Estimation of chlorophyll-a in uncrewed aircraft systems imagery using autonomous surface vessel data with machine learning algorithms and feature selection techniques by Mohammad Shakiul Islam, Padmanava Dash, Abduselam M. Nur, Hafez Ahmad, Rajendra M. Panda, Jessica S. Wolfe, Gray Turnage, Lee Hathcock, Gary D. Chesser, Jr, Robert J. Moorhead

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Salp Swarm Algorithm for Node Localization in Wireless Sensor Networks by Huthaifa M. Kanoosh, Essam Halim Houssein, Mazen M. Selim

“…In this paper, a node localization scheme is proposed based on a recent bioinspired algorithm called Salp Swarm Algorithm (SSA). The proposed algorithm is compared to well-known optimization algorithms, namely, particle swarm optimization (PSO), Butterfly optimization algorithm (BOA), firefly algorithm (FA), and grey wolf optimizer (GWO) under different WSN deployments. …”
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Research on Fire Detection of Cotton Picker Based on Improved Algorithm by Zhai Shi, Fangwei Wu, Changjie Han, Dongdong Song

“…Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model. …”
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Multijunction solar cell parameter estimation based on metaheuristic algorithms by Marwa M. Elzalabani, Doaa M. Atia, Aref Y. Eliwa, Belal A. Abou Zalam, Mahmoud S. AbouOmar

“…The study compared the performance of five optimization algorithms: Grey Wolf Optimizer (GWO), Artificial Hummingbird Algorithm (AHA), Tunicate Swarm Algorithm (TSA), War Strategy Algorithm (WSA), and Moth flam optimizer (MFO). …”
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Water Quality Prediction Based on Hybrid Deep Learning Algorithm by Bhagavathi Perumal, Niveditha Rajarethinam, Anusuya Devi Velusamy, Venkatesa Prabhu Sundramurthy

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Bio-Inspired Metaheuristics in Deep Learning for Brain Tumor Segmentation: A Decade of Advances and Future Directions by Shoffan Saifullah, Rafał Dreżewski, Anton Yudhana, Wahyu Caesarendra, Nurul Huda

“…This review systematically examines developments from 2015 to 2025, focusing on the integration of nature-inspired optimization methods such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and novel hybrids including CJHBA and BioSwarmNet into deep learning-based brain tumor segmentation frameworks. …”
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Redefining IoT networks for improving energy and memory efficiency through compressive sensing paradigm by S. Balamurali, M. Kathirvelu, SatheeshKumar Palanisamy, Ines Hilali Jaghdam

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Personnel Scheduling Problem under Hierarchical Management Based on Intelligent Algorithm by Li Huang, Chunming Ye, Jie Gao, Po-Chou Shih, Franley Mngumi, Xun Mei

“…Two hybrid heuristic algorithms based on multiobjective grey wolf optimizer (MOGWO) and three corresponding single heuristic algorithms are employed to solve this problem. …”
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DATA DRIVEN RULE-BASED PEAK SHAVING ALGORITHM FOR SCHEDULING REFRIGERATORS by Daniel KWEGYIR, Francis Effah BOAFO, Daniel OPOKU, Emmanuel Asuming FRIMPONG

“…In the day-ahead mode, Long Short-Term Memory (LSTM) neural networks are utilized to forecast demand and generation. A Parameter tuned Grey Wolf Optimizer (GWOP) is proposed and employed to determine the optimal generation for the initial timestep of the scheduling period. …”
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A multi-objective improved horse herd optimizer based on convex lens imaging for stochastic optimization of wind energy resources in distribution networks considering reliability a... by Fude Duan, Ali Basem, Dheyaa J. Jasim, Mahdiyeh Eslami, Mustafa Okati

“…Moreover, the superiority of the MOIHHO is investigated in achieving better objective function value compared with conventional MOHHO, multi-objective particle swarm optimization (MOSPO), multi-objective gray wolf optimizer (MOGWO), and multi-objective gazelle optimization algorithm (MOGOA). …”
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Predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithms by Mana Alyami, Irfan Ullah, Furqan Ahmad, Hisham Alabduljabbar

“…Specifically, support vector regression (SVR) was combined with three optimization algorithms: firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO) to create hybrid models for estimating the SE of carbon fiber-reinforced mortars. …”
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A novel economic load dispatch method of microgrid based on hybrid slime mould and genetic algorithm by Wei Ba, Wei Sun, Chunjiang Zhao, Qi Li

