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Journal articles on the topic 'Grey wolf optimizer-cuckoo search'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Al-Imron, Cynthia Novel, Dana Marsetiya Utama, and Shanty Kusuma Dewi. "An Energy-Efficient No Idle Permutations Flow Shop Scheduling Problem Using Grey Wolf Optimizer Algorithm." Jurnal Ilmiah Teknik Industri 21, no. 1 (2022): 1–10. http://dx.doi.org/10.23917/jiti.v21i1.17634.

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Energy consumption has become a significant issue in businesses. It is known that the industrial sector has consumed nearly half of the world's total energy consumption in some cases. This research aims to propose the Grey Wolf Optimizer (GWO) algorithm to minimize energy consumption in the No Idle Permutations Flowshop Problem (NIPFP). The GWO algorithm has four phases: initial population initialization, implementation of the Large Rank Value (LRV), grey wolf exploration, and exploitation. To determine the level of machine energy consumption, this study uses three different speed levels. To i

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Zhang, Sen, Qifang Luo, and Yongquan Zhou. "Hybrid Grey Wolf Optimizer Using Elite Opposition-Based Learning Strategy and Simplex Method." International Journal of Computational Intelligence and Applications 16, no. 02 (2017): 1750012. http://dx.doi.org/10.1142/s1469026817500122.

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To overcome the poor population diversity and slow convergence rate of grey wolf optimizer (GWO), this paper introduces the elite opposition-based learning strategy and simplex method into GWO, and proposes a hybrid grey optimizer using elite opposition (EOGWO). The diversity of grey wolf population is increased and exploration ability is improved. The experiment results of 13 standard benchmark functions indicate that the proposed algorithm has strong global and local search ability, quick convergence rate and high accuracy. EOGWO is also effective and feasible in both low-dimensional and hig

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Layak Ali, Et al. "Grey Wolf Cuckoo Search Algorithm for Training Feedforward Neural Network and Logic Gates Design." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 722–32. http://dx.doi.org/10.17762/ijritcc.v11i9.8865.

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This paper presents a new hybrid Swarm Intelligence (SI) algorithm based on the Cuckoo Search Algorithm (CSA) and Grey Wolf Optimizer (GWO) called the Grey Wolf Cuckoo Search (GWCS) algorithm. The GWCS algorithm extracts and combines CSA and GWO features for efficient optimization. To carry out the comprehensive validation, the developed algorithm is applied to three different scenarios with their counterparts. The first validation is carried out on standard optimization benchmark problems. Further, they are used to train Feedforward Neural Networks and finally applied to design logic gates. T

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Al-Jawher, Waleed A. Mahmoud, and Shaimaa A. Shaaban. "K-Mean Based Hyper-Metaheuristic Grey Wolf and Cuckoo Search Optimizers for Automatic MRI Medical Image Clustering." Journal Port Science Research 7, issue (2024): 109–20. http://dx.doi.org/10.36371/port.2024.special.11.

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In this paper a new clustering algorithm is proposed for optimal clustering of MRI medical image. In our proposed algorithm, the clustering process implemented by K-means clustering algorithm, due to its simplicity and speed. The optimization process was done by a well-known metaheuristic algorithms Grey Wolf Optimizer (GWO) and Cuckoo Search Optimizer. GWO is a metaheuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. It mimics the leadership hierarchy and hunting strategies of wolves to explore the search space efficiently. GWO has shown promising performa

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L. Vamsi Narasimha Rao, P.S.Prakash, and M.Veera Kumari. "Improvement of power system operation using a novel hybrid optimization method for optimal allocation of facts devices in radial transmission line." Scientific Temper 15, no. 04 (2024): 3261–71. https://doi.org/10.58414/scientifictemper.2024.15.4.35.

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This paper presents a novel hybrid heuristic algorithm, termed improved grey wolf optimization and cuckoo search optimization (IGWO-CSO), designed for multi-objective functions. This algorithm aims to optimize the allocation of flexible alternating current transmission systems (FACTS) controllers within power grids, with the objectives of minimizing active power system losses, voltage deviation, and operational costs of the system. In this research work, interline dynamic voltage restorers (IDVR) are utilized as flexible AC transmission system (FACTS) controllers. A comparative analysis is per

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Osea, Zebua, Made Ginarsa I, and Made Ari Nrartha I. "GWO-based estimation of input-output parameters of thermal power plants." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 4 (2020): 2235–44. https://doi.org/10.12928/TELKOMNIKA.v18i4.12957.

