A novel two stage neighborhood search for flexible job shop scheduling problem considering reconfigurable machine tools by Yanjun Shi, Chengjia Yu, Shiduo Ning

“…This paper focuses on the flexible job shop scheduling problem with machine reconfigurations (FJSP-MR) and proposes an improved genetic algorithm with a two-stage neighborhood search (IGA-TNS) to minimize total weighted tardiness (TWT). …”
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Optimization of Reverse Logistics Networks for Hazardous Waste Incorporating Health, Safety, and Environmental Management: Insights from Large Cruise Ship Construction by Huilin Li, Jiaqi Yang, Wei Zhang

“…Realistic factors including vehicle load, transport cost, loading time, and risk weight were considered to improve model applicability. …”
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Digital Industrial Design Method in Architectural Design by Machine Learning Optimization: Towards Sustainable Construction Practices of Geopolymer Concrete by Xiaoyan Wang, Yantao Zhong, Fei Zhu, Jiandong Huang

“…A dataset comprising 63 observations from a quarry mine in Malaysia is employed, with influential parameters normalized and utilized for model development. Consequently, we integrate optimization algorithms (GOA and GWO) with MLP to fine-tune the model’s parameters and improve prediction accuracy. …”
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Optimization Configuration of Electric–Hydrogen Hybrid Energy Storage System Considering Power Grid Voltage Stability by Yunfei Xu, Yiqiong He, Hongyang Liu, Heran Kang, Jie Chen, Wei Yue, Wencong Xiao, Zhenning Pan

“…This model, applied to the IEEE-33 standard test system, utilizes the Multi-Objective Artificial Hummingbird Algorithm (MOAHA) to optimize the capacity and location of the electric–hydrogen hybrid energy storage system. …”
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Microservice Workflow Scheduling with a Resource Configuration Model Under Deadline and Reliability Constraints by Wenzheng Li, Xiaoping Li, Long Chen, Mingjing Wang

“…Experiments on four scientific workflow datasets show that the proposed approach achieves an average cost reduction of 44.59% compared to existing reliability scheduling algorithms, with improvements of 26.63% in the worst case and 73.72% in the best case.…”
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Multi-Scenario Stochastic Optimal Scheduling for Power Systems With Source-Load Matching Based on Pseudo-Inverse Laguerre Polynomials by Jiahao Ye, Lirong Xie, Lan Ma, Yifan Bian, Chuanshi Cui

“…Firstly, to improve the accuracy and stability of wind-photovoltaic power forecasting, a novel multi-objective wind-photovoltaic forecasting model is proposed based on the Laguerre polynomial, pseudo-inverse learning, and hybrid multi-objective Runge-Kutta algorithm (HMORUN). …”
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A Minimized Data Collection Optimization Method for Distribution Networks Considering Multiple-Time and Compressed Candidate Sets by Zhaoxiang YUAN, Zhihong XIAO, Jing WANG, Yanling YU, Yan HUANG, Xingle GAO

“…The minimized data collection technology for distribution networks can optimize the measurement configuration with the minimum economic cost, which plays an important role in improving the system observability. …”
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Encapsulation of anticancer drugs into carbon nanotubes: Heuristic algorithm approach and mathematical model. by Kanes Sumetpipat, Duangkamon Baowan

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Modeling and Solving the Multi-Objective Vehicle Routing Problem with Soft and Fuzzy Time Windows by Ailing Chen, Tianao Li

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A Comprehensive Review of Sizing and Energy Management Strategies for Optimal Planning of Microgrids with PV and Other Renewable Integration by Fadi Agha Kassab, Rusber Rodriguez, Berk Celik, Fabrice Locment, Manuela Sechilariu

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Hierarchical guided manta ray foraging optimization for global continuous optimization problems and parameter estimation of solar photovoltaic models by Zhentao Tang, Kaiyu Wang, Lan Zhuang, Mingxin Zhu, Yongxuan Yao, Huiqin Chen, Jing Li, Xiaoxiang Wu, Shangce Gao

