Meta-transformer: leveraging metaheuristic algorithms for agricultural commodity price forecasting by G. H. Harish Nayak, Md. Wasi Alam, B. Samuel Naik, B. S. Varshini, G. Avinash, Rajeev Ranjan Kumar, Mrinmoy Ray, K. N. Singh

“…To address these challenges, this study proposes a novel framework that combines Transformer models with Metaheuristic Algorithms (MHAs), including the Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO) to enhance agricultural price forecasting accuracy. …”
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Integrating vision transformer-based deep learning model with kernel extreme learning machine for non-invasive diagnosis of neonatal jaundice using biomedical images by M. Eliazer, Sibi Amaran, K. Sreekumar, A. Vikram, Gyanendra Prasad Joshi, Woong Cho

“…Finally, the enhanced coati optimization algorithm (ECOA) method is implemented for the hyperparameter tuning of the KELM method, which results in a higher classification process. …”
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Research on the A* Algorithm Based on Adaptive Weights and Heuristic Reward Values by Xizheng Wang, Gang Li, Zijian Bian

“…Secondly, a radial basis function is used to act as the adaptive weighting coefficient of the heuristic function and adjust the proportion of heuristic functions in the algorithm accordingly to the search distance. Again, optimize the cost function using the reward value provided by the target point so that the current point is away from the local optimum. …”
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Optimization of the Operation Plan of Airport Express Train with Consideration of Train Departure Time Window by Jin He, Yinzhen Li, Yuhong Chao, Ruhu Gao

“…This paper proposes an optimization model for the train operation scheme of the Airport Express Line (AEL) based on the expected arrival time of passengers by the introduction of the train departure time to cope with the time-dependent passenger flow and provide better prompt train service according to passengers’ demand. …”
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Multi-Objective Optimization for Artificial Island Construction Scheduling Using Cooperative Differential Evolution by Tianju Zheng, Liping Sun, Jifeng Chen, Xinyuan Cui, Shuqi Li

“…Comparing MOCDE with established algorithms, results indicate that MOCDE improvements over previous SOTA models, achieving a reduction of 0.56% in Total Time, a decrease of 0.43% in Total Cost, and an enhancement of 7.38% in Total Quality. …”
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Coordinated Optimal Dispatch of Distribution Grids and P2P Energy Trading Markets by Jing Deng, Fawu He, Qingbin Zeng, Jie Yan, Rangxiong Liu, Dongsheng He, Song Zhou

“…To address the nonlinear, high‐dimensional optimization challenges, an improved Convex‐Soft Actor‐Critic (C‐SAC) algorithm is developed, combining deep reinforcement learning with convex optimization to achieve privacy‐preserving distributed coordination. …”
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Multi-objective optimization placement strategy for SDN security controller considering Byzantine attributes by Tao WANG, Hongchang CHEN

“…By giving the software defined network distributed control plane Byzantine attributes, its security can be effectively improved.In the process of realizing Byzantine attributes, the number and location of controllers, and the connection relationship between switches and controllers can directly affect the key network performance.Therefore, a controller multi-objective optimization placement strategy for SDN security controllers considering Byzantine attributes was proposed.Firstly, a Byzantine controller placement problem (MOSBCPP) model that comprehensively considered interaction delay, synchronization delay, load difference and the number of controllers was constructed.Then, a solution algorithm based on NASG-II was designed for this model, which included the initialization function, the mutation function, the fast non-dominated sorting function and the elite strategy selection function.Simulation results show that this strategy can effectively reduce interaction delay, synchronization delay, load difference and the number of controllers, while improving control plane security.…”
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Inverse methods and integral-differential model demonstration for optimal mechanical operation of power plants – numerical graphical optimization for second generation of tribology... by Casesnoves Francisco

“…Stepping forward from a previous conference contribution, the article focuses on extension of inverse problem algorithms to integral-differential modelling and formal/strict demonstration of graphical-optimization method. …”
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An unmanned intelligent inspection technology based on improved reinforcement learning algorithm for power large-area multi-scene inspection by Enmin Wang, Xin Meng, Jinglong Yu, Jiechang Wang, Liang Yin

