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Relevant bibliographies by topics / Grey wolf optimizer-cuckoo search

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Academic literature on the topic 'Grey wolf optimizer-cuckoo search'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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

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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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Book chapters on the topic "Grey wolf optimizer-cuckoo search"

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Yadav, Suman, Manjeet Kumar, Richa Yadav, and Ashwni Kumar. "A Novel Approach for Optimal Digital FIR Filter Design Using Hybrid Grey Wolf and Cuckoo Search Optimization." In Lecture Notes in Networks and Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3369-3_26.

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Chauhan, Sandeep Singh, and Prakash Kotecha. "Performance Evaluation of Grey Wolf Optimizer and Symbiotic Organisms Search for Multi-level Production Planning with Adaptive Penalty." In Smart Innovations in Communication and Computational Sciences. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8968-8_39.

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Wang, Zhi-Sheng, Jeng-Shyang Pan, Kuan-chun Huang, Tien-Szu Pan, and Jian-Po Li. "Hybrid Gray Wolf Optimization and Cuckoo Search Algorithm based on the Taguchi Theory." In Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1053-1_20.

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Davoodkhani, Faraji, S. Arabi Nowdeh, Almoataz Y. Abdelaziz, Saeedeh Mansoori, Sh Nasri, and Mohammad Alijani. "A New Hybrid Method Based on Gray Wolf Optimizer-Crow Search Algorithm for Maximum Power Point Tracking of Photovoltaic Energy System." In Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05578-3_16.

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Hassanin, Mohamed F., Abdullah M. Shoeb, and Aboul Ella Hassanien. "Designing Multilayer Feedforward Neural Networks Using Multi-Verse Optimizer." In Handbook of Research on Machine Learning Innovations and Trends. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2229-4.ch048.

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Artificial neural network (ANN) models are involved in many applications because of its great computational capabilities. Training of multi-layer perceptron (MLP) is the most challenging problem during the network preparation. Many techniques have been introduced to alleviate this problem. Back-propagation algorithm is a powerful technique to train multilayer feedforward ANN. However, it suffers from the local minima drawback. Recently, meta-heuristic methods have introduced to train MLP like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS), Ant Colony Optimizer (A

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Rajan, S. Dheva. "Demystifying Mathematical Principles in Swarm Intelligence With R." In Bio-Inspired Intelligence for Smart Decision-Making. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-5276-2.ch013.

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Swarm intelligence is a type of computational intelligence that is used to tackle complicated problems. SI is the aggregate study of how people in a population interact with one another on a local level. Nature, particularly for systems of biology, is frequently an inspiration. SI benefits include collaborative, adaptable, flexible, decentralized, responsive, self-organized, self-correcting, and secure, with mathematical models serving as the foundation. SI has studied a variety of systems, including schools of fish, flocks of birds, and herds of land animals, which have inspired a variety of

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Bentarzi, Hamid. "PMU Placement Optimization for Fault Observation Using Different Techniques." In Advances in Computer and Electrical Engineering. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4027-5.ch009.

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This chapter presents different techniques for obtaining the optimal number of the phasor measurement units (PMUs) that may be installed in a smart power grid to achieve full network observability under fault conditions. These optimization techniques such as binary teaching learning based optimization (BTLBO) technique, particle swarm optimization, the grey wolf optimizer (GWO), the moth-flame optimization (MFO), the cuckoo search (CS), and the wind-driven optimization (WDO) have been developed for the objective function and constraints alike. The IEEE 14-bus benchmark power system has been us

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Heidari, Ali Asghar, and Rahim Ali Abbaspour. "Enhanced Chaotic Grey Wolf Optimizer for Real-World Optimization Problems." In Handbook of Research on Emergent Applications of Optimization Algorithms. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2990-3.ch030.

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The gray wolf optimizer (GWO) is a new population-based optimizer that is inspired by the hunting procedure and leadership hierarchy in gray wolves. In this chapter, a new enhanced gray wolf optimizer (EGWO) is proposed for tackling several real-world optimization problems. In the EGWO algorithm, a new chaotic operation is embedded in GWO which helps search agents to chaotically move toward a randomly selected wolf. By this operator, the EGWO algorithm is capable of switching between chaotic and random exploration. In order to substantiate the efficiency of EGWO, 22 test cases from IEEE CEC 20

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Jeet, Kawal. "Nature-Inspired Algorithms for Bi-Criteria Parallel Machine Scheduling." In Exploring Critical Approaches of Evolutionary Computation. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5832-3.ch007.

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Nature has always been a source of inspiration for human beings. Nature-inspired search-based algorithms have an enormous computational intelligence and capabilities and are observing diverse applications in engineering and manufacturing problems. In this chapter, six nature-inspired algorithms, namely artificial bee colony, bat, black hole, cuckoo search, flower pollination, and grey wolf optimizer algorithms, have been investigated for scheduling of multiple jobs on multiple potential parallel machines. Weighted flow time and tardiness have been used as optimization criteria. These algorithm

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Gosain, Anjana, and Kavita Sachdeva. "Random Walk Grey Wolf Optimizer Algorithm for Materialized View Selection (RWGWOMVS)." In Novel Approaches to Information Systems Design. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2975-1.ch005.

