.png){kind=icon}
返回
Research on Large-Scale Bi-Level Particle Swarm Optimization Algorithm
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3072199.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Targeting at the slow convergence and the local optimum problems of particle swarm optimization (PSO), a large-scale bi-level particle swarm optimization algorithm is proposed in this paper, which enlarges the particle swarm scale and enhances the initial population diversity on the basis of multi-particle swarms. On the other hand, this algorithm also improves the running efficiency of the particle swarms by the structural advantages of bi-level particle swarms, for which, the upper-level particle swarm provides decision-making information while the lower level working particle swarms run at the same time, enhancing the operation efficiency of particle swarms. The two levels of particle swarms collaborate and work well with each other. In order to prevent population precocity and slow convergence in the later stage, an accelerated factor based on increasing exponential function is applied at the same time to control the coupling among particle swarms. And the simulation results show that the large-scale bi-level particle swarm optimization algorithm is featured in better superiority and stability.
Keyword:
Particle swarm optimization
Statistics
Sociology
Optimization
Convergence
Decision making
Acceleration
Bi-level particle swarm
swarm intelligence
particle swarm optimization
large-scale particle swarm
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
暂无机构信息
引用论文
Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm蛾焰优化算法: 一种新的自然启发启发式范式
PREAI
分享收藏Glycoprotein G-protein Coupled Receptors in Disease: Luteinizing Hormone Receptors and Follicle Stimulating Hormone Receptors.png)
DiseasesOAAI
DiseasesIF0
OAAI分享收藏Path planning for mobile robot using self-adaptive learning particle swarm optimization基于自适应学习粒子群算法的移动机器人路径规划
PREAI
分享收藏分享收藏
查看更多内容