Control Allocation Strategy Based on Min–Max Optimization and Simple Neural Network by Kaixin Li, Mei Liu, Xinliang Li, Xiaobin Yu, Kun Liu

“…The presented method integrates min–max optimization with the force decomposition (FD) algorithm, effectively handling actuator saturation while maintaining low computational complexity. …”
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Determining Point of Economic Cattle Milk Production through Machine Learning and Evolutionary Algorithm for Enhancing Food Security by Sonam Bhardwaj, Ayon Tarafdar, Manoj Baghel, Triveni Dutt, Gyanendra Kumar Gaur

“…Artificial neural networks (ANNs) in conjugation with genetic algorithms (GAs) have been demonstrated to be an effective tool for system modelling and optimization in a variety of applications. …”
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Deep Reinforcement Learning-Based Motion Control Optimization for Defect Detection System by Yuhuan Cai, Liye Zhao, Xingyu Chen, Zhenjun Li

“…A composite reward mechanism is introduced to mitigate potential motor instability, while CP-MPA is utilized to optimize the performance of the proposed m-TD3 composite controller. …”
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Optimizing Automated Negotiation: Integrating Opponent Modeling with Reinforcement Learning for Strategy Enhancement by Ya Zhang, Jinghua Wu, Ruiyang Cao

“…However, most existing research primarily focuses on modeling the formal negotiation phase, while neglecting the critical role of opponent analysis during the pre-negotiation stage. …”
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Multi energy dynamic soaring trajectory optimization method based on reinforcement learning by ZHANG Yunfei, WANG Honglun, ZHANG Menghua, GONG Yinan

“…On the foundation of the trained policies, the method utilizes the twin delayed deep deterministic policy gradient algorithm for policy enhancement. This significantly boosts the real-time inference capabilities while addressing the challenges traditional optimal control algorithms face in dynamic soaring due to varying wind fields. …”
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Two-Stage Integrated Optimization Design of Reversible Traction Power Supply System by Xiaodong Zhang, Wei Liu, Qian Xu, Zhuoxin Yang, Dingxin Xia, Haonan Liu

“…The parallel cheetah algorithm is employed to solve this complex optimization problem. …”
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Gene selection based on adaptive neighborhood-preserving multi-objective particle swarm optimization by Sumet Mehta, Fei Han, Muhammad Sohail, Bhekisipho Twala, Asad Ullah, Fasee Ullah, Arfat Ahmad Khan, Qinghua Ling

“…The analysis of high-dimensional microarray gene expression data presents critical challenges, including excessive dimensionality, increased computational burden, and sensitivity to random initialization. Traditional optimization algorithms often produce inconsistent and suboptimal results, while failing to preserve local data structures limiting both predictive accuracy and biological interpretability. …”
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Design Method for Low-Ice-Class Propellers Based on Multi-Objective Optimization by Chenxu Gu, Kang Han, Kaiqiang Weng, Chao Wang, Chunhui Wang

“…The objective of this paper was to establish a comprehensive methodology for the optimized design of propellers for ice-class vessels, aiming to enhance hydrodynamic efficiency while ensuring structural integrity. …”
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Hybrid optimization driven fake news detection using reinforced transformer models by Ganesh Karthik M, Khadri Syed Faizz Ahmad, Sai Geetha Pamidimukkala, Asha Prashant Sathe, Sirisha G.N.V.G, Sitha Ram M, Koteswararao Ch

“…The model is further optimized using PSODO, a hybrid Particle Swarm Optimization and Dandelion Optimization algorithm, addressing limitations such as slow convergence and local optima entrapment. …”
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An optimized implementation of adaptive noise canceller based on proposed shift and add multiplier by Aneela Pathan, Khalil M. Zohaib, Rizwan Aziz, Adil Hussain Chandio, Syed Haseeb Shah

“…Reducing resources is necessary to optimize the implementation. The literature on resource-optimized filter implementation with multiplier optimization has been seen with a number of approaches. …”
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POTMEC: A Novel Power Optimization Technique for Mobile Edge Computing Networks by Tamilarasan Ananth Kumar, Rajendirane Rajmohan, Sunday Adeola Ajagbe, Oluwatobi Akinlade, Matthew Olusegun Adigun

“…This paper introduces POTMEC, a power optimization framework that combines a channel-aware adaptive power allocator using real-time SNR measurements, a MATLAB-trained RL model for joint offloading decisions and a decaying step-size algorithm guaranteeing convergence. …”
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APG mergence and topological potential optimization based heuristic user association strategy by Zhirui HU, Meihua BI, Fangmin XU, Meilin HE, Changliang ZHENG

