Modern possibilities for optimizing the calculation of intraocular lens optical power using deep machine learning capabilities by A.R. Vinogradov, B.G. Dzhashi, S.V. Balalin

“…The development of a software application based on artificial intelligence is a rather labor-intensive process that requires processing a large amount of data, using formulas and algorithms with obtaining the final result, as well as subsequent training, optimization, and improving the quality of its work. …”
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Multi-objective design optimization of a transonic axial fan stage using sparse active subspaces by Richard Amankwa Adjei, Chengwei Fan

“…Coupled with freeform method and multi-objective genetic algorithm, an automated optimization loop was run at a single operating condition. …”
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Experimental and computational optimization of sheet metal forming parameters for cylindrical cups of Al1100 and SS202 by Amit Kaimkuriya, S. Balaguru

“…Integrating WOA with FEA yielded valuable insights into defect mitigation, particularly in reducing wrinkling and fractures, thereby improving product quality. This study demonstrates the effectiveness of combining advanced optimization algorithms with simulation tools, promoting sustainable manufacturing by enhancing efficiency and material utilization in deep drawing processes.…”
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Deep Reinforcement Learning-Based Two-Phase Hybrid Optimization for Scheduling Agile Earth Observation Satellites by Guanghui Zhou, Zhicheng Jin, Dongning Liu

“…The experimental results demonstrate that the TPHO framework with MRC rules achieves superior performance, yielding a total reward improvement exceeding 16% compared with the A-ALNS algorithm in the most complex scenario involving 1200 tasks, yet requiring less than 3% of the computational duration of A-ALNS.…”
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RM-MOCO: A Fast-Solving Model for Neural Multi-Objective Combinatorial Optimization Based on Retention by Huiqing Wei, Fei Han, Qing Liu, Henry Han

“…Recently, learning-based methods have achieved good results in solving MOCO problems. However, most of these methods use attention mechanisms and their variants, which have room for further improvement in the speed of solving MOCO problems. …”
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Prediction and optimization of hardness in AlSi10Mg alloy produced by laser powder bed fusion using statistical and machine learning approaches by İnayet Burcu Toprak

“…This study highlights the importance of integrating Machine Learning and statistical analysis methods for the effective modeling and optimization of LPBF processes. The findings contribute significantly to the literature and serve as a valuable reference for future research aimed at improving LPBF process efficiency and performance.…”
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Design and Multi-Objective Optimization of Auxetic Sandwich Panels for Blastworthy Structures Using Machine Learning Method by Andika, Sigit Puji Santosa, Djarot Widagdo, Arief Nur Pratomo

“…The optimization results show significant improvements in blastworthiness performance, with notable reductions in permanent displacement and enhancements in specific energy absorption (SEA). …”
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Thermo-hydrodynamic and exergy optimization of a photovoltaic thermal (PV/T) air collector using NSGA-II by Seyed Morteza Javadpour, Rasoul Soufi Noughabi, Ebrahim Kadivar

“…The Non-Dominated Sorting Genetic Algorithm (NSGA-II) optimization method, combined with the finite volume method, is employed to identify and analyze the optimal configuration in comparison to the initial design. …”
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A Practical Method for Red-Edge Band Reconstruction for Landsat Image by Synergizing Sentinel-2 Data with Machine Learning Regression Algorithms by Yuan Zhang, Zhekui Fan, Wenjia Yan, Chentian Ge, Huasheng Sun

“…Red-edge bands are the most essential spectral data for multispectral remote sensing images, with them playing a critical role in monitoring vegetation growth status at regional and global scales. …”
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A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization by Mostafa Akbari, Ezatollah Hassanzadeh, Yaghuob Dadgar Asl, Amirhossein Moghanian

“…Lastly, the third section pertains to the optimization of FSW parameters, illustrating how AI-driven algorithms analyze complex interactions among multiple variables to determine the most effective process settings. …”
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Balancing conflicting objectives in pre-salt reservoir development: A robust multi-objective optimization framework by Auref Rostamian, Amir Davari Malekabadi, Marx Vladimir De Souda Miranda, Vinicius Edurado Botechia, Denis José Schiozer

