Three-Dimensional Path Planning for Unmanned Aerial Vehicles Based on Hybrid Multi-Strategy Dung Beetle Optimization Algorithm by Hongmei Fei, Ruru Liu, Leilei Dong, Zhaohui Du, Xuening Liu, Tao Luo, Jie Zhou

“…This paper proposes a novel UAV path planning method based on the Hybrid Multi-Strategy Dung Beetle Optimization Algorithm (HMSDBO), which effectively reduces path length and improves path smoothness. …”
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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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Multi-Objective Optimization of Insulation Thickness with Respect to On-Site RES Generation in Residential Buildings by Agis M. Papadopoulos, Konstantinos Polychronakis, Elli Kyriaki, Effrosyni Giama

“…This paper investigates the optimization of insulation thickness with respect to the integration of renewable energy systems in residential buildings in order to improve energy efficiency, maximize the contribution of renewables and reduce life cycle costs. …”
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A Day-Ahead Optimal Battery Scheduling Considering the Grid Stability of Distribution Feeders by Umme Mumtahina, Sanath Alahakoon, Peter Wolfs

“…This study presents a comprehensive framework for optimizing energy management systems by integrating advanced methodologies for weather forecasting, energy cost analysis, and grid stability using a mixed-integer linear programming (MILP) algorithm. …”
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The Elitist Non-Dominated Sorting Crisscross Algorithm (Elitist NSCA): Crisscross-Based Multi-Objective Neural Architecture Search by Zhihui Chen, Ting Lan, Dan He, Zhanchuan Cai

“…This paper proposes a multi-objective evolutionary algorithm called the elitist non-dominated sorting crisscross algorithm (elitist NSCA) and applies it to neural architecture search, which considers two optimization objectives: the accuracy and network parameters. …”
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Application of supervised machine learning and unsupervised data compression models for pore pressure prediction employing drilling, petrophysical, and well log data by Abu Bakker Siddique, Tanveer Alam Munshi, Nazmul Islam Rakin, Mahamudul Hashan, Sushmita Sarker Chnapa, Labiba Nusrat Jahan

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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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Integrated Optimization on Train Control and Timetable to Minimize Net Energy Consumption of Metro Lines by Yuhe Zhou, Yun Bai, Jiajie Li, Baohua Mao, Tang Li

“…Energy-efficient metro operation has received increasing attention because of the energy cost and environmental concerns. This paper developed an integrated optimization model on train control and timetable to minimize the net energy consumption. …”
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Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration by Ala'a R. Al-Shamasneh, Faten Khalid Karim, Yu Wang

“…This algorithm adeptly balances resource expenditures with model quality, employing Lyapunov-driven optimization theory to convert long-term stochastic challenges into short-term deterministic resolutions. …”
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Gaussian Process Regression Total Nitrogen Prediction Based on Data Decomposition Technology and Several Intelligent Algorithms by WANG Yongshun, CUI Dongwen