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The fuel cost curve of thermal generators was very important in the calculation of economic dispatch and optimal power flow. Temperature and aging could make changes to fuel cost curve so curve estimation need to be done periodically. The accuracy of the curve parameters estimation strongly affected the calculation of the dispatch. This paper aims to estimate the fuel cost curve parameters by using the grey wolf optimizer method. The problem of curve parameter estimation was made as an optimization problem. The objective function to be minimized was the total number of absolute error or the di

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Wang, Shipeng, Xiaoping Yang, Xingqiao Wang, and Zhihong Qian. "A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization." Sensors 19, no. 12 (2019): 2735. http://dx.doi.org/10.3390/s19122735.

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The random placement of a large-scale sensor network in an outdoor environment often causes low coverage. In order to effectively improve the coverage of a wireless sensor network in the monitoring area, a coverage optimization algorithm for wireless sensor networks with a Virtual Force-Lévy-embedded Grey Wolf Optimization (VFLGWO) algorithm is proposed. The simulation results show that the VFLGWO algorithm has a better optimization effect on the coverage rate, uniformity, and average moving distance of sensor nodes than a wireless sensor network coverage optimization algorithm using Lévy-embe

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Rajendra, K., S. Subramanian, N. Karthik, K. Naveenkumar, and S. Ganesan. "Grey Wolf Optimizer and Cuckoo Search Algorithm for Electric Power System State Estimation with Load Uncertainty and False Data." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 2s (2023): 59–67. http://dx.doi.org/10.17762/ijritcc.v11i2s.6029.

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State estimate serves a crucial purpose in the control centre of a modern power system. Voltage phasor of buses in such configurations is referred to as state variables that should be determined during operation. A precise estimation is needed to define the optimal operation of all components. So many mathematical and heuristic techniques can be used to achieve the aforementioned objective. An enhanced power system state estimator built on the cuck search algorithm is described in this work. Several scenarios, including the influence of load uncertainty and the likelihood of false data injecti

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Alzaghoul, Esra F., and Sandi N. Fakhouri. "Collaborative Strategy for Grey Wolf Optimization Algorithm." Modern Applied Science 12, no. 7 (2018): 73. http://dx.doi.org/10.5539/mas.v12n7p73.

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Grey wolf Optimizer (GWO) is one of the well known meta-heuristic algorithm for determining the minimum value among a set of values. In this paper, we proposed a novel optimization algorithm called collaborative strategy for grey wolf optimizer (CSGWO). This algorithm enhances the behaviour of GWO that enhances the search feature to search for more points in the search space, whereas more groups will search for the global minimal points. The algorithm has been tested on 23 well-known benchmark functions and the results are verified by comparing them with state of the art algorithms: Polar part

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Chakraborty, Sayan, Ratika Pradhan, Amira S. Ashour, Luminita Moraru, and Nilanjan Dey. "Grey-Wolf-Based Wang’s Demons for Retinal Image Registration." Entropy 22, no. 6 (2020): 659. http://dx.doi.org/10.3390/e22060659.

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Image registration has an imperative role in medical imaging. In this work, a grey-wolf optimizer (GWO)-based non-rigid demons registration is proposed to support the retinal image registration process. A comparative study of the proposed GWO-based demons registration framework with cuckoo search, firefly algorithm, and particle swarm optimization-based demons registration is conducted. In addition, a comparative analysis of different demons registration methods, such as Wang’s demons, Tang’s demons, and Thirion’s demons which are optimized using the proposed GWO is carried out. The results es

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Yue, Zhihang, Sen Zhang, and Wendong Xiao. "A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm." Sensors 20, no. 7 (2020): 2147. http://dx.doi.org/10.3390/s20072147.

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Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration abili

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L. Vamsi Narasimha Rao. "Enhancing Operational Performance of Power System through Optimal Allocation of TCSC Using a Novel Hybrid Optimisation Technique." Journal of Information Systems Engineering and Management 10, no. 3 (2025): 1034–53. https://doi.org/10.52783/jisem.v10i3.6557.

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This paper presents a novel hybrid optimization methodology integrating the Improved Grey Wolf Optimizer (IGWO) and Cuckoo Search Optimization (CSO) for the optimal placement and sizing of Thyristor-Controlled Series Compensators (TCSC) in power distribution networks. The proposed IGWO-CSO algorithm aims to enhance power system performance by minimizing active power loss, total bus voltage deviation and operating costs (OC). The optimization problem is formulated as a single-objective and multi-objective framework to ensure an effective trade-off between multiple power system parameters. To de

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13

Momanyi, Enock, and Davies Segera. "A Master-Slave Binary Grey Wolf Optimizer for Optimal Feature Selection in Biomedical Data Classification." BioMed Research International 2021 (October 12, 2021): 1–12. http://dx.doi.org/10.1155/2021/5556941.

