.png){kind=icon}
返回
A Distributed Brain Storm Optimization for Numerical Optimization and Graph Planarization
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2906899.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Brain storm optimization is a newly proposed meta-heuristic that has shown great success in many applications. However, it still suffers from slow convergence speed and population premature problems. To address these issues, this paper proposes a distributed brain storm optimization algorithm, namely DBSO, for both continuous and discrete optimization applications. In DBSO, problem solutions are located on several isolated groups, and they communicate with each other by a leader and literal learning strategies. By doing so, both exploitation and exploration abilities of the algorithm can be greatly enhanced. Experiments are conducted based on a set of continuous numerical benchmark functions and discrete graph planarization instances. The extensive experiments and statistical analysis indicate that DBSO can perform better or competitive results than other methods in terms of solution accuracy and convergence speed.
Keyword:
Brain storm optimization
meta-heuristics
evolutionary computation
graph planarization
distributed structure
computational intelligence
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
引用论文
.png){kind=icon}
分享收藏Distributed evolutionary algorithms and their models: A survey of the state-of-the-art分布式进化算法及其模型: 最新技术综述OAAI
OAAI分享收藏
查看更多内容