“…For performance evaluation, GSMA is compared with slime mould algorithm (SMA), grey wolf optimizer (GWO), sparrow search algorithm (SSA), Harris Hawks optimization (HHO), whale optimization algorithm (WOA) and particle swarm optimization (PSO) using standard optimization functions. …”
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Optimizing Gammatone Cepstral Coefficients for Gear Fault Detection by Zrar Kh Abdul, Abdulbasit K. Al-Talabani, Wisam Hazim Gwad, Entisar Alkayal, Halgurd S. Maghdid, Safar Maghdid Asaad

“…In this research, three key parameters of GTCC, namely, the number of coefficients, maximum frequency, and minimum frequency, are optimized using two metaheuristic algorithms: Fitness Dependent Optimizer (FDO) and Grey Wolf Optimization (GWO). …”
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An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms by Maithri Bairy, Krishnaraj Chadaga, Niranjana Sampathila, R. Vijaya Arjunan, G. Muralidhar Bairy

“…Mutual information achieved a maximum testing accuracy of 90 % and highest precision of 94 %. The Whale Optimization Algorithm, Jaya Algorithm, Grey Wolf Optimizer and Sine Cosine Algorithm were the next best performing algorithms. …”
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Advanced control parameter optimization in DC motors and liquid level systems by Serdar Ekinci, Davut Izci, Mohammad H. Almomani, Kashif Saleem, Raed Abu Zitar, Aseel Smerat, Vaclav Snasel, Absalom E. Ezugwu, Laith Abualigah

“…Comparative assessments with competitive algorithms, such as the grey wolf optimizer and particle swarm optimization, reveal MGO’s superior performance. …”
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Multi-objective quantum hybrid evolutionary algorithms for enhancing quality-of-service in internet of things by Shailendra Pratap Singh, Gyanendra Kumar, Umakant Ahirwar, Shitharth Selvarajan, Firoz Khan

“…To address these issues, we introduce a quantum-inspired hybrid algorithm that combines the strengths of Multi-Objective Grey Wolf Optimization Algorithm (MOGWOA) and Multi-Objective Whale Optimization Algorithm (MOWOA), enhanced with quantum principles. …”
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Enhancing network lifetime in WSNs through coot algorithm-based energy management model by Namita Shinde, Dr. Vinod H․ Patil

“…To improve the performance of Wireless Sensor Networks (WSN), this study offers a novel energy-efficient clustering and routing technique based on the Coot Optimization Algorithm (COA). This addresses issues such as high energy consumption, communication delays, and security.To ensure energy savings and network reliability, the fitness function evaluates cluster heads and best routes based on constraints.COOT outperforms other Metaheuristics Algorithms like Butterfly Optimization Algorithm, Genetic Algorithm, Tunicate Swarm Gray Wolf Optimization Algorithm, and Bird Swarm Algorithm in simulation with performance measurements and enhancing network functionality and protection.Key methodology points include: • Proposed a multiple constraints clustering and routing technique using COAto solve the most crucial issues that arise in WSNs. • Integrated an advanced fitness function that determines cluster head selection, and the routing path based on residual energy, delay, security, trust, distance, and link quality so that energy load is evenly distributed and credible data flow is maintained across the network and made Innovative and Effective Solution. • Proven Results Demonstrated superior network performance, achieving the lowest delay, highest network lifetime (3571 rounds) and enhanced security (0.8) and trust (0.6) compared to existing algorithms with less energy consumption, making it the most suitable solution for WSN performance improvement.…”
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Chaotic chimp-mountain gazelle optimized FOPID control for frequency regulation in islanded airport microgrids with heterogeneous energy systems by Odelu P., Chandan Kumar Shiva, Sachidananda Sen, Vedik Basetti, Chandra Sekhar Reddy

“…Simulation results demonstrates that the CCMGO optimized fractional order proportional-integral-derivative controller exhibits better performances compared to the conventional genetic algorithm and particle swarm optimization based controllers, as well as contemporary metaheuristic algorithms like grey wolf optimizer and whale optimization algorithm. …”
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A Novel Method of Parameter Identification for Lithium-Ion Batteries Based on Elite Opposition-Based Learning Snake Optimization by Wuke Li, Ying Xiong, Shiqi Zhang, Xi Fan, Rui Wang, Patrick Wong

“…EOLSO outperforms some traditional optimization methods, including the Gray Wolf Optimizer (GWO), Honey Badger Algorithm (HBA), Golden Jackal Optimizer (GJO), Enhanced Snake Optimizer (ESO), and Snake Optimizer (SO), in both standard functions and HPPC experiments. …”
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Performance Evaluation of Hybrid Bio-Inspired and Deep Learning Algorithms in Gene Selection and Cancer Classification by Shahad S. Alkamli, Hala M. Alshamlan

“…Our findings reveal that hybrid bio-inspired methods, such as Grey Wolf Optimizer and Harris Hawks Optimization, achieve high classification accuracy with minimal selected genes, making them computationally efficient for clinical applications. …”
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