Harris Hawks Optimization: A Formal Analysis of Its Variants and Applications

被引：1
作者：
Abu Khurma, Ruba [1 ]
Aljarah, Ibrahim [1 ]
Castillo, Pedro A. [2 ,3 ]
机构：
[1] Univ Jordan, King Abdullah 2 Sch Informat Technol, Amman, Jordan
[2] Univ Granada, Dept Comp Architecture & Comp Technol, ETSIIT, Granada, Spain
[3] Univ Granada, CITIC, Granada, Spain
来源：
PROCEEDINGS OF THE 13TH INTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL INTELLIGENCE (IJCCI) | 2021年
关键词：
Harris Hawcks Optimization Algorithm; HHO; Meta-heuristic Optimization; Swarm Intelligence Algorithms; FEATURE-SELECTION; ALGORITHM;
D O I：
10.5220/0010636600003063
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The Harris Hawks Optimization (HHO) is a recent meta-heuristic algorithm developed by Hideri in 2019. HHO algorithm has been widely utilized to solve many optimization problems in different fields. The primary objective of HHO is to define a fitness function that can successfully optimize a specific problem by finding the minimum or maximum value. This survey presents a thorough study of the algorithm, including its variants such as binary, hybridization, multi-objective and modifications. It highlights the main applications such as medical, engineering, machine learning, and network applications. Finally, the conclusion summarizes the current works on HHO and suggests possible future directions.
引用
收藏
页码：88 / 95
页数：8
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