A Survey of Learning-Based Intelligent Optimization Algorithms

• Published in: Archives of computational methods in engineering Vol. 28; no. 5; pp. 3781 - 3799
• Main Authors: Li, Wei, Wang, Gai-Ge, Gandomi, Amir H.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.08.2021; Springer Nature B.V
• Subjects: Algorithms; Engineering; Machine learning; Mathematical and Computational Engineering; Optimization; Optimization algorithms; Original Paper; Statistical analysis
• ISSN: 1134-3060; 1886-1784
• DOI: 10.1007/s11831-021-09562-1

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.