Journal of Bionic Engineering (2023) 20:2840–2862https://doi.org/10.1007/s42235-023-00414-1

Recent Advances of Chimp Optimization Algorithm: Variants and Applications Mohammad Sh. Daoud1  · Mohammad Shehab2  · Laith Abualigah3,4,5,6,7,14 · Mohammad Alshinwan8  · Mohamed Abd Elaziz9  · Mohd Khaled Yousef Shambour10 · Diego Oliva11 · Mohammad A. Alia12 · Raed Abu Zitar13

Mohammad Shehab moh.shehab12@gmail.com 

1 College of Engineering, Al Ain University, 112612 Abu Dhabi, United Arab Emirates 

2 College of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan 

3 Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq 25113, Jordan 

4 Hourani Center for Applied Scientifc Research, Al-Ahliyya Amman University, Amman 19328, Jordan 5 MEU Research Unit, Middle East University, Amman 11831, Jordan 

6 Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan 

7 School of Computer Sciences, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia 

8 Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordan

9 Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 7120001, Egypt 

10 The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research, Umm Al-Qura University, Makkah 24382, Saudi Arabia 

11 Depto. de Innovacio?n Basada en la Informacio?n y el Conocimiento, Universidad de Guadalajara, CUCEI, 45129 Guadalajara, Mexico 

12 Cyber Security Department, Faculty of Science and Information Technology, Al Zaytoonah University of Jordan, Amman 11733, Jordan 

13 Sorbonne Center of Artifcial Intelligence, Sorbonne University-Abu Dhabi, 38044 Abu Dhabi, United Arab Emirates 

14 School of Engineering and Technology, Sunway University Malaysia, 27500 Petaling Jaya, Malaysia

Abstract: Chimp Optimization Algorithm (ChOA) is one of the recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also, it is simple, easy to use, fexible, scalable, and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable convergence. Therefore, the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using ChOA have been overviewed and summarized. Initially, introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of ChOA are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of ChOA are discussed in detail which are categorized into modifed, hybridized, and paralleled versions. The main applications of ChOA are also thoroughly described. The applications belong to the domains of economics, image processing, engineering, neural network, power and energy, networks, etc. Evaluation of ChOA is also provided. The review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining, and clustering. As well, it is wealthy in research on health, environment, and public safety. Also, it will aid those who are interested by providing them with potential future research. 

Keywords: Artifcial intelligence · Nature-inspired optimization algorithms · Chimp optimization algorithm · Optimization problems