“…Consequently, this study investigates a multi scene unmanned intelligent patrol technology for power large area, based on an improved reinforcement learning algorithm. The unmanned intelligent patrol model is designed according to the patrol UAVs, wireless charging piles distributed in appropriate locations, and the targets to be patrolled (i.e., multiple scenes within a large power area). …”
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An analytical optimal calibration framework of bonded particle model for rock strength envelop modelling by Xiaoxiong Zhou, Hongyi Xu, Qiuming Gong, Yanan Ma, Weiqiang Xie

“…Adaptive moment estimation (Adam) was chosen as the iterative optimization algorithm to avoid the vanishing gradient problem. …”
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Enhancing Sustainable Manufacturing in Industry 4.0: A Zero-Defect Approach Leveraging Effective Dynamic Quality Factors by Rouhollah Khakpour, Ahmad Ebrahimi, Seyed Mohammad Seyed Hosseini

“…The methodology follows these steps:</p> <p style="text-align: left;">Step 1: Analysing effective dynamic factors of product quality</p> <p style="text-align: left;">Step2: Evaluating Triple Bottom Line (TBL) criteria</p> <p style="text-align: left;">Step 3: Measuring current sustainability state</p> <p style="text-align: left;">Step 4: Implementing ZDM strategies</p> <p style="text-align: left;">Step 5: Measuring improvements in sustainability</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;"><strong>Results</strong></p> <p style="text-align: left;">&nbsp;<strong>Effects</strong> <strong>of Single Unit Defective Product on TBL Sustainability State in Value Stream</strong></p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">Summary of current sustainability state</p> <table style="float: left;" width="479"> <tbody> <tr> <td width="64"> <p>Product model</p> </td> <td width="56"> <p>Daily schedule (set)</p> </td> <td width="61"> <p>Defective product rate (%)</p> </td> <td width="58"> <p>Number of defective products (set)</p> </td> <td width="85"> <p>Environmental sustainability</p> <p>State</p> </td> <td width="78"> <p>Social sustainability</p> <p>state</p> </td> <td width="78"> <p>Economic sustainability</p> <p>state</p> </td> </tr> <tr> <td width="64"> <p>Refrigerator</p> </td> <td width="56"> <p>480 set</p> </td> <td width="61"> <p>3%</p> </td> <td width="58"> <p>15</p> </td> <td width="85"> <p>Wasted material: 15 set</p> <p>&nbsp;</p> <p>Wasted energy: 239.25 kwh</p> </td> <td width="78"> <p>Waste of manpower: 1650 pmin</p> </td> <td width="78"> <p>Wasted costs:</p> <p>3265.65 $</p> </td> </tr> </tbody> </table> <p style="text-align: left;"><strong>&nbsp;</strong></p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">Future TBL sustainability state</p> <table style="float: left;" width="486"> <tbody> <tr> <td width="67"> <p>Product model</p> </td> <td width="59"> <p>Daily schedule (set)</p> </td> <td width="56"> <p>Defective product rate (%)</p> </td> <td width="16"> <p>&nbsp;</p> </td> <td width="61"> <p>Number of defective products (set)</p> </td> <td width="83"> <p>Environmental sustainability</p> <p>state</p> </td> <td width="82"> <p>Social sustainability state</p> </td> <td width="62"> <p>Economic sustainability state</p> </td> </tr> <tr> <td width="67"> <p>Refrigerator</p> </td> <td width="59"> <p>480 set</p> </td> <td width="56"> <p>0.2%</p> </td> <td width="16"> <p>&nbsp;</p> </td> <td width="61"> <p>1</p> </td> <td width="83"> <p>Wasted material: 1 set</p> <p>&nbsp;</p> <p>Wasted energy: 15.95 kwh</p> </td> <td width="82"> <p>Waste of manpower: 110 pmin</p> </td> <td width="62"> <p>Wasted costs:</p> <p>217.71 $</p> </td> </tr> </tbody> </table> <p style="text-align: left;"><strong>&nbsp;</strong></p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;"><strong>Discussion and conclusion</strong></p> <p style="text-align: left;">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Implementing the proposed approach aimed at achieving zero-defect products and enhancing TBL sustainability as its ultimate goal has provided valuable insights for practitioners and tangible improvements in the case study of this research. …”
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Complementary Filter Optimal Tuning Methodology for Low-Cost Attitude and Heading Reference Systems with Statistical Analysis of Output Signal by Grzegorz Kopecki, Zbigniew A. Łagodowski