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Optimal selection of materialized views is crucial for enhancing the performance and efficiency of data warehouse to render decisions effectively. Numerous evolutionary optimization algorithms like particle swarm optimization (PSO), genetic algorithm (GA), bee colony optimization (BCO), backtracking search optimization algorithm (BSA), etc. have been used by researchers for the selection of views optimally. Various frameworks like multiple view processing plan (MVPP), lattice, and AND-OR view graphs have been used for representing the problem space of MVS problem. In this chapter, the authors

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Conference papers on the topic "Grey wolf optimizer-cuckoo search"

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Zhongyuan, ZHANG, YANG Zhenyu, JI Xiaofeng, and REN Bangbang. "An Approach Based on Grey Wolf Optimizer for Path Planning in Search-ing Missing Submersibles." In The 14th International Conference on Logistics and Systems Engineering. Aussino Academic Publishing House, 2025. https://doi.org/10.52202/078960-0001.

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Sirmayanti, Pulung Hendro Prastyo, and Mahyati. "An Enhanced Grey Wolf Optimizer with Opposition, Mutation, and Local Search Strategy for Feature Selection." In 2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT). IEEE, 2024. https://doi.org/10.1109/comnetsat63286.2024.10862636.

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Lara-Monta�o, Oscar D., Fernando I. G�mez-Castro, Claudia Guti�rrez-Antonio, and Elena N. Dragoi. "Optimization Of Heat Exchangers Through an Enhanced Metaheuristic Strategy: The Success-Based Optimization Algorithm." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.167193.

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The optimization of shell-and-tube heat exchangers (STHEs) is critical for improving energy efficiency, reducing operational costs, and mitigating environmental impacts in industrial applications. This study evaluates the performance of the Success-Based Optimization Algorithm (SBOA), a novel metaheuristic strategy inspired by behavioral patterns in success perception, against seven established algorithms�Cuckoo Search, Differential Evolution (DE), Grey Wolf Optimization (GWO), Jaya Algorithm, Particle Swarm Optimization, Teaching-Learning Based Optimization, and Whale Optimization Algorithm�f

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Xu, Hui, Xiang Liu, and Jun Su. "An improved grey wolf optimizer algorithm integrated with Cuckoo Search." In 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE, 2017. http://dx.doi.org/10.1109/idaacs.2017.8095129.

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Silva, Joao Paulo, Rodrigo Cesar Silva, Mariana Macedo, Hugo Siqueira, and Carmelo Bastos Filho. "Volitive Grey Wolf Optimizer." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2024. http://dx.doi.org/10.21528/cbic2023-109.

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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. We present in this paper the Volitive Grey Wolf Optimizer (VGWO), a Grey Wolf Optimizer variant created by the addition of the collective volitive movement proposed in Fish School Search. The Volitive operator allows a self-regulated balance between exploration and exploitation that generates diversity when necessary. We evaluate the performance of VGWO and five other metaheuristics by s

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Elzalik, M., Taha A. Enany, Mokhtar Said, and Amir Y. Hassan. "Comparison between Cuckoo Search algorithm and Grey Wolf Optimizer Algorithm on Photovoltaic Models Performance." In 2022 23rd International Middle East Power Systems Conference (MEPCON). IEEE, 2022. http://dx.doi.org/10.1109/mepcon55441.2022.10021814.

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Chen, Xiaojuan, and Haiyang Zhang. "Grey wolf optimizer with global search strategy." In 2021 International Conference on Electronic Information Engineering and Computer Science (EIECS). IEEE, 2021. http://dx.doi.org/10.1109/eiecs53707.2021.9588109.

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Sharma, Satyam, Ridhi Kapoor, and Sanjeev Dhiman. "A Novel Hybrid Metaheuristic Based on Augmented Grey Wolf Optimizer and Cuckoo Search for Global Optimization." In 2021 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC). IEEE, 2021. http://dx.doi.org/10.1109/icsccc51823.2021.9478142.

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Deb, Subhasish, Benjamin Chatuanramthrnghaka, Subir Datta, Sanjoy Debbarma, Ksh Robert Singh, and Ramesh Kumar. "Congestion Management by Generator Real Power Rescheduling using Hybrid Grey Wolf Optimizer and Cuckoo Search Algorithm." In 2021 1st International Conference on Power Electronics and Energy (ICPEE). IEEE, 2021. http://dx.doi.org/10.1109/icpee50452.2021.9358753.

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Preethi, P., I. Vasudevan, S. Saravanan, R. Krishna Prakash, and A. Devendhiran. "Leveraging Network Vulnerability Detection using Improved Import Vector Machine and Cuckoo Search based Grey Wolf Optimizer." In 2023 1st International Conference on Optimization Techniques for Learning (ICOTL). IEEE, 2023. http://dx.doi.org/10.1109/icotl59758.2023.10435119.

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