“…Total nitrogen (TN) is one of the important indicators to reflect the degree of water pollution and measure the eutrophication status of lakes and reservoirs.To improve the accuracy of TN prediction,based on the empirical wavelet transform (EWT) and wavelet packet transform (WPT) decomposition technology,this paper proposes a Gaussian process regression (GPR) prediction model optimized by osprey optimization algorithm (OOA),rime optimization algorithm (ROA),bald eagle search (BES) and black widow optimization algorithm (BWOA) respectively.Firstly,the TN time series is decomposed into several more regular subsequence components by EWT and WPT respectively.Then,the paper briefly introduces the principles of OOA,ROA,BES,and BWOA algorithms and applies OOA,ROA,BES,and BWOA to optimize GPR hyperparameters.Finally,EWT-OOA-GPR,EWT-ROA-GPR,EWT-BES-GPR,EWT-BWOA-GPR,WPT-OOA-GPR,WPT-ROA-GPR,WPT-BES-GPR,WPT-BWOA-GPR models (EWT-OOA-GPR and other eight models for short) are established to predict the components of TN by the optimized super-parameters.The final prediction results are obtained after reconstruction,and WT-OOA-GPR,WT-ROA-GPR,WT-BES-GPR and WT-BWOA-GPR models based on wavelet transform (WT) are built.Eight models,including EWT-OOA-SVM based on support vector machine (SVM),the paper compares the unoptimized EWT-GPR,WPT-GPR models,and the uncomposed OOA-GPR,ROA-GPR,BES-GPR,and BWOA-GPR models.The models were verified by the monitoring TN concentration time series data of Mudihe Reservoir,an important drinking water source in China,from 2008 to 2022.The results are as follows.① The average absolute percentage error of eight models such as EWT-OOA-GPR for TN prediction is between 0.161% and 0.219%,and the coefficient of determination is 0.999 9,which is superior to other comparison models,with higher prediction accuracy and better generalization ability.② EWT takes into account the advantages of WT and EMD.WPT can decompose low-frequency and high-frequency signals at the same time.Both of them can decompose TN time series data into more regular modal components,significantly improving the accuracy of model prediction,and the decomposition effect is better than that of the WT method.③ OOA,ROA,BES,and BWOA can effectively optimize GPR hyperparameters and improve GPR prediction performance.…”
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RFID‐Based Enhanced Resource Optimization for 5G/6G Network Applications by Stella N. Arinze, Augustine O. Nwajana

“…RFID readers strategically placed across the network continuously captured this data, which was processed by a centralized controller using a custom‐designed optimization algorithm. This algorithm dynamically managed key network resources, including spectrum allocation, load balancing, and energy consumption, ensuring efficient operation under varying network conditions. …”
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A rapid detection method for egg quality using CARS and SSA⁃XGBoost improved by combining hyperspectral analysis by WANG Linyi, ZOU Qianying, SUN Qiang

“…Optimizing multiple hyperparameters of the XGBoost model through the Tartary Sea Salp Swarm Algorithm to improve the predictive performance of the XGBoost model. …”
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Optimizing TCP Performance in Multi-AP Residential Broadband Connections via Minislot Access by Domenico Giustiniano, Eduard Goma, Alberto Lopez Toledo, George Athanasiou

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Optimization method of investment package based on Markowitz portfolio theory by A. D. Baydalin

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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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Reverse Logistics Network Optimization for Retired BIPV Panels in Smart City Energy Systems by Cimeng Zhou, Shilong Li

“…Meanwhile, the multi-level recycling network which covers “building points-regional transfer stations-specialized distribution centers” is designed in the research, the Pareto solution set is solved by the improved NSGA-II algorithm, a “1 + 1” du-al-core construction model of distribution center and transfer station is developed, so as to minimize the total cost and life cycle carbon footprint of the logistics network. …”
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Optimization of Graphene Oxide’s Characteristics with TOPSIS Using an Automated Decision-Making Process by Javanbakht T.

“…Moreover, their advantages and inconveniences could be investigated better once this investigation provides information on optimizing its candidates. In the current research work, a novel automated decision-making process was used with the TOPSIS algorithm using the Łukasiewicz disjunction, which helped detect the confusion of properties and determine its impact on the rank of candidates. …”
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Optimizing urban energy flows: Integrative vehicle-to-building strategies and renewable energy management by Jian Hern Yeoh, Kai-Yun Lo, I-Yun Lisa Hsieh

“…This study introduces a scalable, multi-objective optimization model that utilizes Vehicle-to-Building (V2B) interactions to mitigate grid load by leveraging EVs as mobile energy storage units. …”
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Ensemble Learning-Based Wine Quality Prediction Using Optimized Feature Selection and XGBoost by Sonam Tyagi, Ishwari Singh Rajput, Bhawnesh Kumar, Harendra Singh Negi

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