共 231 条
A Survey of Learning-Based Intelligent Optimization Algorithms
被引:201
作者:
Li, Wei
[1
]
Wang, Gai-Ge
[1
]
Gandomi, Amir H.
[2
]
机构:
[1] Ocean Univ China, Dept Comp Sci & Technol, Qingdao 266100, Peoples R China
[2] Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia
基金:
中国国家自然科学基金;
关键词:
PARTICLE SWARM OPTIMIZATION;
ARTIFICIAL BEE COLONY;
KRILL HERD;
SEARCH ALGORITHM;
MEMETIC ALGORITHM;
CRYPTANALYSIS;
SELECTION;
SCHEME;
MODEL;
D O I:
10.1007/s11831-021-09562-1
中图分类号:
TP39 [计算机的应用];
学科分类号:
081203 ;
0835 ;
摘要:
A large number of intelligent algorithms based on social intelligent behavior have been extensively researched in the past few decades, through the study of natural creatures, and applied to various optimization fields. The learning-based intelligent optimization algorithm (LIOA) refers to an intelligent optimization algorithm with a certain learning ability. This is how the traditional intelligent optimization algorithm combines learning operators or specific learning mechanisms to give itself some learning ability, thereby achieving better optimization behavior. We conduct a comprehensive survey of LIOAs in this paper. The research includes the following sections: Statistical analysis about LIOAs, classification of LIOA learning method, application of LIOAs in complex optimization scenarios, and LIOAs in engineering applications. The future insights and development direction of LIOAs are also discussed.
引用
收藏
页码:3781 / 3799
页数:19
相关论文