Study on the probabilistic characteristics of forces in the support structure of heliostat array based on the DBO-BP algorithm by Haiyin Luo, Qiwei Xiong, Xuewen Zhang, Shi Zuo

“…Dung beetle optimization (DBO) algorithm is a metaheuristic algorithm mimicking dung beetle ball-rolling behavior, while Back propagation (BP) neural network is a feedforward artificial neural network trained via error backpropagation to adjust parameters. …”
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Quantum-Enhanced Generalized Pattern Search Optimization by Colton Mikes, David Huckleberry Gutman, Victoria E. Howle

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An Improved Whale Algorithm for Setting Standard Scheduled Block Time Based on the Airline Fairness by Qian Wang, Yong Tian, Lili Lin, Ratnaji Vanga, Lina Ma

“…The objective of this paper is to develop and solve a mathematical model for standard SBT setting with consideration of both fairness and reliability. We use whale optimization algorithm (WOA) and an improved version of the whale optimization algorithm (IWOA) to solve the SBT setting problem. …”
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A novel Parrot Optimizer for robust and scalable PEMFC parameter optimization by Mohammad Aljaidi, Pradeep Jangir, Arpita, Sunilkumar P. Agrawal, Sundaram B. Pandya, Anil Parmar, Gulothungan G., Ali Fayez Alkoradees, Mohammad Khishe, Reena Jangid

“…I–V and V–P characteristic simulations match experimental results across different temperature and pressure values which proves the theoretical value and practical usage of PO in solving nonlinear optimization problems. The study demonstrates PO as a dependable optimization method which improves PEMFC design processes while enhancing operational reliability through future research that includes real-time control and algorithm combination and system scalability.…”
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Space Trajectory Planning with a General Reinforcement-Learning Algorithm by Andrea Forestieri, Lorenzo Casalino

“…The proposed framework integrates Monte Carlo Tree Search with a neural network to efficiently explore and optimize space trajectories. While developed for space trajectory planning, the algorithm is broadly applicable to any problem involving hybrid action spaces. …”
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Design of a liquid cooled battery thermal management system using neural networks, cheetah optimizer and salp swarm algorithm by Anjan Kumar, Laith Hussein Jasim, Padmanabha Vijaya, Dipak Patel, J. Gowrishankar, R. Sivaranjani, Ankur Srivastava, Mayank Kundlas, Sarbeswara Hota, Banafshe Hamidi

“…In the first phase, predictive modeling was performed using multilayer perceptron neural networks (MLPNN) optimized by three metaheuristic algorithms: cheetah optimizer (CO), grey wolf optimizer (GWO), and marine predators algorithm (MPA). …”
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Parameter Optimization of Milling Process for Surface Roughness Constraints by GUO Bin, YUE Caixu, ZHANG Anshan, JIANG Zhipeng, YUE Daxun, QIN Yiyuan

“… In the milling process of 6061 aluminum considering the requirement of controlling the surface roughness of workpiece, artificially selected milling parameters may be conservative, resulting in low material removal rate and high manufacturing cost.Taking the surface roughness as the constraint condition and the maximum material removal rate as the goal, the surface roughness regression model is established based on extreme gradient boosting (XGBOOST) with the spindle speed, feed speed and cutting depth as the optimization objects.The milling parameters of spindle speed, feed speed and cutting depth are optimized by genetic algorithm.The optimal milling parameters are obtained by using the multi objective optimization characteristics of genetic algorithm.It can be seen from the four groups of optimization results that the maximum change of surface roughness is only 0.048μm, while the minimum material removal rate increases by 2458.048mm3/min.While achieving surface roughness, the processing efficiency is improved, and the manufacturing costs are reduced, resulting in good optimization effects, which has a certain guiding role in the actual processing.…”
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Optimizing energy storage and return of prosthetic feet: A biomechanical approach using advanced optimization techniques by Aphiwat Saemua, Supakit Rooppakhun

“…This study introduces a novel design framework that combines Latin Hypercube Sampling (LHS), Kriging, and a Multi-Objective Genetic Algorithm (MOGA) to optimize weight, stiffness, and energy return. …”
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Dynamic Optimization of Xylitol Production Using Legendre-Based Control Parameterization by Eugenia Gutiérrez, Marianela Noriega, Cecilia Fernández, Nadia Pantano, Leandro Rodriguez, Gustavo Scaglia