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A new master-slave binary grey wolf optimizer (MSBGWO) is introduced. A master-slave learning scheme is introduced to the grey wolf optimizer (GWO) to improve its ability to explore and get better solutions in a search space. Five high-dimensional biomedical datasets are used to test the ability of MSBGWO in feature selection. The experimental results of MSBGWO are superior in terms of classification accuracy, precision, recall, F -measure, and number of features selected when compared to those of the binary grey wolf optimizer version 2 (BGWO2), binary genetic algorithm (BGA), binary particle

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Seçkiner, Serap Ulusam, and Şeyma Yilkici Yüzügüldü. "A new health-based metaheuristic algorithm: cholesterol algorithm." International Journal of Industrial Optimization 4, no. 2 (2023): 115–30. http://dx.doi.org/10.12928/ijio.v4i2.7651.

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This paper seeks to explore the effectiveness of a new health-based metaheuristic algorithm inspired by the cholesterol metabolism of the human body. In the study, the main idea is the focus on the performance of the cholesterol algorithm on unconstrained continuous optimization problems. The performances of the proposed cholesterol algorithm are evaluated based on 23 comparison tests and results were compared with Particle Swarm Optimization, Genetic Algorithm, Grey Wolf Optimization, Whale Optimization Algorithm, Harris Hawks Optimization, Differential Evolution, FireFly Algorithm, Cuckoo Se

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Negi, Ganga, Anuj Kumar, Sangeeta Pant, and Mangey Ram. "Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer." Decision Making: Applications in Management and Engineering 4, no. 2 (2021): 241–56. http://dx.doi.org/10.31181/dmame210402241n.

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Reliability allocation to increase the total reliability has become a successful way to increase the efficiency of the complex industrial system designs. A lot of research in the past have tackled this problem to a great extent. This is evident from the different techniques developed so far to achieve the target. Stochastic metaheuristics like simulated annealing, Tabu search (TS), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CS), Genetic Algorithm (GA), Grey wolf optimization technique (GWO) etc. have been used in recent years. This paper proposes a framework for implementin

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Jing-Sen Liu, Jing-Sen Liu, Qing-Qing Liu Jing-Sen Liu, and Fang Zuo Qing-Qing Liu. "A Guided Mutation Grey Wolf Optimizer Algorithm Integrating Flower Pollination Mechanism." 電腦學刊 33, no. 2 (2022): 051–67. http://dx.doi.org/10.53106/199115992022043302005.

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<p>The basic Grey Wolf Optimizer (GWO) has some shortcomings, for example, the convergence speed is slow, it is easy to fall into local extremum, and high-dimensional optimization ability is poor and so on. In response to these shortcomings, an improved grey wolf algorithm which combines flower pollination mechanism, teaching mechanism and polynomial variation is proposed in this study. The flower pollination mechanism is integrated with GWO algorithm, Levy distribution is introduced into the global search of grey wolf population. And the double random mechanism is added in the local sea

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Jing-Sen Liu, Jing-Sen Liu, Qing-Qing Liu Jing-Sen Liu, and Fang Zuo Qing-Qing Liu. "A Guided Mutation Grey Wolf Optimizer Algorithm Integrating Flower Pollination Mechanism." 電腦學刊 33, no. 2 (2022): 051–67. http://dx.doi.org/10.53106/199115992022043302005.

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<p>The basic Grey Wolf Optimizer (GWO) has some shortcomings, for example, the convergence speed is slow, it is easy to fall into local extremum, and high-dimensional optimization ability is poor and so on. In response to these shortcomings, an improved grey wolf algorithm which combines flower pollination mechanism, teaching mechanism and polynomial variation is proposed in this study. The flower pollination mechanism is integrated with GWO algorithm, Levy distribution is introduced into the global search of grey wolf population. And the double random mechanism is added in the local sea

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Rosli, Siti Julia, Hasliza A. Rahim, Khairul Najmy Abdul Rani, et al. "A Hybrid Modified Method of the Sine Cosine Algorithm Using Latin Hypercube Sampling with the Cuckoo Search Algorithm for Optimization Problems." Electronics 9, no. 11 (2020): 1786. http://dx.doi.org/10.3390/electronics9111786.

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The metaheuristic algorithm is a popular research area for solving various optimization problems. In this study, we proposed two approaches based on the Sine Cosine Algorithm (SCA), namely, modification and hybridization. First, we attempted to solve the constraints of the original SCA by developing a modified SCA (MSCA) version with an improved identification capability of a random population using the Latin Hypercube Sampling (LHS) technique. MSCA serves to guide SCA in obtaining a better local optimum in the exploitation phase with fast convergence based on an optimum value of the solution.