“…Abstract The manta ray foraging optimization (MRFO) algorithm has gained widespread application in engineering problems due to its notable performance and low computational cost. …”
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Charge and discharge scheduling method for large-scale electric vehicles in V2G mode via MLGCSO by Songling Pang, Kaidi Fan, Meiyi Huo

“…Compared with traditional methods, the diversity and convergence of particle swarm learning are enhanced, and the optimization performance is improved. Simulation results indicate that when compared with three state-of-the-art optimizers, the optimization accuracy of the proposed algorithm is increased by at least 34% and the total cost is reduced by 3.14% and 1.62% respectively, demonstrating that the MLGCSO exhibits high optimization performance and remarkable optimization effects.…”
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Hybrid extreme learning machine for real-time rate of penetration prediction by Abdelhamid Kenioua, Omar Djebili, Ammar Touati Brahim

“…Abstract This study presents a comparative analysis of hybrid Extreme Learning Machine (ELM) models optimized with metaheuristic algorithms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO) for real-time Rate of Penetration (ROP) prediction in drilling operations. …”
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Optimizing Berth Allocation for Maritime Autonomous Surface Ships (MASSs) in the Context of Mixed Operation Scenarios by Lixin Shen, Xueting Shu, Chengcheng Li, Tomaž Kramberger, Xiaoguang Li, Lixin Jiang

“…A large-scale simulation of the mixed-type berth allocation model is carried out using an improved simulated annealing algorithm. …”
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Deploying UAV-based detection of bridge structural deterioration with pilgrimage walk optimization-lite for computer vision by Jui-Sheng Chou, Chi-Yun Liu, Pin-Jun Guo

“…This system uses UAVs to capture high-resolution images, which are then processed by the You Only Look Once (YOLO) models for instance segmentation. The YOLOv7 model, fine-tuned with the Pilgrimage Walk Optimization (PWO)-Lite algorithm, achieved the highest accuracy, recording a 65.6 % mAP50 on the testing set. …”
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Research on switch migration method based on minimum cost path by Yingxu LAI, Yewei PU, Jing LIU

“…In order to protect the controller,especially the controller in backbone network,from security threats and attacks,improve the security of the software-defined network (SDN) control plane,a switch migration algorithm based on minimum cost path was proposed.A load prediction module was added to the migration model,which executed a controller load prediction algorithm to obtain a load prediction matrix,and then a migration-target controller set was determined according to the load prediction matrix.The improved Dijkstra algorithm was used to determine the minimum cost path.According to the load state of the controller and the traffic priority of the switch to be migrated,the optimal migration switch set was determined.The problem of isolated nodes was solved that may occur during the migration process.The experimental results show that the migration timing of the algorithm is more reasonable,the selection of the migration controller and the target controller is more reasonable,the load balancing of the control plane is realized,the number of migrations and cost are reduced,and the performance of the controller is improved.…”
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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

“…In addition, The Newton-Raphson Method (NRM) hybrid with meta-heuristic algorithms, or MOAs, is used to improve solution estimates, accelerate convergence rates, reduce computational costs, and enhance robustness. …”
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Methods and Algorithms for Predictive Analytics of Time Series in Energy Consumption by Aleksandr Karmanov

“…States must minimize carbon dioxide emissions and businesses must optimize their energy costs. In this regard, improving energy efficiency plays a key role in solving the climate crisis and reducing costs for businesses. …”
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Enhancing Lidar-Based 3D Classification Through an Improved Deep Learning Framework With Residual Connections by Shih-Lin Lin, Jun-Yi Wu

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UAV Path Planners in Complex Environments: A Multi-Dimensional Perturbation Based on Artificial Bee Colony by Xuzhao Chai, Guanhao Zhou, Haoyu Wang, Ping Liu, Zhongyun Liu, Chao Li, Li Yan

“…In the onlooker bee phase, the curvature-guided elite neighbor search strategy is used to prioritizes high-curvature waypoints, enhancing optimization efficiency in complex terrain. Furthermore, path costs are independently modeled in the horizontal and vertical directions. …”
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