Chameleon swarm algorithm with Morlet wavelet mutation for superior optimization performance by Vipan Kusla, Gurbinder Singh Brar, Harpreet Kaur, Ramandeep Sandhu, Chander Prabha, Md. Mehedi Hassan, Shahab Abdulla, Md Rittique Alam, Samah Alshathri, Walid El-Shafai

“…Five performance metrics—average energy consumption, total energy consumption, total residual energy, dead node and cluster head frequency are taken into consideration when evaluating the performances against state-of-the-art algorithms. For nine different simulation scenarios, the proposed algorithm mCSAMWL outperforms the Atom Search Optimization (ASO), Hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSO-GWO), Bald Eagle Search Algorithm (BES), the African Vulture Optimization Algorithm (AVOA), and the Chameleon Swarm Algorithm (CSA) in terms of average energy consumption and total energy consumption by 50.9%, 52.6%, 45%, 42.4%, 50.1% and 51.4%, 53.3%, 45.6%, 42.4%, 50.7%.…”
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Optimizing Kernel Extreme Learning Machine based on a Enhanced Adaptive Whale Optimization Algorithm for classification task. by ZeSheng Lin

“…Furthermore, inspired by the grey wolf optimization algorithm, use 3 excellent particle surround strategies instead of the original random selecting particles. …”
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Dynamic performance improvement of oscillating water column wave energy conversion system using optimal walrus optimization algorithm-based control strategy by Habiba A. ElDemery, Hany M. Hasanien, Mohammed Alharbi, Chuanyu Sun, Dina A. Zaky

“…The proposed WOA-based PI controller design’s effectiveness is evaluated by comparing its simulation results with that obtained from using genetic algorithm (GA), grey wolf (GWO), particle swarm (PWO), and harmony search (HS) optimization-based PI controllers under symmetrical and unsymmetrical faults. …”
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Optimizing Photovoltaic Panel Performance: A Comparative Study of Meta-Heuristic Algorithms by M. Sundar Rajan

“…This method was compared against other well-known algorithms such as Artificial Bee Colony (ABC), Dragonfly Algorithm (DA), Grey Wolf Optimizer (GWO), Cuckoo Search (CS), Composite Grey Wolf Optimizer (CGWO), and the standalone PSO and GWO methods. …”
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Robotic arm path planning based on the improved dung beetle optimization algorithm by XUAN Yitong, LI Lijun, CHEN Haifei

“…Thirdly, the comparative simulations were conducted using basic dung beetle algorithm, jellyfish algorithm, grey wolf algorithm, whale algorithm, and the improved dung beetle algorithm in Matlab software. …”
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Optimal restoration algorithm for active distribution network considering island mode of the microgrids by J. Marín Quintero, J. Ayala Uribe, O. Bernal, C. Orozco Henao

“…This paper introduces a restoration strategy for Active Distribution Networks based on the Binary Grey Wolf Optimization (BGWO) algorithm. The approach accounts for the island operation mode of MGs and leverages the controllability of intelligent electronic devices (IEDs). …”
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Queueing modeling and optimization of hybrid electric vehicle infrastructures using evolutionary algorithms by Shreekant Varshney, Manthan Shah, Bhasuru Abhinaya Srinivas, Mayank Gupta, Kaibalya Prasad Panda

“…The novelty of the proposed research is to formulate the cost optimization problem and demonstrate optimal converging outcomes through an in-depth comparative investigation of multiple evolutionary algorithms, including the particle swarm optimization (PSO), cuckoo search (CS), grey wolf (GW), and honey badger (HB) optimizers. …”
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Comparative Analysis of Optimization Algorithms to Enhance WPGSs Performance: Malaysia Case Study by Hussein Shutari, Taib Ibrahim, Nursyarizal Bin Mohd Nor, Hakim Abdulrab, Yahya Z. Alharthi, Mohammad Faridun Naim Tajuddin, Maveeya Baba

“…The growing interest in wind power and its expanding integration into the electrical power grid necessitate significant efforts to enhance the efficiency and performance of wind power generation Systems (WPGS) through optimized design and operation. This paper investigates the implementation of five optimization algorithms:sine cosine algorithm (SCA), grey wolf optimizer (GWO), particle swarm optimizer (PSO), transient search optimization (TSO), and a hybrid sine cosine algorithm-transient search optimizer (HSCATSO), to optimally design control schemes (ODCSs) of frequency converters within grid-connected WPGS. …”
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An efficient metaheuristic optimization algorithm for optimal power extraction from PV systems under various weather and load-changing conditions by Md.Al Imran Fahim, Md.Salah Uddin Yusuf, Monira Islam, Munshi Jawad Ibne Azad

“…A comprehensive study compares the HHO technique with established methods such as perturb and observe (P&O), modified P&O (MP&O), incremental conductance (IC), Spline MPPT, particle swarm optimization (PSO), grasshopper optimization (GHO), and grey wolf optimization (GWO) across fast-changing irradiance, partial shading, complex partial shading, and load-changing conditions. …”
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Optimal sizing and placement of STATCOM, TCSC and UPFC using a novel hybrid genetic algorithm-improved particle swarm optimization by Urbanus Mwanzia Ngei, Abraham Mutunga Nyete, Peter Musau Moses, Cyrus Wekesa

