Optimizing high voltage gain interleaved boost converters for PV and wind systems using hybrid deep learning with bitterling fish and secretary bird algorithms by G.Veera Sankara Reddy, S. Vijayaraj

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Classification of biomedical lung cancer images using optimized binary bat technique by constructing oblique decision trees by Shobha Aswal, Neelu Jyothi Ahuja, Ritika Mehra

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Rendering algorithm for 3D model of goods in power warehouse based on linear interpolation and 2D texture mapping by Gui Luo, Xin Chen, Quan Liu, Anlin Liu, Xuan Zhang, Li Tian, Yueshi Gao

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Hybrid Disassembly Line Balancing of Multi-Factory Remanufacturing Process Considering Workers with Government Benefits by Xiaoyu Niu, Xiwang Guo, Peisheng Liu, Jiacun Wang, Shujin Qin, Liang Qi, Bin Hu, Yingjun Ji

“…Furthermore, a discrete zebra optimization algorithm is proposed to solve the model, integrating a survival-of-the-fittest strategy to improve its optimization capabilities. …”
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Machine learning algorithms for diabetic kidney disease risk predictive model of Chinese patients with type 2 diabetes mellitus by Lu-Xi Zou, Xue Wang, Zhi-Li Hou, Ling Sun, Jiang-Tao Lu

“…Among the seven forecasting models constructed by MLAs, the accuracy of the Light Gradient Boosting Machine (LightGBM) model was the highest, indicated that the LightGBM algorithms might perform the best for predicting 3-year risk of DKD onset.Conclusions Our study could provide powerful tools for early DKD risk prediction, which might help optimize intervention strategies and improve the renal prognosis in T2DM patients.…”
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PM2.5 Concentration Prediction Based on Markov Blanke Feature Selection and Hybrid Kernel Support Vector Regression Optimized by Particle Swarm Optimization by Lian-Hua Zhang, Ze-Hong Deng, Wen-Bo Wang

“…Abstract This study employed air quality and meteorological data as research materials and extracted the optimal feature subset by using the approximate Markov blanket-based normal maximum relevance minimum redundancy (nMRMR) algorithm to serve as the input data of the prediction model. …”
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The Robust Steiner Team Orienteering Problem with Decreasing Priorities under budgeted uncertainty by Lucas Assunção, Andréa Cynthia Santos

“…The cut-and-branch algorithm based on the stronger model also stands out, solving 20 more instances than the other. …”
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Improvement of quality of 3D printed objects by elimination of microscopic structural defects in fused deposition modeling. by Evgeniy G Gordeev, Alexey S Galushko, Valentine P Ananikov

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Optimal Allocation Strategy for Power Quality Control Devices Based on Harmonic and Three-Phase Unbalance Comprehensive Evaluation Indices for Distribution Network by Fang ZHUO, Zebin YANG, Hao YI, Guangyu YANG, Meng WANG, Xiaoqing YIN, Chengzhi ZHU

“…Secondly, taking the global configuration effects, the total number and capacity of control devices as the optimization goals, and regarding the harmonic distortions and unbalance degrees of the nodes satisfying the standard as the constraint condition, the optimal configuration node and capacity of each device is determined by multi-objective particle swarm algorithm. …”
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Opposition-based learning techniques in metaheuristics: classification, comparison, and convergence analysis by Rihab Lakbichi, Farouq Zitouni, Saad Harous, Aridj Ferhat, Abdelhadi Limane, Abdulaziz S. Almazyad, Guojiang Xiong, Ali Wagdy Mohamed

“…In recent years, opposition-based learning (OBL) has emerged as a powerful enhancement strategy in metaheuristic algorithms (MAs), gaining significant attention for its potential to accelerate convergence and improve solution quality. …”
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A multi-task genetic programming approach for online multi-objective container placement in heterogeneous cluster by Ruochen Liu, Haoyuan Lv, Ping Yang, Rongfang Wang