“…A simple method for acquiring calibration data is introduced, and these data are subsequently used in the proposed iterative algorithm for optimal time constant selection. The described method minimizes measurement errors and improves the accuracy of the system, ensuring operational stability. …”
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Chaotic Mountain Gazelle Optimizer Improved by Multiple Oppositional-Based Learning Variants for Theoretical Thermal Design Optimization of Heat Exchangers Using Nanofluids by Oguz Emrah Turgut, Mustafa Asker, Hayrullah Bilgeran Yesiloz, Hadi Genceli, Mohammad AL-Rawi

“…This theoretical research study proposes a novel hybrid algorithm that integrates an improved quasi-dynamical oppositional learning mutation scheme into the Mountain Gazelle Optimization method, augmented with chaotic sequences, for the thermal and economical design of a shell-and-tube heat exchanger operating with nanofluids. …”
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A recommender algorithm based on SVD ++model under trust network by Peiwu CHEN, Fangxing SHU

“…Recommender algorithms are usually modeled based on user behavior data.However, the sparseness of explicit behavior data may cause the cold start problem of recommender algorithms.In order to solve the impact of data sparseness and cold-start problems on the effect of recommender algorithms, implicit trust relationship based on user similarity was introduced based on the existing revealed trust relationship, and a new recommender algorithm was designed through the SVD++ implicit semantic model.In order to improve the effect of the algorithm, the neighborhood model was integrated further, and the algorithm score prediction formula and loss function were derived.In the Epinions open source data set, RMSE and MAE were used as test indicators, and comparative experiments were conducted on the entire user set and the cold start user set.The experimental results show that the recommender algorithm can optimize the cold start problem of the original recommender algorithm to a certain extent, and achieve a better rating prediction accuracy.…”
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Study on the Switching Model Predictive Control Algorithm in Batch Polymerization Process by Jong Nam Kim, Chun Bae Ma, Hyok Jo, Un Chol Han, Hyon-Tae Pak, Son Il Hong, Ri Myong Kim

“…Finally, a switching model predictive control algorithm that determines the optimal manipulated value based on the on-line updated step response model is constructed, and a cascade control system using this algorithm is introduced to the temperature control of batch polyvinyl chloride suspension polymerization process. …”
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Applicability of Different Assimilation Algorithms in Crop Growth Model Simulation of Evapotranspiration by Jingshu Wang, Ping Li, Rutian Bi, Lishuai Xu, Peng He, Yingjie Zhao, Xuran Li

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Comprehensive Utilization and Optimal Allocation of Environmental Protection Building Materials in the Construction of Civil Engineering by Li Ren, Huasha Ru, Ruiya Xiao, Yao Ju

“…The research methodology includes a multi-criteria decision-making approach, integrating material selection criteria such as mechanical properties, sustainability, cost, and environmental impact. An optimization model, using genetic algorithms and life cycle assessment, was employed to achieve the best material distribution for specific project needs. …”
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Metamodel-Based Optimization Method for Traffic Network Signal Design under Stochastic Demand by Wei Huang, Xuanyu Zhang, Haofan Cheng, Jiemin Xie

“…Moreover, incorporating the gradient information of the traffic flow in the optimization search algorithm can further improve the solution performance. …”
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Study on the anti-penetration randomness of metal protective structures based on optimized artificial neural network by Lan Liu, Weidong Chen, Shengzhuo Lu, Yanchun Yu, Mingwu Sun

“…And by adopting the Back Propagation Neural Network optimized by Dynamic Lifecycle Genetic Algorithm (DLGABPNN) as the surrogate model of APRMPS, this paper presents the technical route of DLGABPNN-MCS, the Monte Carlo Simulation with DLGABPNN calculation as repeated sampling tests, to addressing APRMPS. …”
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Multi-objective particle swarm optimization algorithm-based method for optimal placement and sizing of distributed generations and shunt capacitors in a radial distribution network by Pinank Patel, Nagaraj Patil, Ahmed Mohsen, Aditya Kashyap, Nofal Adrees Hasan, Karthikeyan A, Dhirendra Nath Thatoi, Deepak Gupta, Alireza kamranfar

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