Year: 2020
Type: article
Abstract: Elephant herding optimization (EHO) is a nature-inspired metaheuristic optimization algorithm based on the herding behavior of elephants. EHO uses a clan operator to update the distance of the elephants in each clan with respect to the position of a matriarch elephant. The superiority of the EHO method to several state-of-the-art metaheuristic algorithms has been demonstrated for many benchmark problems and in various application areas. A comprehensive review for the EHO-based algorithms and their applications are presented in this paper. Various aspects of the EHO variants for continuous optimization, combinatorial optimization, constrained optimization, and multi-objective optimization are reviewed. Future directions for research in the area of EHO are further discussed.
Source: Mathematics
DOI: 10.3390/math8091415
Authors: Juan Li, Hong Lei, Amir H. Alavi, Gai-Ge Wang
Institutions: Jilin University, Wuchang University of Technology, Wuhan Technology and Business University, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Asia University, University of Pittsburgh, Northeast Normal University, Ocean University of China
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,267
OpenAlex ID: W3080342797