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Mouhou, Abdelaziz, and Abdelmajid Badri. "Low Integer-Order Approximation of Fractional-Order Systems Using Grey Wolf Optimizer-Based Cuckoo Search Algorithm." Circuits, Systems, and Signal Processing 41, no. 4 (2021): 1869–94. http://dx.doi.org/10.1007/s00034-021-01872-w.

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Garapati, Satish Kumar, and A. N. Sigappi. "An enhanced Grey Wolf optimizer Cuckoo search optimization with Naïve Bayes classifier for intrusion detection system." Journal of Information and Optimization Sciences 45, no. 8 (2024): 2227–36. https://doi.org/10.47974/jios-1785.

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Firewalls, cryptographic methods, and antivirus scanners are becoming increasingly ineffective in the face of increasingly complex threats. To make network channels and machines more secure, stronger protective walls are required. In addition to the policies already in place, an intrusion detection system can serve as an extra line of defence. This research suggests using a Naïve Bayes classifier in conjunction with an Enhanced Grey Wolf Optimizer Cuckoo Search Optimization (EGWCSO) to enhance performance. Min-Max normalization and 1-N encoding are used in the preprocessing step. EGWCSO is app

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Wang, Zhendong, Huamao Xie, Zhongdong Hu, Dahai Li, Junling Wang, and Wen Liang. "Node coverage optimization algorithm for wireless sensor networks based on improved grey wolf optimizer." Journal of Algorithms & Computational Technology 13 (January 2019): 174830261988949. http://dx.doi.org/10.1177/1748302619889498.

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Aiming at the problem of wireless sensor network node coverage optimization with obstacles in the monitoring area, based on the grey wolf optimizer algorithm, this paper proposes an improved grey wolf optimizer (IGWO) algorithm to improve the shortcomings of slow convergence, low search precision, and easy to fall into local optimum. Firstly, the nonlinear convergence factor is designed to balance the relationship between global search and local search. The elite strategy is introduced to protect the excellent individuals from being destroyed as the iteration proceeds. The original weighting s

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Shringi, Sakshi, H. Sharma, and D. L. Suthar. "Fitness-Based Grey Wolf Optimizer Clustering Method for Spam Review Detection." Mathematical Problems in Engineering 2022 (April 29, 2022): 1–15. http://dx.doi.org/10.1155/2022/6499918.

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Customers nowadays rely heavily on online reviews when making buying decisions. Various internet websites, including Amazon, Yelp, Google Plus, BookMyShow, Facebook, Twitter, and others, allow users to generate massive amounts of data. The information is gathered through feedback/reviews, comments, and tweets. Companies can leverage this information to improve the quality of their products. Spam reviews are created pretentiously by some businesses and people to promote or degrade the popularity of any product, organization, or person due to their reliance on these online reviews. Thus, identif

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Brahim, Ouacha, Bouyghf Hamid, Nahid Mohammed, and Abenna Said. "Design and miniaturization of a microsystem to power biomedical implants using grey wolf optimizer-based cuckoo search algorithm." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 1329–37. https://doi.org/10.11591/ijece.v13i2.pp1329-1337.

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One of the greatest techniques, inductive coupling is frequently utilized in the biomedical sector for wireless energy transfer to implants. The aim of this article is to develop and analyze the effect of inductor geometrical characteristics, distance between transmitter (TX) and receiver (RX) and also the operating frequency on the wireless power transfer system, using grey wolf optimizer-based cuckoo search (GWO-CS) algorithm. Power transfer efficiency (PTE), power provided to load, and other critical components must all be improved or maximized and miniaturaze the microsystem proposed. The

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Long, Nguyen Ngoc, Nguyen Huu Quyet, Nguyen Xuan Tung, Bui Tien Thanh, and Tran Ngoc Hoa. "Damage Identification of Suspension Footbridge Structures using New Hunting-based Algorithms." Engineering, Technology & Applied Science Research 13, no. 4 (2023): 11085–90. http://dx.doi.org/10.48084/etasr.5983.

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Metaheuristic algorithms have been applied to tackle challenging optimization problems in various domains, such as health, education, manufacturing, and biology. In particular, the field of Structural Health Monitoring (SHM) has received significant interest, particularly in the area of damage identification in structures. Popular optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS), Teaching Learning Based Optimization (TLBO), Artificial Hummingbird Algorithm (AHA), Moth Flame Optimizer (MFO), among others, have been employed to address

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Abdulhasan Salim, Jinan, Baraa M. Albaker, Muwafaq Shyaa Alwan, and M. Hasanuzzaman. "Hybrid MPPT approach using Cuckoo Search and Grey Wolf Optimizer for PV systems under variant operating conditions." Global Energy Interconnection 5, no. 6 (2022): 627–44. http://dx.doi.org/10.1016/j.gloei.2022.12.005.