“…The proposed technique employs Legendre polynomials to parameterize two control actions (the feeding rates of glucose and xylose), and it uses a hybrid optimization algorithm combining Monte Carlo sampling with genetic algorithms for coefficient selection. …”
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Application of GA-ACO Optimized BP Neural Network in Fault Diagnosis of Planetary Gearbox by Chang Gao, Zhongqing Yu, Qiang Zhou

“…Comparing the performance of ACO-BP neural network algorithm and GA-ACO-BP algorithm， the results show that the convergence speed of ACO Optimized BP neural network is slow and the recognition accuracy is not high， while GA-ACO-BP algorithm can accurately and quickly diagnose and identify the fault of planetary gearbox.…”
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Optimization of multi-user scheduling in WPCNs by Lina Yuan, Huajun Chen, Tingsui Liu, Ging Gong

“…The findings provide theoretical and practical insights for WPCNs optimization and deployment. Future research will explore algorithm optimization, practical deployment strategies, and network security to advance wireless power network technology.…”
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Slope stability prediction under seismic loading based on the EO-LightGBM algorithm by Ning Ma, Ning Ma, Yuqi Zhang, Zaizhen Yao

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Energy-Efficient DC Power Traction Network Systems for Urban Mass Transportation: A Comparative Study of Optimization Algorithms by Dongxian Yu, Zhixin Wu, Lei Wang, Zaihui Cao, Hamdolah Aliev

“…To validate the proposed method, we compare the performance of the GWO algorithm with other optimization techniques, such as the Teaching-Learning-Based Optimizer (TLBO) and Differential Evolution (DE). …”
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Optimizing Assembly Error Reduction in Wind Turbine Gearboxes Using Parallel Assembly Sequence Planning and Hybrid Particle Swarm-Bacteria Foraging Optimization Algorithm by Sydney Mutale, Yong Wang, De Tian

“…This study introduces a novel approach for minimizing assembly errors in wind turbine gearboxes using a hybrid optimization algorithm, Particle Swarm-Bacteria Foraging Optimization (PSBFO). …”
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Facial recognition optimization based on adversarial sample generation in the field of artificial intelligence by Jie Yang

“…Firstly, the traditional AdaBoost is improved using particle swarm optimization algorithm and dual threshold classification method. …”
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Developing an Optimization Model for Minimizing Solid Waste Collection Costs by Muciz Özcan, Semih Cengiz, Mehmet Şen

“…The Simulated Annealing (SA) algorithm, one of the heuristic optimization techniques used to identify the best solutions to complicated problems, is employed to solve the routing problem of solid waste collection vehicles in this study. …”
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Optimization model of electricity metering management based on MOPSO by Sheng Li, Xiaodan Zhou

“…The study uses Schaffer and Griewank functions to test its performance. The optimized algorithm performed well on both Schaffer and Griewank functions, indicating that the algorithm still had high computational efficiency while ensuring computational accuracy. …”
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Classification Based on Brain Storm Optimization With Feature Selection by Yu Xue, Yan Zhao, Adam Slowik

“…Moreover, the optimal weight parameters corresponding to the different structures are also searched by the BSO algorithm while searching the optimal feature subset. …”
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An artificial intelligence technique: experimental analysis of population-based physarum-energy optimization algorithm by Mehmet Fatih Demiral

“…The computational results show that the PEOs and their hybrids (k-NN, 2-opt, 3-opt, k-opt) can find high-quality results compared to the original physarum-energy optimization algorithm (PEO), ant colony optimization (ACO), black hole algorithm (BH), tabu search (TS), and other hybrid algorithms such as whale optimization algorithm + 4-opt heuristic (WOA + 4-opt), camel algorithm + 4-opt heuristic (CA + 4-opt), genetic algorithm + 2-opt heuristic (GA + 2-opt), and the k-NN, 2-opt, 3-opt, k-opt algorithms. …”
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Optimization of the control system of BP-PID rice polishing unit based on WAO algorithm by HUANG Jinliang, ZHOU Jin, YU Wei

“…Finally, the parameters of the PID controller of the traditional unit were trained by the BP neural network, and the whale algorithm (WOA) was introduced to optimize the BP neural network to achieve fast and accurate control of the rice polishing unit.ResultsCompared with the traditional PID control unit, the overall fragmentation rate of the BP-PID controlled unit optimized by the WAO algorithm was reduced by about 2%, the control time was reduced by about 30%, and the temperature rise decreased by about 2 ℃ at room temperature.ConclusionThe control system can effectively complete the adjustment of the processing parameters of the unit and has a good control effect.…”
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