stage to improve the quality of the initial solutions. Finally, to evaluate the performance of the RLDMDE algorithm, this paper selects two benchmark function sets, CEC2013 and CEC2017, and six engineering design problems for testing. The results demonstrate that the RLDMDE algorithm has good performance and strong competitiveness in solving optimization problems.
Keyword:
Differential evolution
Multi-population
Population diversity
Reinforcement learning
Individual dynamic migration
期刊
IF:
4.6
论文数:
2.1K
被引数:
6.6K
机构
引用论文
Fault-tolerant adaptive tracking control of Euler-Lagrange systems-An echo state network approach driven by reinforcement learning.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏分享收藏A Gentle Introduction to Reinforcement Learning and its Application in Different FieldsIEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization求解全局数值优化问题的策略自适应差分进化算法OAAI
OAAI分享收藏
查看更多内容