Swarm intelligence: past, present and future

被引：70
|
作者：
Yang, Xin-She [1 ]
Deb, Suash [2 ]
Zhao, Yu-Xin [3 ]
Fong, Simon [4 ]
He, Xingshi [5 ]
机构：
[1] Middlesex Univ, Sch Sci & Technol, London NW4 4BT, England
[2] Victoria Univ, Decis Sci & Modelling Program, Melbourne, Vic, Australia
[3] Harbin Engn Univ, Coll Automat, Harbin, Heilongjiang, Peoples R China
[4] Univ Macau, Dept Comp & Informat Sci, Macau, Peoples R China
[5] Xian Polytech Univ, Coll Sci, 19 Jinhua South Rd, Xian, Shaanxi, Peoples R China
来源：
SOFT COMPUTING | 2018年 / 22卷 / 18期
关键词：
Swarm intelligence; Nature-inspired algorithms; Optimization; Self-organization; Bioinspired computing; Metaheuristics; FLOWER POLLINATION ALGORITHM; CUCKOO SEARCH ALGORITHM; FIREFLY ALGORITHM; PARTICLE SWARM; BAT ALGORITHM; DIFFERENTIAL EVOLUTION; OPTIMIZATION; METAHEURISTICS; COMBINATORIAL; CONVERGENCE;
D O I：
10.1007/s00500-017-2810-5
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Many optimization problems in science and engineering are challenging to solve, and the current trend is to use swarm intelligence (SI) and SI-based algorithms to tackle such challenging problems. Some significant developments have been made in recent years, though there are still many open problems in this area. This paper provides a short but timely analysis about SI-based algorithms and their links with self-organization. Different characteristics and properties are analyzed here from both mathematical and qualitative perspectives. Future research directions are outlined, and open questions are also highlighted.
引用
收藏
页码：5923 / 5933
页数：11
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