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
共 231 条
[1]   An elite opposition-flower pollination algorithm for a 0-1 knapsack problem [J].
Abdel-Basset, Mohamed ;
Zhou, Yongquan .
INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION, 2018, 11 (01) :46-53
[2]   Action-Selection Method for Reinforcement Learning Based on Cuckoo Search Algorithm [J].
Abed-alguni, Bilal H. .
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2018, 43 (12) :6771-6785
[3]   Opposition-based learning in the shuffled bidirectional differential evolution algorithm [J].
Ahandani, Morteza Alinia .
SWARM AND EVOLUTIONARY COMPUTATION, 2016, 26 :64-85
[4]   Opposition-based learning in shuffled frog leaping: An application for parameter identification [J].
Ahandani, Morteza Alinia ;
Alavi-Rad, Hosein .
INFORMATION SCIENCES, 2015, 291 :19-42
[5]   A hybrid method of 2-TSP and novel learning-based GA for job sequencing and tool switching problem [J].
Ahmadi, Ehsan ;
Goldengorin, Boris ;
Suer, Gursel A. ;
Mosadegh, Hadi .
APPLIED SOFT COMPUTING, 2018, 65 :214-229
[6]   Differential Evolution for learning the classification method PROAFTN [J].
Al-Obeidat, Feras ;
Belacel, Nabil ;
Carretero, Juan A. ;
Mahanti, Prabhat .
KNOWLEDGE-BASED SYSTEMS, 2010, 23 (05) :418-426
[7]   Cooperative learning for radial basis function networks using particle swarm optimization [J].
Alexandridis, Alex ;
Chondrodima, Eva ;
Sarimveis, Haralambos .
APPLIED SOFT COMPUTING, 2016, 49 :485-497
[8]   A constrained portfolio trading system using particle swarm algorithm and recurrent reinforcement learning [J].
Almahdi, Saud ;
Yang, Steve Y. .
EXPERT SYSTEMS WITH APPLICATIONS, 2019, 130 :145-156
[9]   Learning of interval and general type-2 fuzzy logic systems using simulated annealing: Theory and practice [J].
Almaraashi, M. ;
John, R. ;
Hopgood, A. ;
Ahmadi, S. .
INFORMATION SCIENCES, 2016, 360 :21-42
[10]   On the use of local search heuristics to improve GES-based Bayesian network learning [J].
Alonso, Juan I. ;
de la Ossa, Luis ;
Gamez, Jose A. ;
Puerta, Jose M. .
APPLIED SOFT COMPUTING, 2018, 64 :366-376