“…Therefore, it is reasonable to model the problem of improving network scalable degree as minimizing network coupling degree,and it is feasible to improve network scalable degree by reducing network coupling degree.2)The upper limit of computational complexity of the proposed algorithm is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"> <mi mathvariant="script">O</mi><mo stretchy="false">(</mo><mi>K</mi><mi>N</mi><msub> <mi>log</mi> <mn>2</mn> </msub> <mi>N</mi><mo>+</mo><msup> <mi>k</mi> <mn>2</mn> </msup> <mo>+</mo><mi>N</mi><mi>N</mi><msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>p</mtext> </msub> <mo stretchy="false">)</mo></math></inline-formula>,while that of directly solving the optimization problem is<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="script">O</mi><mo stretchy="false">(</mo><msup> <mi>N</mi> <mrow> <msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>u</mtext> </msub> <mi>K</mi></mrow> </msup> <mo stretchy="false">)</mo></math></inline-formula>.3)For theoretical analysis of the network scalable degree,take Fig.3 as an example.If AP2 changes,12 APs in Fig. 3(a)are affected and the network scalable degree is η₂=0.51,while 4 APs in Fig.3(c)are affected and the network scalable degree is η₂=0.79.4)Fig.5 shows the simulation results of network scalable degree.Compared with the traditional strategy,the network scalable degree is improved by 9.59% with 4.43% user rate loss.Compared with the strategy in[10],the network scalable degree is improved by 22.15% with 4.99% user rate loss. 5) The algorithm parameters, the threshold β₀of overlap rate and the upper limit number N₀of AP associated, effect the performance.As shown in Fig.6,with β₀or N₀decreases,η increases and the total user rate decreases. …”
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An optimized multi-task contrastive learning framework for HIFU lesion detection and segmentation by Matineh Zavar, Hamid Reza Ghaffari, Hamid Tabatabaee

“…By employing a genetic algorithm, OMCLF explores and optimizes augmentation techniques suitable for medical data, avoiding distortions that could compromise diagnostic accuracy. …”
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Regularized K-Means Clustering via Fully Corrective Frank-Wolfe Optimization by Ahmed Yacoub Yousif, Basad Al-Sarray

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Detection and Optimization of Photovoltaic Arrays’ Tilt Angles Using Remote Sensing Data by Niko Lukač, Sebastijan Seme, Klemen Sredenšek, Gorazd Štumberger, Domen Mongus, Borut Žalik, Marko Bizjak

“…The modules are grouped into arrays, and tilt angles are optimized using a Simulated Annealing (SA) algorithm, which maximizes simulated solar irradiance while accounting for shadowing, direct, and anisotropic diffuse irradiances. …”
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Optimizing Concrete Mix Design for Cost and Carbon Reduction Using Machine Learning by Angga T. Yudhistira, Arief S. B. Nugroho, Iman Satyarno, Tantri N. Handayani, Malindu Sandanayake, Rimba Erlangga, Jonathan Lianto, Alfa Rosyid Ernanto

“…XGBoost Machine Learning Algorithm is used to make predictions, and PSO is used to obtain the optimal mixture. …”
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Integrated Approach to Optimizing Selection and Placement of Water Pipeline Condition Monitoring Technologies by Diego Calderon, Mohammad Najafi

“…This article introduces a unified framework and methods for optimally selecting condition monitoring technologies while locating their deployment at the most vulnerable pipe segments. …”
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Multi-Objective Machine Learning Optimization of Cylindrical TPMS Lattices for Bone Implants by Mansoureh Rezapourian, Ali Cheloee Darabi, Mohammadreza Khoshbin, Irina Hussainova

“…To address anatomical variability, a novel implant size-based categorization (small, medium, and large) was introduced, and separate optimization runs were conducted for each group. The optimization was performed via the NSGA-II algorithm to maximize mechanical performance (U and EA) and surface efficiency (SA/VR), while filtering for biologically relevant RD values (20–40%). …”
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A Study of Multi-distributed Resource Equalization Allocation for Virtual Power Plants Based on Genetic-heuristic Algorithm by Haifeng Li, Tao Jin, Xian Xu, Lin Shi

“…Combining the global search capability of a genetic algorithm and the local optimization capability of an ant colony algorithm, the genetic algorithm stage adopts real-number encoding and a dynamic crossover-mutation strategy, while the ant colony algorithm stage optimizes the pheromone update mechanism to avoid premature convergence. …”
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Energy Scheduling of Hydrogen Hybrid UAV Based on Model Predictive Control and Deep Deterministic Policy Gradient Algorithm by Haitao Li, Chenyu Wang, Shufu Yuan, Hui Zhu, Bo Li, Yuexin Liu, Li Sun

“…The proposed method is designed to optimize energy management in hydrogen-powered UAVs across diverse flight missions. …”
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