“…MOCP-MTGP can automatically generate multiple groups of allocation rules from historical workload patterns and different cluster states, and capture the similarities between all online tasks to guide the transfer of general knowledge during optimization. Comprehensive experiments show that the proposed algorithm can improve the resource utilization of clusters, reduce the number of physical machines, and effectively meet resource constraints and high availability requirements.…”
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Lightweight Tea Shoot Picking Point Recognition Model Based on Improved DeepLabV3+ by HU Chengxi, TAN Lixin, WANG Wenyin, SONG Min

“…[Conclusions]This study effectively implements an efficient and accurate tea shoot recognition method through targeted model improvements and optimizations, furnishing crucial technical support for the practical application of intelligent tea picking robots. …”
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A Discrete Brain Storm Optimization Algorithm for Hybrid Flowshop Scheduling Problems with Batch Production at Last Stage in the Steelmaking-Refining-Continuous Casting Process by Kunkun Peng, Chunjiang Zhang, Weiming Shen, Xinfu Pang, Yanlan Mei, Xudong Deng

“…In this paper, a Discrete Brain Storm Optimization (DBSO) algorithm is proposed to handle SRCC scheduling problems. …”
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Application of genetic algorithm for the set-covering problem solution by Igor S. Konovalov, Vladimir A. Fatkhi, Valery G. Kobak

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Effective Facial Expression Recognition System Using Artificial Intelligence Technique by Imad S. Yousif, Tarik A. Rashid, Ahmed S. Shamsaldin, Sabat A. Abdulhameed, Abdulhady Abas Abdullah

“…This paper presents an improved performance of the Facial Expression Recognition (FER) systems via augmentation in Artificial Neural Networks and Genetic Algorithms, two renowned artificial intelligence techniques possessing disparate strengths. …”
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Optimization of extraction in supercritical fluids in obtaining Pouteria lucuma seed oil by response surface methodology and artificial neuronal network coupled with a genetic algo... by Alex Chauca-Cerrutti, Marianela Inga, José Luis Pasquel-Reátegui, Indira Betalleluz-Pallardel, Gustavo Puma-Isuiza

“…LS was previously characterized, and the extraction parameters were optimized using a Box-Behnken design, considering temperature (40–60°C), pressure (100–300 bar), and CO2 flow rate (3–7 mL/min), applying the response surface methodology (RSM) and neural networks with genetic algorithm (ANN+GA). …”
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Hierarchical Disturbance Propagation Mechanism and Improved Contract Net Protocol for Satellite TT&C Resource Dynamic Scheduling by Zhiqing Xiang, Yi Gu, Xinwei Wang, Guohua Wu

“…Finally, a large number of simulation experiments were carried out and compared with various comparative algorithms. The results show that the proposed algorithm can effectively improve the solution effect of satellite TT&C resource dynamic scheduling problems, and has good application prospects.…”
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A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization by Jianqiu Chen, Huan Xiong, Shixuan Zhou, Xiang Wang, Benxiao Lou, Longtang Ning, Qingwei Hu, Yang Tang, Guobin Gu

“…Finally, an SVM classification algorithm is employed for personnel detection. To process small sample categories, data enhancement techniques (e.g., random flip and rotation) and K-fold cross-validation are applied to optimize the model parameters. …”
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A CCP-Based Decentralized Optimization Approach for Electricity–Heat Integrated Energy Systems with Buildings by Xiangyu Zhai, Xuexue Qin, Jiahui Zhang, Xiaoyang Liu, Xiang Bai, Song Zhang, Zhenfei Ma, Zening Li

“…To address the disadvantages of high computational complexity and poor information privacy in centralized optimization, an accelerated asynchronous decentralized alternating direction method of multipliers (A-AD-ADMM) algorithm is proposed, which decomposes the original optimization problem into sub-problems of ES and TS for distributed solving, significantly improving solution efficiency. …”
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Minimizing Delay at Closely Spaced Signalized Intersections Through Green Time Ratio Optimization: A Hybrid Approach With K-Means Clustering and Genetic Algorithms by Ruti R. Politi, Serhan Tanyel

“…Closely spaced intersections play a key role in traffic flow management. This study aims to model different traffic related parameters to minimize the delay of a closely spaced intersection by optimizing the green time ratio with the help of the genetic algorithm. …”
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