Results 21 to 30 of about 1,072,852 (213)

An Improved Grey Wolf Optimizer Based on Tracking and Seeking Modes to Solve Function Optimization Problems

IEEE Access, 2020
Grey wolf optimizer (GWO) is a new meta-heuristic algorithm. The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Three main stages of hunting include: encircling, tracking and attacking.
M. W. Guo, J. S. Wang, L. F. Zhu, S. S. Guo, W. Xie   +4 more
doaj   +1 more source

Risk prediction of coal and gas outburst

Gong-kuang zidonghua, 2022
In order to solve the problems of low accuracy and slow response speed of existing support vector machine (SVM)-based coal and gas outburst prediction methods, a risk prediction method of coal and gas outburst based on improved grey wolf optimizer (IGWO)
LI Yan, NAN Xinyuan, LIN Wanke
doaj   +1 more source

Joint power control and user grouping mechanism for efficient uplink non‐orthogonal multiple access‐based 5G communication: Utilising the Lèvy‐flight firefly algorithm

IET Networks, EarlyView., 2023
We utilise a metaheuristic optimisation method, inspired by nature, called the Lévy‐flight firefly algorithm (LFA), to tackle the power regulation and user grouping in the NOMA systems. Abstract The non‐orthogonal multiple access strategies have shown promise to boost fifth generation and sixth generation wireless networks' spectral efficiency and ...
Zaid Albataineh, Khaled F. Hayajneh, Haythem Bany Salameh, Raed Al Athamneh, Yaser Jararweh   +4 more
wiley   +1 more source

Volitive Grey Wolf Optimizer [PDF]

, 2023
Swarm-based metaheuristics have become the most prominent method for solving optimization problems. Several operators already proposed in the literature can also be reused to expand the current metaheuristics.
Bastos Filho, Carmelo, Macedo, Mariana, Silva, Rodrigo Cesar, Siqueira, Hugo, Silva, Joao Paulo   +4 more
core   +1 more source

Adaptive grey wolf optimizer

, 2022
Swarm-based metaheuristic optimization algorithms have demonstrated outstanding performance on a wide range of optimization problems in both science and industry.
Meidani, Kazem, Farimani, Amir Barati, Hemmasian, AmirPouya, Mirjalili, Seyedali   +3 more
core   +1 more source

Hybridization of Grey Wolf Optimizer and Crow Search Algorithm Based on Dynamic Fuzzy Learning Strategy for Large-Scale Optimization

IEEE Access, 2020
A novel optimization algorithm named hybrid grey wolf optimizer with crow search algorithm (GWO-CSA) is developed in this paper for handling large-scale numerical optimization problems.
Rizk Masoud Rizk-Allah, Adam Slowik, Aboul Ella Hassanien   +2 more
doaj   +1 more source

Hybrid Grey Wolf Optimizer Based Optimal Capacitor Placement in Radial Distribution Systems

, 2022
The problem of optimal capacitor placement (OCP), or the determination of both the optimal bus locations and sizes in radial distribution systems (RDS) has been solved in this article using a new metaheuristic algorithm named as the hybrid grey wolf ...
Mirjalili, S, Cherukuri, S Hari Charan, Jayabarathi, T, Sanjay, R, Jha, Aditya, Raghunathan, T   +5 more
core   +1 more source

Building energy optimization using Grey Wolf Optimizer (GWO): Source codes

, 2021
The building model and source codes for building optimization using Grey Wolf Optimizer (GWO) Extract GWO folder and then add it to Mat lab paths before run, so the software will know the GWO function.
Ghalambaz, M (via Mendeley Data)
core   +1 more source

A novel hybrid GWO-SCA approach for optimization problems

Engineering Science and Technology, an International Journal, 2017
Recent trend of research is to hybridize two and several number of variants to find out better quality of solution of practical and recent real applications in the field of global optimization problems.
N. Singh, S.B. Singh
doaj   +1 more source

An Efficient Pattern Mining Convolution Neural Network (CNN) algorithm with Grey Wolf Optimization (GWO)

CoRR, 2022
Automation of feature analysis in the dynamic image frame dataset deals with complexity of intensity mapping with normal and abnormal class. The threshold-based data clustering and feature analysis requires iterative model to learn the component of image frame in multi-pattern for different image frame data type.
Aatif Jamshed, Bhawna Mallick, Rajendra Kumar Bharti   +2 more
openaire   +2 more sources