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Sun, Lijun, Binbin Feng, Tianfei Chen, Dongliang Zhao, and Yan Xin. "Equalized Grey Wolf Optimizer with Refraction Opposite Learning." Computational Intelligence and Neuroscience 2022 (May 11, 2022): 1–18. http://dx.doi.org/10.1155/2022/2721490.

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Grey wolf optimizer (GWO) is a global search algorithm based on grey wolf hunting activity. However, the traditional GWO is prone to fall into local optimum, affecting the performance of the algorithm. Therefore, to solve this problem, an equalized grey wolf optimizer with refraction opposite learning (REGWO) is proposed in this study. In REGWO, the issue about the low swarm population variety of GWO in the late iteration is well overcome by the opposing learning of refraction. In addition, the equilibrium pool strategy reduces the likelihood of wolves going to the local extremum. To investiga

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Ridha, Djamel Mohammedi, Zine Rabie, Mosbah Mustafa, and Arif Salem. "Optimum Network Reconfiguration using Grey Wolf Optimizer." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 5 (2018): 2428–35. https://doi.org/10.12928/TELKOMNIKA.v16i5.10271.

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Distribution system Reconfiguration is the process of changing the topology of the distribution network by opening and closing switches to satisfy a specific objective. It is a complex, combinatorial optimization problem involving a nonlinear objective function and constraints. Grey Wolf Optimizer (GWO) is a recently developed metaheuristic search algorithm inspired by the leadership hierarchy and hunting strategy of grey wolves in nature. The objective of this paper is to determine an optimal network reconfiguration that presents the minimum power losses, considering network constraints, and

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Ouacha, Brahim, Hamid Bouyghf, Mohammed Nahid, and Said Abenna. "Design and miniaturization of a microsystem to power biomedical implants using grey wolf optimizer-based cuckoo search algorithm." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 1329. http://dx.doi.org/10.11591/ijece.v13i2.pp1329-1337.

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<p>One of the greatest techniques, inductive coupling is frequently utilized in the biomedical sector for wireless energy transfer to implants. The aim of this article is to develop and analyze the effect of inductor geometrical characteristics, distance between transmitter (TX) and receiver (RX) and also the operating frequency on the wireless power transfer system, using grey wolf optimizer-based cuckoo search (GWO-CS) algorithm. Power transfer efficiency (PTE), power provided to load, and other critical components must all be improved or maximized and miniaturaze the microsystem propo

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Singh, Shitu, and Jagdish Chand Bansal. "Mutation-driven grey wolf optimizer with modified search mechanism." Expert Systems with Applications 194 (May 2022): 116450. http://dx.doi.org/10.1016/j.eswa.2021.116450.

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Wang, Yang, Chengyu Jin, Qiang Li, et al. "A Dynamic Opposite Learning-Assisted Grey Wolf Optimizer." Symmetry 14, no. 9 (2022): 1871. http://dx.doi.org/10.3390/sym14091871.

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The grey wolf optimization (GWO) algorithm is widely utilized in many global optimization applications. In this paper, a dynamic opposite learning-assisted grey wolf optimizer (DOLGWO) was proposed to improve the search ability. Herein, a dynamic opposite learning (DOL) strategy is adopted, which has an asymmetric search space and can adjust with a random opposite point to enhance the exploitation and exploration capabilities. To validate the performance of DOLGWO algorithm, 23 benchmark functions from CEC2014 were adopted in the numerical experiments. A total of 10 popular algorithms, includi

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Nguyen, Phuong Nam, Chau Le Thi Minh, Hoang Minh Vu Nguyen, Huy Anh Quyen, Ngoc Au Nguyen, and Ngoc Hung Nguyen. "A Meta-Heuristic based Solution for Mitigating Sub-Synchronous Resonance in Grid-connected Wind Farms." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 24055–60. https://doi.org/10.48084/etasr.11262.

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This study proposes a novel optimization strategy for tuning the parameters of a Rotor Side Converter (RSC), employing a hybrid approach that integrates three advanced optimization algorithms: Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), and Grey Wolf Optimizer (GWO). The proposed method is integrated with existing damping techniques on both the grid side, using a Thyristor Controlled Series Capacitor (TCSC) and the wind farm side, utilizing a Supplementary Damping Controller (SDC) to enhance the Sub-Synchronous Oscillation (SSO) damping effectiveness. Based on the IEEE Fi

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Tawhid, Mohamed A., and Ahmed F. Ali. "Multidirectional Grey Wolf Optimizer Algorithm for Solving Global Optimization Problems." International Journal of Computational Intelligence and Applications 17, no. 04 (2018): 1850022. http://dx.doi.org/10.1142/s1469026818500220.

