Tramp Ship Routing and Scheduling with Integrated Carbon Intensity Indicator (CII) Optimization by Haiying Yang, Feiyang Ren, Jingbo Yin, Siqi Wang, Rafi Ullah Khan

“…This computational study, based on real historical data, verifies the effectiveness of the proposed model and algorithm. The results demonstrate notable improvements in fleet efficiency and environmental performance, increasing profitability by 4.38% while maintaining favorable CII ratings. …”
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Particle Swarm Optimization Based Optimal Design of Six-Phase Induction Motor for Electric Propulsion of Submarines by Lelisa Wogi, Amruth Thelkar, Tesfabirhan Shoga Tahiro, Tadele Ayana, Shabana Urooj, Samia Larguech

“…This research presented a comparison of optimal model design of a six phase squirrel cage induction motor (IM) for electric propulsion by using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). …”
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Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem by Binh Minh Nguyen, Trung Tran, Thieu Nguyen, Giang Nguyen

“…However, the optimization process still suffers poverty in the exploitation phase, which is improved in this work by its hybridization with our evolution version of the Whale Optimization Algorithm. …”
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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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SQNR Improvement Enabled by Nonuniform DAC Output Levels for IM-DD OFDM Systems by Jizong Peng, Lei Han, Qingming Zhu, Ciyuan Qiu, Yong Zhang, Christine Tremblay, Yikai Su

“…Our scheme enables transmission of such signals with no further optimization with ∼−25-dBm receiver sensitivity, while it is impossible using a conventional way. …”
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Improved energy efficiency using meta-heuristic approach for energy harvesting enabled IoT network by Rekha, Ritu Garg

“…OPA-APSO differs from most existing approaches as it considers the amount of energy harvested while optimizing the solution. Finally, simulation results demonstrate that OPA-APSO improves energy efficiency and throughput of the network significantly as compared to other existing techniques. …”
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Adaptive energy loss optimization in distributed networks using reinforcement learning-enhanced crow search algorithm by S. Bharath, A. Vasuki

“…By balancing exploration-exploitation using CSA while adapting search parameters through reinforcement learning, RL-CSA ensures scalability, improved DG utilization (98%), and better voltage stability (< 0.005 p.u.), making it a robust and intelligent alternative for modern smart grid optimization.…”
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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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Improved stereo matching network based on dense multi-scale feature guided cost aggregation by ZHANG Bo, ZHANG Meiling, LI Xue, ZHU Lei

“…To further improve the disparity prediction accuracy of stereo matching algorithm in the ill-posed regions such as repeating textures, no texture, and edge, an improved dense multi-scale feature guided aggregation network (DGNet) based on PSMNet was proposed. …”
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Dual Strategy of Reconfiguration with Capacitor Placement for Improvement Reliability and Power Quality in Distribution System by Ali Nasser Hussain, Wathiq Rafa Abed, Mohanad Muneer Yaqoob

“…While the constraints include limits of system reliability indices to provide optimal constraints on negative interactions of power quality. …”
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Reflective Distributed Denial of Service Detection: A Novel Model Utilizing Binary Particle Swarm Optimization—Simulated Annealing for Feature Selection and Gray Wolf Optimization-... by Daoqi Han, Honghui Li, Xueliang Fu

“…The BPSO-SA algorithm enhances the global search capability of Particle Swarm Optimization (PSO) using the SA mechanism and effectively screens out the optimal feature subset; the GWO algorithm optimizes the hyperparameters of LightGBM by simulating the group hunting behavior of gray wolves to enhance the detection performance of the model. …”
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Damage Identification in Large-Scale Structures Using Time Series Analysis and Improved Sparse Regularization by Huihui Chen, Xiaojing Yuan

“…Compared to the moth-flame optimization (MFO) algorithm and traditional regularization methods, for both noise-free and noise polluted data, the iteration curves illustrate that the proposed method can achieve convergence within about 200 iterations, while the MFO algorithm is always trapped into local optima; meanwhile, the traditional regularization method needs more iterations or even cannot meet the preset tolerance. …”
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Nonlinear Stability Analysis of Shallow-Buried Bias Tunnel Based on Failure Mode Improvement by Wei Luo, Gequan Xiao, Zhi Tao, Jingyu Chen, Xi Lu, Haifeng Wang

“…The SQP algorithm in MATLAB (R2022a) software was used to optimize the solution, and the influence of slope top load, buried depth ratio, cohesion, and axial tensile stress on the surrounding rock pressure and the failure mode of shallow-buried tunnel was analyzed through examples. …”
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The Study of Roadside Visual Perception in Internet of Vehicles Based on Improved YOLOv5 and CombineSORT by LI Xiaohui, YANG Jie, XIA Qin

“…On the contrary, the algorithms applying YOLOv5, YOLOX, YOLOv7 and the paper's improved YOLOv5 achieved the recall rates from 95.26% to 96.28%, while algorithms applying DeepSORT, StrongSORT, Bot-SORT and CombineSORT achieved the MOTA values from 0.887 to 0.901. …”
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Improved Cylinder-Based Tree Trunk Detection in LiDAR Point Clouds for Forestry Applications by Shaobo Ma, Yongkang Chen, Zhefan Li, Junlin Chen, Xiaolan Zhong

“…To address these challenges, this study proposes an improved individual tree trunk detection algorithm, Random Sample Consensus Cylinder Fitting (RANSAC-CyF), specifically optimized for detecting cylindrical tree trunks. …”
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ZZ-YOLOv11: A Lightweight Vehicle Detection Model Based on Improved YOLOv11 by Zhe Zhang, Zhongyang Zhang, Gang Li, Chenxi Xia

“…Aiming at the problems of insufficient vehicle detection accuracy, high misdetection and omission rate, and heavy model computational burden caused by complex lighting conditions, target occlusion, and other factors in urban traffic scenarios, this paper proposes an improved lightweight detection network, ZZ-YOLO. Firstly, the current mainstream target detection algorithms lack components to improve the network’s focus on the edges of the objects, which can indirectly lead to unclear classification and localization. …”
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Prediction for Coastal Wind Speed Based on Improved Variational Mode Decomposition and Recurrent Neural Network by Muyuan Du, Zhimeng Zhang, Chunning Ji

“…This study proposes a systematic framework, termed VMD-RUN-Seq2Seq-Attention, for noise reduction, outlier detection, and wind speed prediction by integrating Variational Mode Decomposition (VMD), the Runge–Kutta optimization algorithm (RUN), and a Sequence-to-Sequence model with an Attention mechanism (Seq2Seq-Attention). …”
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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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Improving Cardiovascular Disease Prediction through Stratified Machine Learning Models and Combined Datasets by Tara Yousif Mawlood, Alla Ahmad Hassan, Rebwar Khalid Muhammed, Aso M. Aladdin, Tarik A. Rashid, Bryar A. Hassan

“…Results revealed that ensemble models, particularly RF and DT, achieved optimal performance with 100% accuracy, while stratification significantly improved the outcomes of SVM, GNB, and GB. …”
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Low-carbon economic dispatch based on improved ISODATA scenario reduction for wind power in IES by Yuangen HUANG, Xingyu LIU, Tianran LI, Zhenya JI, Wei XU

“…A low-carbon, economic dispatch method for IES using an enhanced wind power scenario reduction algorithm is introduced in this paper. It employs an improved iterative self-organizing data analysis technique algorithm (ISODATA) for clustering historical wind power scenarios, addressing the limitations of traditional clustering algorithms in determining cluster centers and analyzing inherent data features. …”
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