Swarm intelligence-based optimisation algorithms: an overview and future research issues

被引：21

作者：

Hu, Jinqiang ^{[1
]}

Wu, Husheng ^{[1
]}

Zhong, Bin ^{[1
]}

Xiao, Renbin ^{[2
]}

机构：

[1] Armed Police Force Engn Univ, Sch Equipment Management & Support, Xian 710086, Peoples R China

[2] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Peoples R China

来源：

INTERNATIONAL JOURNAL OF AUTOMATION AND CONTROL | 2020年 / 14卷 / 5-6期

基金：

中国国家自然科学基金;

关键词：

swarm intelligence; optimisation algorithm; universality; theoretical analysis; hybridisation strategy; complex optimisation problems; BEE COLONY ALGORITHM; CUCKOO SEARCH ALGORITHM; PIGEON-INSPIRED OPTIMIZATION; MIN ANT SYSTEM; DIFFERENTIAL EVOLUTION; PARAMETER ADAPTATION; CONVERGENCE ANALYSIS; SERVICE COMPOSITION; LOCAL SEARCH; HYBRID;

D O I：

10.1504/IJAAC.2020.110077

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Swarm intelligence-based optimisation algorithms, inspired by the collective intelligent behaviours of biology groups, have been widely recognised as efficient optimisers for many complex problems, e.g., dynamic optimisation problems, large-scale optimisation problems and many-objective optimisation problems. Swarm intelligence-based algorithms are the generic concepts to represent a range of metaheuristics with population-based iterative process, guided random search and parallel processing. This paper conducts an in-depth analysis of universality and difference of existing swarm intelligence-based algorithms. It also provides a systematical survey of some well-known algorithms. In addition, the expected research issues such as theoretical analysis, hybridisation strategy and complex problems optimisation are discussed thoroughly to inspire future study and more extensive applications.

引用

页码：656 / 693

页数：38

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