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In this paper, we propose a new hybrid population-based meta-heuristics algorithm inspired by grey wolves in order to solve integer programming and minimax problems. The proposed algorithm is called Multidirectional Grey Wolf Optimizer (MDGWO) algorithm. In the proposed algorithm, we try to accelerate the standard grey wolf optimizer algorithm (GWO) by invoking the multidirectional search method with it in order to accelerate the search instead of letting the standard GWO run for more iterations without significant improvement in the results. MDGWO starts the search by applying the standard GW

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Ahmed, Rasel, Amril Nazir, Shuhaimi Mahadzir, Mohammad Shorfuzzaman, and Jahedul Islam. "Niching Grey Wolf Optimizer for Multimodal Optimization Problems." Applied Sciences 11, no. 11 (2021): 4795. http://dx.doi.org/10.3390/app11114795.

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Metaheuristic algorithms are widely used for optimization in both research and the industrial community for simplicity, flexibility, and robustness. However, multi-modal optimization is a difficult task, even for metaheuristic algorithms. Two important issues that need to be handled for solving multi-modal problems are (a) to categorize multiple local/global optima and (b) to uphold these optima till the ending. Besides, a robust local search ability is also a prerequisite to reach the exact global optima. Grey Wolf Optimizer (GWO) is a recently developed nature-inspired metaheuristic algorith

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Chen, Ke, Bo Xiao, Chunyang Wang, Xuelian Liu, Shuning Liang, and Xu Zhang. "Cuckoo Coupled Improved Grey Wolf Algorithm for PID Parameter Tuning." Applied Sciences 13, no. 23 (2023): 12944. http://dx.doi.org/10.3390/app132312944.

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In today’s automation control systems, the PID controller, as a core technology, is widely used to maintain the system output near the set value. However, in some complex control environments, such as the application of ball screw-driven rotating motors, traditional PID parameter adjustment methods may not meet the requirements of high precision, high performance, and fast response time of the system, making it difficult to ensure the stability and production efficiency of the mechanical system. Therefore, this paper proposes a cuckoo search optimisation coupled with an improved grey wolf opti

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Chen, Yingyu, Shenhua Yang, Yongfeng Suo, and Minjie Zheng. "Ship Track Prediction Based on DLGWO-SVR." Scientific Programming 2021 (September 14, 2021): 1–14. http://dx.doi.org/10.1155/2021/9085617.

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To improve the accuracy of ship track prediction, the improved Grey Wolf Optimizer (GWO) and Support Vector Regression (SVR) models are incorporated for ship track prediction. The hunting strategy of dimensional learning was used to optimize the move search process of GWO and balance exploration and exploitation while maintaining population diversity. Selection and updating procedures keep GWO from being stuck in locally optimal solutions. The optimal parameters obtained by modified GWO were substituted into the SVR model to predict ship trajectory. Dimension Learning Grey Wolf Optimizer and S

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Wang, Jun, Pengcheng Luo, Xinwu Hu, and Xiaonan Zhang. "A Hybrid Discrete Grey Wolf Optimizer to Solve Weapon Target Assignment Problems." Discrete Dynamics in Nature and Society 2018 (November 7, 2018): 1–17. http://dx.doi.org/10.1155/2018/4674920.

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We propose a hybrid discrete grey wolf optimizer (HDGWO) in this paper to solve the weapon target assignment (WTA) problem, a kind of nonlinear integer programming problems. To make the original grey wolf optimizer (GWO), which was only developed for problems with a continuous solution space, available in the context, we first modify it by adopting a decimal integer encoding method to represent solutions (wolves) and presenting a modular position update method to update solutions in the discrete solution space. By this means, we acquire a discrete grey wolf optimizer (DGWO) and then through co

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Mosaa, Afrah U., and Waleed A. Mahmoud Al-Jawher. "A proposed Hyper-Heuristic optimizer Nesting Grey Wolf Optimizer and COOT Algorithm for Multilevel Task." Journal Port Science Research 6, no. 4 (2023): 310–17. http://dx.doi.org/10.36371/port.2023.4.1.

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It can be extremely difficult to find the optimal solution in many complex optimization problems. The goal of optimization algorithms in such cases is to locate a feasible solution that is as close as possible to the optimal one. These algorithms are called metaheuristic optimization algorithms and the majority of them take their inspiration from nature and work to solve challenging problems in a variety of fields. In this paper, a combination between GWO and Coot algorithm was proposed. The effectiveness of the GWO algorithm has been demonstrated in many fields, including engineering and medi

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Alsaeedi, Lec Ali Hakem, Suha Muhammed Hadi, Yarub Alazzawi, and Emad Badry Badry. "Eight-Figure Pattern for Enhancing the Searching Process of Grey Wolf Optimization (Eight-GWO)." Wasit Journal for Pure sciences 4, no. 2 (2025): 20–31. https://doi.org/10.31185/wjps.718.

