A comprehensive survey: Whale Optimization Algorithm and its applications

被引:526
作者
Gharehchopogh, Farhad Soleimanian [1 ]
Gholizadeh, Hojjat [2 ]
机构
[1] Islamic Azad Univ, Urmia Branch, Dept Comp Engn, Orumiyeh, Iran
[2] Amirkabir Univ Technol, Dept Comp Engn & IT, Tehran, Iran
关键词
Whale optimization algorithm; Meta-heuristics; Hybridization; Improved; Optimization; PARTICLE SWARM OPTIMIZATION; NATURE-INSPIRED ALGORITHM; MOTH-FLAME OPTIMIZATION; GREY WOLF OPTIMIZER; DIFFERENTIAL EVOLUTION; MEGAPTERA-NOVAEANGLIAE; PARAMETER-ESTIMATION; LOCAL SEARCH; BEHAVIOR; SYSTEM;
D O I
10.1016/j.swevo.2019.03.004
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
Whale Optimization Algorithm (WOA) is an optimization algorithm developed by Mirjalili and Lewis in 2016. An overview of WOA is described in this paper, rooted from the bubble-net hunting strategy, besides an overview of WOA applications that are used to solve optimization problems in various categories. The best solution has been determined to make something as functional and effective as possible through the optimization process by minimizing or maximizing the parameters involved in the problems. Research and engineering attention have been paid to Meta-heuristics for purposes of decision-making given the growing complexity of models and the needs for quick decision making in the engineering. An updated review of research of WOA is provided in this paper for hybridization, improved, and variants. The categories included in the reviews are Engineering, Clustering, Classification, Robot Path, Image Processing, Networks, Task Scheduling, and other engineering applications. According to the reviewed literature, WOA is mostly used in the engineering area to solve optimization problems. Providing an overview and summarizing the review of WOA applications are the aims of this paper.
引用
收藏
页码:1 / 24
页数:24
相关论文
共 196 条
[1]
Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation [J].
Abd El Aziz, Mohamed ;
Ewees, Ahmed A. ;
Hassanien, Aboul Ella .
EXPERT SYSTEMS WITH APPLICATIONS, 2017, 83 :242-256
[2]
Parameter estimation of solar cells diode models by an improved opposition-based whale optimization algorithm [J].
Abd Elaziz, Mohamed ;
Oliva, Diego .
ENERGY CONVERSION AND MANAGEMENT, 2018, 171 :1843-1859
[3]
A Novel Whale Optimization Algorithm for Cryptanalysis in Merkle-Hellman Cryptosystem [J].
Abdel-Basset, Mohamed ;
El-Shahat, Doaa ;
El-henawy, Ibrahim ;
Sangaiah, Arun Kumar ;
Ahmed, Syed Hassan .
MOBILE NETWORKS & APPLICATIONS, 2018, 23 (04) :723-733
[4]
RETRACTED: A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem (Retracted article. See vol. 128, pg. 567, 2022) [J].
Abdel-Basset, Mohamed ;
Manogaran, Gunasekaran ;
El-Shahat, Doaa ;
Mirjalili, Seyedali .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2018, 85 :129-145
[5]
Finite Position Set-Phase Locked Loop for Sensorless Control of Direct-Driven Permanent-Magnet Synchronous Generators [J].
Abdelrahem, Mohamed ;
Hackl, Christoph M. ;
Kennel, Ralph .
IEEE TRANSACTIONS ON POWER ELECTRONICS, 2018, 33 (04) :3097-3105
[6]
Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm [J].
Abedinpourshotorban, Hosein ;
Shamsuddin, Siti Mariyam ;
Beheshti, Zahra ;
Jawawi, Dayang N. A. .
SWARM AND EVOLUTIONARY COMPUTATION, 2016, 26 :8-22
[7]
Ahmed AS, 2017, PROC INT MID EAST P, P631, DOI 10.1109/MEPCON.2017.8301247
[8]
Opposition-Based Whale Optimization Algorithm [J].
Alamri, Hammoudeh S. ;
Alsariera, Yazan A. ;
Zamli, Kamal Z. .
ADVANCED SCIENCE LETTERS, 2018, 24 (10) :7461-7464
[9]
Optimizing connection weights in neural networks using the whale optimization algorithm [J].
Aljarah, Ibrahim ;
Faris, Hossam ;
Mirjalili, Seyedali .
SOFT COMPUTING, 2018, 22 (01) :1-15
[10]
Alzacjebah A, 2018, INT CONF INFORM COMM, P84, DOI 10.1109/IACS.2018.8355446