Smart Energy Strategy for AC Microgrids to Enhance Economic Performance in Grid-Connected and Standalone Operations: A Gray Wolf Optimizer Approach by Sebastian Lobos-Cornejo, Luis Fernando Grisales-Noreña, Fabio Andrade, Oscar Danilo Montoya, Daniel Sanin-Villa

“…To assess performance, 100 independent runs per method were conducted, comparing GWO against particle swarm optimization (PSO) and genetic algorithms (GAs). …”
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Enhanced grey wolf optimization for maximum power point tracking in photovoltaic systems with hybrid battery-supercapacitor storage by Chirine Benzazah, Najoua Mrabet, Ahmed ElAkkary, Fathallah Rerhrhaye

“…The proposed method was compared with conventional and metaheuristic optimisation techniques, including Particle Swarm Optimization, Ant Colony Optimization, and the standard Grey Wolf Optimization algorithm. …”
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Medium- and Long-term Runoff Prediction Based on SMA-LSSVM by TIAN Jinghuan, LI Congxin, LI Ang

“…Medium-and long-term runoff prediction is extremely important for flood control,disaster reduction and the utilization efficiency improvement of water resources.To avoid the influence of prediction model parameters on prediction accuracy,this paper proposes a medium-and long-term runoff prediction model based on least squares support vector machine (LSSVM) optimized by the slime mold algorithm (SMA).Firstly,five standard test functions are selected to compare the simulation results of SMA and particle swarm optimization (PSO) algorithms in different dimensions.Secondly,SMA is used to optimize the penalty parameters and kernel parameters of LSSVM,and the comparison models of LSSVM and PSO-LSSVM are constructed.Finally,the models are verified with the monthly runoff of Manwan Hydropower Station Reservoir and Yingluoxia Hydrological Station as prediction examples.The results show that the mean square error of the SMA-LSSVM model is 29.26% and 7.42% lower than those of the LSSVM and PSO-LSSVM models,respectively,in the monthly runoff prediction of the Manwan station,and 32.61% and 6.61% lower,respectively,in the monthly runoff prediction of the Yingluoxia station.The proposed SMA-LSSVM model has better comprehensive prediction performance and also provides a new method for medium- and long-term runoff prediction.…”
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Alternating current servo motor and programmable logic controller coupled with a pipe cutting machine based on human-machine interface using dandelion optimizer algorithm - attenti... by Santosh Prabhakar Agnihotri, Mandar Padmakar Joshi

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Neural network-based link prediction algorithm by Yonghao PAN, Hongtao YU, Shuxin LIU

“…To improve the difference existed in the link prediction accuracy and adaptability of different topology structure similarity based methods,a neural network-based link prediction algorithm,which fused similarity indices by neural network was proposed.The algorithm uses neural network to study the numerical characteristics of different similarity indices,and uses particle swarm optimization to optimize the neural network,and calculates the fusion index by the optimized neural network model.The experiment on the real network data set shows that the prediction accuracy of the algorithm is obviously higher than that before the fusion,and the accuracy is better than the existing methods.…”
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Reinforcing long lead time drought forecasting with a novel hybrid deep learning model: a case study in Iran by Mahnoosh Moghaddasi, Mansour Moradi, Mahdi Mohammadi Ghaleni, Zaher Mundher Yaseen

“…Key parameters of the DFFNN, including the number of neurons and layers, learning rate, training function, and weight initialization, were optimized using the WSO algorithm. The model’s performance was validated against two established optimizers: Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). …”
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Link Prediction in Social Networks Using the HTOA by Foad Asef, Vahid Majidnezhad, Mohammad-Reza Feizi-Derakhshi

“…Comparisons with other optimization techniques, such as the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), reveal that the proposed method outperforms them in selecting key features and achieving faster convergence. …”
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QPSO-Based Adaptive DNA Computing Algorithm by Mehmet Karakose, Ugur Cigdem

“…This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). …”
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System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario by Wenhao Luo, Yuan Zeng, Ximeng Zheng, Lingyan Zha, Weicheng Cai, Qing Wang, Jingjin Zhang

“…Finally, a network coverage optimization scheme was designed by combining a particle swarm optimization (PSO) algorithm and link quality prediction model, and a reliable cluster routing protocol was designed by combining K-means algorithm. …”
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AMBWO: An Augmented Multi-Strategy Beluga Whale Optimization for Numerical Optimization Problems by Guoping You, Zengtong Lu, Zhipeng Qiu, Hao Cheng

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Optimal Assembly Position and Multi-objective Trajectory Optimization for Dual Robotic Arms Collaboration by Wang Tianrui, Tao Ping

“…In order to solve the limitation and randomness in determining the collaborative assembly position of dual robotic arms by the traditional manual demonstration method, and taking the coordinated assembly of dual robotic arms axle holes as the engineering background, this study uses the particle swarm algorithm to perform multiple searches for the optimal assembly position for the overall global flexibility in the collaborative assembly process and carries out multi-objective trajectory optimization based on the optimal position, with respect to the overall motion flexibility and trajectory planning of the robotic arm. …”
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Design of a Lorentz Force Magnetic Bearing Group Steering Law Based on an Adaptive Weighted Pseudo-Inverse Law by Chenyu Wang, Lei Li, Weijie Wang, Yanbin Zhao, Baiqi Li, Yuan Ren

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A precise ultra high frequency partial discharge location method for switchgear based on received signal strength ranging by Jiajia Song, Jinbo Zhang, Xinnan Fan

“…By combining the characteristics of ultra high frequency wireless sensor array positioning, the particle size is optimized. The simulation results show that the location accuracy using the ultra high frequency switchgear partial discharge location method based on received signal strength indicator ranging with the improved particle swarm optimization algorithm performs significantly better.…”
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Advanced Queueing and Location-Allocation Strategies for Sustainable Food Supply Chain by Amirmohammad Paksaz, Hanieh Zareian Beinabadi, Babak Moradi, Mobina Mousapour Mamoudan, Amir Aghsami

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Architectural space design methods using data algorithms by Jingyang Liu, Yifan Zhang, Shiru Zhao

“…Experts and scholars were invited to score to verify the superiority of particle swarm optimization algorithm for architectural space design. …”
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Modeling of strike drone missions for conducting wave attacks in conditions of enemy anti-drone actions by Oleg Fedorovich, Dmytro Krytskyi, Mikhail Lukhanin, Oleksandr Prokhorov, Yuliia Leshchenko

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Seakeeping of Hydrofoil-Equipped Unmanned Surface VehicleBased on LQR and ZOA by Xinhua SHUI, Fuhai DUAN

“…To minimize the motion amplitude of hydrofoil-equipped USV during navigation, the LQR parameters were optimized using ZOA and particle swarm optimization(PSO) algorithms， respectively under different sampling frequencies and population sizes for comparison. …”
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Chiller power consumption forecasting for commercial building based on hybrid convolution neural networks-long short-term memory model with barnacles mating optimizer by Mohd Herwan Sulaiman, Zuriani Mustaffa

“…The study compares the proposed CNN-LSTM-BMO against other metaheuristic optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Differential Evolution (DE). …”
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Optimizing sustainable blended concrete mixes using deep learning and multi-objective optimization by Rupesh Kumar Tipu, Preeti Rathi, Kartik S. Pandya, Vijay R. Panchal

“…The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm finds multiple optimal solutions which simultaneously optimize three competing objectives that include strength maximization and cost minimization and cement reduction. …”
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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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