.png){kind=icon}
返回
Coevolutionary Structure-Redesigned-Based Bacterial Foraging Optimization
.png){kind=icon}
PRE
AI
DOI：10.1109/TCBB.2017.2742946.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a Coevolutionary Structure-Redesigned-Based Bacteria Foraging Optimization (CSRBFO) based on the natural phenomenon that most living creatures tend to cooperate with each other so as to fulfill tasks more effectively. Aiming at lowering computational complexity while maintaining the critical search capability of standard bacterial foraging optimization (BFO), we employ a general loop to replace the nested loop and eliminate the reproduction step of BFO. Hence, the proposed CSRBFO only consists of two main steps: (1) chemotaxis and (2) elimination & dispersal. A coevolutionary strategy by which all bacteria can learn from each other and search for optima cooperatively is incorporated into the chemotactic step to accelerate convergence and facilitate accurate search. In the elimination & dispersal step, the three-stage evolutionary strategy with different learning methods for maintaining diversity is studied. An evaluation of the convergence status is then added to determine whether bacteria should move on to the next stage or not. The combination of coevolutionary strategy and convergence status evaluation is expected to balance exploration and exploitation. Experimental results comparing seven well-known heuristic algorithms on 24 benchmark functions demonstrate that the proposed CSRBFO outperforms the comparison algorithms significantly in most of the cases.
Keyword:
Structure-redesigned-based algorithm
bacterial foraging optimization (BFO)
coevolutionary strategy
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
I
IF:
3.4
论文数:
3.3K
被引数:
6.4K
机构
引用论文
.png){kind=icon}
A Cooperative Co-Evolutionary Genetic Algorithm for Tree Scoring and Ancestral Genome Inference用于树评分和祖先基因组推断的合作协同进化遗传算法
PREAI
分享收藏Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
PREAI
分享收藏
Ant colony optimization-based feature selection method for surface electromyography signals classification基于蚁群优化的表面肌电信号特征选择分类方法
PREAI
分享收藏
查看更多内容