“…Comparison of GA-IPSO technique with other algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Improved Grey Wolf Optimization (IGWO) and Differential Evolution Algorithm (DEA) showed that the proposed hybrid technique was superior and more efficient in solving the FACTS optimization problem.…”
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Various optimization algorithms for efficient placement and sizing of photovoltaic distributed generations in different networks. by Ahmed A Zaki Diab, Fayza S Mahmoud, Hamdy M Sultan, Abou-Hashema M El-Sayed, Mohamed A Ismeil, Omar Makram Kamel

“…The key implications of integrating RDGs include the improvement of voltage profiles and the minimization of power losses. Various optimization techniques, namely Salp Swarm Algorithm (SSA), Marine Predictor Algorithm (MPA), Grey Wolf Optimizer (GWO), Improved Grey Wolf Optimizer (IGWO), and Seagull Optimization Algorithm (SOA), have been applied to achieve optimal allocation and sizing of RDGs in radial distributed systems (RDS). …”
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Optimizing Solid Oxide Fuel Cell Performance Using Advanced Meta-Heuristic Algorithms by Siva Ram Rajeyyagari, Srinivas Nowduri

“…These include the Angle of Attack Optimization (AOA), Particle Swarm Optimization with Grey Wolf Optimizer (PSOGWO), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Moth Flame Optimization (MFO), and Multi-Verse Optimizer (MVO). …”
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Quadrotor Robust Fractional-Order Control Based on a Recent Bonobo Optimization Algorithm by Faycal Ziani, Aissa Benhammou, Mohamed Amine Hartani, Hegazy Rezk

“…The five fractional parameters for each engine are also improved using the Bonobo Optimization (BO) algorithm. The optimized results in this paper are compared with the algorithms used, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO). …”
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Multiobjective Optimal Control for Hydraulic Turbine Governing System Based on an Improved MOGWO Algorithm by Xin Xia, Jie Ji, Chao-shun Li, Xiaoming Xue, Xiaolu Wang, Chu Zhang

“…In order to optimize the solution to the multiobjective problems, a novel multiobjective grey wolf optimizer algorithm with searching factor (sMOGWO) is also proposed with two improvements: adding searching step to search more no-domain solutions nearby the wolves and adjusting control parameters to keep exploration ability in later period. …”
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Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms by Hu Caixiong, Zhang Lili, Li Haoran, Zhang Yaowen, Yao Yunsheng

“…A long short-term memory (LSTM) neural network combined with the gray wolf optimizer (GWO) algorithm was introduced to improve prediction accuracy. …”
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An Improved Whale Optimization Algorithm for the Integrated Scheduling of Automated Guided Vehicles and Yard Cranes by Shuaishuai Gong, Ping Lou, Jianmin Hu, Yuhang Zeng, Chuannian Fan

“…For container tasks of size 100, the objective function value (OFV) of the IWOA was reduced by 9.25% compared to the standard Whale Optimization Algorithm. Comparisons with other algorithms, such as the Genetic Algorithm, Particle Swarm Optimization, and Grey Wolf Optimizer, showed an OFV reduction of 9.61% to 11.75%. …”
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A New Bipolar Approach Based on the Rooster Algorithm Developed for Utilization in Optimization Problems by Mashar Cenk Gençal

“…Then, the performance of the BIRA algorithm was compared with the performances of meta-heuristic algorithms widely used in the literature, Standard Genetic Algorithm (SGA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Cuckoo Search (CS), and Grey Wolf Optimizer (GWO), and thus, its success in the literature was tested. …”
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Investigation of technoeconomic optimization for sizing renewable energy systems using metaheuristic and hybrid algorithms by Bouafia Mohammed, El Fathi Amine, El Akchioui Nabil

“…This study focuses on optimizing grid-connected Hybrid Renewable Energy Systems (HRES) in Morocco using technoeconomic approaches, including metaheuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimization (GWO) and Artificial Bee Colony (ABC) and hybrid methods combining these latter with Modified Marquardt Gradient Descent (MGD). …”
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DESIGN HYBRID META-HEURISTIC APPROACHES FOR IMPROVED RELIABILITY OPTIMIZATION by Shakuntla Singla, Manisha Rani, Shilpa Rani, A. K. Lal

“…This study evaluates four metaheuristic methods for SRGM variable estimation: Gravitational Search Algorithm (GSA), Sine-Cosine Algorithm (SCA), Grey-Wolf Optimizer (GWO), and Regenerative Genetic Algorithm (RGA). …”
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Innovative hybrid algorithm for efficient routing of limited capacity vehicles by Vu Hong Son Pham, Van Nam Nguyen, Nghiep Trinh Nguyen Dang

“…This study addresses the critical challenges posed by the capacitated vehicle routing problem (CVRP), particularly in the logistics of cement transportation under capacity constraints. Existing algorithms, including grey wolf optimizer (GWO) and whale optimization algorithm (WOA), exhibit significant limitations such as imbalanced exploration and exploitation, inefficiency in refining solutions, and inadequate adaptability to dynamic routing conditions. …”
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