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Evolutionary algorithms suffer significantly from a stack at the local optima. This paper proposes a new strategy that detects when the search gets stuck in a local optimum and then switches to a more dynamic approach to escape. The proposed model is based on simulating eight pattern movements and embedded with a Grey Wolf Optimizer algorithm (GWO). It is called the Eight-Figure Grey Wolf Optimizer (Eight-GWO). The proposed model combines two phases: regular search when searching progresses over time while the second phase, searching by eight patterns when the algorithm reaches stuck. The Eigh

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Oultiligh, Ahmed, Hassan Ayad, Abdeljalil El Kari, Mostafa Mjahed, and Nada El Gmili. "A novel improved elephant herding optimization for path planning of a mobile robot." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (2024): 206. http://dx.doi.org/10.11591/ijece.v14i1.pp206-217.

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Swarm intelligence algorithms have been in recent years one of the most used tools for planning the trajectory of a mobile robot. Researchers are applying those algorithms to find the optimal path, which reduces the time required to perform a task by the mobile robot. In this paper, we propose a new method based on the grey wolf optimizer algorithm (GWO) and the improved elephant herding optimization algorithm (IEHO) for planning the optimal trajectory of a mobile robot. The proposed solution consists of developing an IEHO algorithm by improving the basic EHO algorithm and then hybridizing it

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Long, Wen, Shaohong Cai, Jianjun Jiao, Ming Xu, and Tiebin Wu. "A new hybrid algorithm based on grey wolf optimizer and cuckoo search for parameter extraction of solar photovoltaic models." Energy Conversion and Management 203 (January 2020): 112243. http://dx.doi.org/10.1016/j.enconman.2019.112243.

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Ton, Trieu Ngoc, Hai Hoang Lai, Loi Van Pham, and Tuyen Ngoc Hoang. "Optimization of Distributed Generation Planning to Maximize the Absorption Rate of Renewable Energy in Distribution Networks." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 23008–13. https://doi.org/10.48084/etasr.10921.

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This paper presents a multi-objective optimization approach for optimal Distributed Generation (DG) placement and sizing, optimizing power loss reduction, cost efficiency, voltage stability, and Renewable Energy Source (RES) absorption. The Gray Wolf Optimizer (GWO) was chosen for its strong global search, fast convergence, and ability to avoid local optima. Simulations on IEEE 33-bus and IEEE 69-bus systems compared GWO against the Cuckoo Search Algorithm (CSA), Multi-Objective Particle Swarm Optimization (MOPSO), and Genetic Algorithm (GA). The results showed that GWO achieved the least powe

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Alghamdi, Ali S. "Greedy Sine-Cosine Non-Hierarchical Grey Wolf Optimizer for Solving Non-Convex Economic Load Dispatch Problems." Energies 15, no. 11 (2022): 3904. http://dx.doi.org/10.3390/en15113904.

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Economic load dispatch (ELD) provides significant benefits to the operation of the power system. It appears to be a complex nonconvex optimization problem subject to several equal and unequal constraints. The greedy sine-cosine nonhierarchical gray wolf optimizer (G-SCNHGWO) is introduced in this study to solve complex nonconvex ELD optimization problems efficiently and robustly. The sine and cosine functions assist the search agents of the grey wolf optimizer (GWO) algorithm in avoiding trapping in a local optimum. In addition, the greedy nonhierarchical concept is integrated into GWO to enri

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Rezaei, Farshad, Hamid Reza Safavi, Mohamed Abd Elaziz, Shaker H. Ali El-Sappagh, Mohammed Azmi Al-Betar, and Tamer Abuhmed. "An Enhanced Grey Wolf Optimizer with a Velocity-Aided Global Search Mechanism." Mathematics 10, no. 3 (2022): 351. http://dx.doi.org/10.3390/math10030351.

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This paper proposes a novel variant of the Grey Wolf Optimization (GWO) algorithm, named Velocity-Aided Grey Wolf Optimizer (VAGWO). The original GWO lacks a velocity term in its position-updating procedure, and this is the main factor weakening the exploration capability of this algorithm. In VAGWO, this term is carefully set and incorporated into the updating formula of the GWO. Furthermore, both the exploration and exploitation capabilities of the GWO are enhanced in VAGWO via stressing the enlargement of steps that each leading wolf takes towards the others in the early iterations while st

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Li, Linguo, Lijuan Sun, Jian Guo, Jin Qi, Bin Xu, and Shujing Li. "Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding." Computational Intelligence and Neuroscience 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/3295769.