“…The study focuses on maximizing expected monetary value (EMV) and the net present value of RM4 considering economic uncertainty (NPVeco of RM4), of the most pessimistic scenario among the RMs. The optimization variables are location, type (injection or production), and number of wells, while the non-dominated sorting genetic algorithm II (NSGA-II) is employed for multi-objective optimization. …”
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Modern approaches to the diagnosis and treatment of cardiac sarcoidosis: results of a cohort study by S. V. Mairina, D. V. Ryzhkova, L. B. Mitrofanova, A. V. Ryzhkov, P. M. Murtazalieva, O. M. Moiseeva

“…All patients underwent 18F-fluorodeoxyglucose positron emission tomography (PET).Results. The most common (53%) electrocardiographic abnormality was right bundle branch block. …”
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HiGMA-DADCN: Hirudinaria granulosa multitropic algorithm optimised double attention enabled deep convolutional neural network for psoriasis classification by Soumya C S, Jayanna H S

“…The HiGMA algorithm plays a crucial role in identifying and extracting the most relevant regions of affected skin through optimal segmentation. …”
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An efficient enhanced stacked auto encoder assisted optimized deep neural network for forecasting Dry Eye Disease by Steffi Rajan, Suresh Ponnan

“…The approach described here is novel because it merges chaotic maps into FS, employs SLSTM-STSA for improved classification accuracy (CA), and optimizes with the adaptive quantum rotation of the Enhanced Quantum Bacterial Foraging Optimisation Algorithm (EQBFOA). …”
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A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning by Ruoyu Sun, Yue Xi, Angelos Stefanidis, Zhengyong Jiang, Jionglong Su

“…Among these DRL algorithms, the combination of actor-critic algorithms and deep function approximators is the most widely used DRL algorithm. …”
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Optimal Trajectory Determination and Mission Design for Asteroid/Deep-Space Exploration via Multibody Gravity Assist Maneuvers by Sean Fritz, Kamran Turkoglu

“…This paper discusses the creation of a genetic algorithm to locate and optimize interplanetary trajectories using gravity assist maneuvers to improve fuel efficiency of the mission. …”
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Power Enhancement under Partial Shading Condition Using a Two-Step Optimal PV Array Reconfiguration by Mohamad Hossien Nahidan, Mehdi Niroomand, Behzad Mirzaeian Dehkordi

“…The introduced algorithm searches for all possible connections and finally identifies the most optimal solution. …”
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Traction Drive Control System for Railway Electric Rolling Stock Based on the Application of Power Factor as an Optimization Criterion by Goolak S., Gorobchenko O., Holub H., Kulbovskiy I., Petrychenko O.

“…The stated objective has been achieved through the solution of the following tasks: development of an algorithm for applying traction drive power factor as an optimization criterion, taking into account stochastic disturbance effects acting on the traction drive from the traction power supply system and mechanical load; development of a structural scheme for an optimized automatic control system of electric rolling stock traction drives, in which the proposed algorithm is implemented. …”
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Synergistic Framework for Fuel Cell Mass Transport Optimization: Coupling Reduced-Order Models with Machine Learning Surrogates by Shixin Li, Qingshan Liu, Yisong Chen

“…The combination of the one-dimensional model, the surrogate model, and the genetic algorithm can effectively improve the optimization efficiency.…”
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An Efficient Mutual Authentication and Fractional Lyrebird Optimization With Deep Learning–Based SIP-Based DRDoS Attack Detection by V. Sreenivasulu, C. V. Ravikumar

“…The detection performance of DSA is increased by training using the fractional lyrebird optimization algorithm (FLOA); FLOA provides a more effective, reliable, and scalable optimization strategy for training DSAs than traditional algorithms. …”
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