A survey of bacterial foraging optimization

被引:55
作者
Guo, Chen [1 ,2 ]
Tang, Heng [2 ]
Niu, Ben [1 ,3 ]
Lee, Chang Boon Patrick [2 ]
机构
[1] Shenzhen Univ, Coll Management, Shenzhen 518060, Peoples R China
[2] Univ Macau, Fac Business Adm, Macau, Peoples R China
[3] Shenzhen Univ, Inst Big Data Intelligent Management & Decis, Shenzhen, Peoples R China
基金
中国国家自然科学基金;
关键词
Bacterial foraging optimization; Swarm intelligence; CiteSpace; Knowledge mapping; PARTICLE SWARM OPTIMIZATION; OPTIMAL POWER-FLOW; ADAPTIVE COMPUTATIONAL CHEMOTAXIS; HYBRID GENETIC ALGORITHM; DISTRIBUTION-SYSTEM; EMERGING TRENDS; DISTRIBUTED OPTIMIZATION; REGENERATIVE MEDICINE; CONGESTION MANAGEMENT; ECONOMIC-DISPATCH;
D O I
10.1016/j.neucom.2020.06.142
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Bacterial foraging optimization algorithm (BFO) is a biological-inspired swarm intelligence optimization algorithm that simulates the foraging behavior of bacteria to obtain maximal energy during the searching process. Since its inception, it has evoked wide attention from researchers. The number of articles about BFO and its variants has grown significantly over the past decades and continues to grow. Hence, there is a clear need for a scientific and comparative review of extant BFO literature based on a common framework that offers a quick glimpse of the development of BFO. In light of this, we develop a bibliometric review via using CiteSpace to construct a knowledge mapping, and then conduct a thorough literature review of BFO research. Based on the analytical results, we present the influential researchers, journals or proceedings, and articles, and discuss various key issues of BFO. Finally, we suggest serval research directions that require further attention. This survey is expected to exhibit a lucid outline and useful guidance for the researchers of BFO. (c) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页码:728 / 746
页数:19
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