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The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapur’s entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes

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Sabery, Ghulam Ali, Ghulam Hassan Danishyar, and Ghulam Sarwar Mubarez. "A Comparative Study of Metaheuristic Optimization Algorithms for Solving Engineering Design Problems." Journal of Mathematics and Statistics Studies 4, no. 4 (2023): 56–69. http://dx.doi.org/10.32996/jmss.2023.4.4.6.

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Metaheuristic optimization algorithms (Nature-Inspired Optimization Algorithms) are a class of algorithms that mimic the behavior of natural systems such as evolution process, swarm intelligence, human activity and physical phenomena to find the optimal solution. Since the introduction of meta-heuristic optimization algorithms, they have shown their profound impact in solving the high-scale and non-differentiable engineering problems. This paper presents a comparative study of the most widely used nature-inspired optimization algorithms for solving engineering classical design problems, which

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Bouhadouza, Boubekeur, Fares Sadaoui, and Abdelkader Boukaroura. "Performance of PSO-GWO, VAGWO, and GWO optimization techniques for economic dispatch problem: studied case of the southeast algerian electrical network." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e10179. http://dx.doi.org/10.54021/seesv5n2-437.

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Economic dispatch (ED) aims to identify the most cost-effective strategy for allocating power generation while meeting demand and adhering to the physical constraints of the power system. In this paper, three algorithms are proposed to solve the ED problem in power systems, including a hybrid Particle Swarm Optimization with Grey Wolf Optimizer (PSO-GWO), modified Velocity Aided Grey Wolf Optimizer (VAGWO), and Grey Wolf Optimizer (GWO). PSO is a meta-heuristic optimization technique designed to find the optimal solution to a problem by guiding the movement of particles within a defined explor

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Naka, Edjola, Eris Zeqo, and Alsa Kaziu. "Performance Analysis of Metaheuristic Algorithms on Benchmark Functions." Interdisciplinary Journal of Research and Development 11, no. 2 (2024): 10. http://dx.doi.org/10.56345/ijrdv11n202.

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The discipline of optimization can be used to maximize or minimize several problems. The use of metaheuristic algorithms is a strategy that often works well for global optimization. They are a type of stochastic algorithm that, via trial and error, finds workable solutions to difficult optimization problems in a reasonable amount of time, but they do not provide assurance that the answers are optimal. This paper aims to offer a comparative analysis of several metaheuristics in searching for the optimal solution. The selected metaheuristics are Artificial Bee Colony, Ant Lion Optimizer, Bat, Bl

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Yan, Fu, Jianzhong Xu, and Kumchol Yun. "Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information." Complexity 2019 (December 5, 2019): 1–36. http://dx.doi.org/10.1155/2019/7189653.

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The grey wolf optimizer (GWO) algorithm is a recently developed, novel, population-based optimization technique that is inspired by the hunting mechanism of grey wolves. The GWO algorithm has some distinct advantages, such as few algorithm parameters, strong global optimization ability, and ease of implementation on a computer. However, the paramount challenge is that there are some cases where the GWO is prone to stagnation in local optima. This drawback of the GWO algorithm may be attributed to an insufficiency in its position-updated equation, which disregards the positional interaction inf

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Hou, Yuxiang, Huanbing Gao, Zijian Wang, and Chuansheng Du. "Improved Grey Wolf Optimization Algorithm and Application." Sensors 22, no. 10 (2022): 3810. http://dx.doi.org/10.3390/s22103810.

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This paper proposed an improved Grey Wolf Optimizer (GWO) to resolve the problem of instability and convergence accuracy when GWO is used as a meta-heuristic algorithm with strong optimal search capability in the path planning for mobile robots. We improved chaotic tent mapping to initialize the wolves to enhance the global search ability and used a nonlinear convergence factor based on the Gaussian distribution change curve to balance the global and local searchability. In addition, an improved dynamic proportional weighting strategy is proposed that can update the positions of grey wolves so

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Daru Kusuma, Purba, and Meta Kallista. "Guided imitation optimizer: a metaheuristic combining guided search and imitation search." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4217. http://dx.doi.org/10.11591/ijai.v13.i4.pp4217-4228.

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This paper proposes a novel metaphor-free metaheuristic, namely the guided imitation optimizer (GIO). This metaheuristic combines the guided search and imitation-based search. There are five guided searches and three imitation based searches. Meanwhile, there are three references used in this metaheuristic: global finest, a randomly picked solution among the swarm, and a randomized solution within the search space. GIO is then evaluated by using 23 classic functions that consist of seven high dimension unimodal functions (HDUF), six high dimension multimodal functions (HDMF), and ten fixed dim

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