23 papers · ranked by Valyu relevance
Zijian Gao, Yuanting Zhong, Zeyuan Ma, Yue-Jiao Gong + 1 more
Dynamic Optimization Problems (DOPs) are challenging to address due to their complex nature, i.e., dynamic environment variation. Evolutionary Computation methods are generally advantaged in solving DOPs since they resemble dynamic biological evolution. However, existing evolutionary dynamic optimization methods rely…
Kai Gong, Tong Lu, Xu Wang, Xinggao Liu
Cytotoxic chemotherapy and immune checkpoint inhibitors (ICIs) have transformed the management of advanced cancers, yet durable responses remain restricted to subsets of patients and strongly depend on the tumor immune microenvironment (TIME). Distinct “hot” and “cold” TIMEs differ in pre-existing effector T-cell…
Jinliang Xu, Liping Ma
This paper studies whether the Metabolic Multi-Agent Optimizer (MMAO) can be credibly derived into a dynamic-optimization method without replacing its core metabolic control loop by external adaptation modules. The proposed MMAO-Dyn maps private energy, communal budget, role drift, success feedback, and lifecycle…
Amin Nassaji, Ilias Mitrai, Prodromos Daoutidis
This paper addresses the solution of nonlinear dynamic optimization problems that compute optimal manipulated input profiles to enforce desired output profiles. Such trajectory optimization problems commonly arise in chemical process applications, for example, batch processes where optimal temperature or feeding…
Shao Chang, Qi Zhao, Nana Pu, Shi Cheng + 2 more
a Australian Artificial Intelligence Institute (AAII), Faculty of Engineering and Information Technology, University of Technology Sydney, 15 Broadway, Ultimo, Sydney, 2007, NSW, Australia b School of Big Data and Artificial Intelligence, Chengdu Technological University, Chengdu, 611730, Sichuan, China c School of…
Li Yan, Bolun Liu, Chao Li, Jing Liang + 4 more
—Dynamic multimodal multiobjective optimization presents the dual challenge of simultaneously tracking multiple equivalent pareto optimal sets and maintaining population diversity in time-varying environments. However, existing dynamic multiobjective evolutionary algorithms often neglect solution modality, whereas…
Mark Korang Yeboah, Nana Yaw Asiedu, Ahmad Addo, Razieh Rafieenia + 1 more
Consolidated bioprocessing (CBP), in which enzyme production, substrate hydrolysis, and fermentation occur in a single bioreactor, offers a promising pathway for lignocellulosic ethanol production. However, CBP operation involves competing objectives, including ethanol titer, volumetric productivity, substrate…
Xianjing Wu, Ruizhe Huang, Chuliang Wei, Xutao Chu + 3 more
The evolution towards 6G is set to transform Vehicle-to-Everything (V2X) networks by introducing advanced technologies such as Six-Dimensional Movable Antenna (6DMA). This technology endows Roadside Units (RSUs) with dynamic beam-steering capabilities, enabling adaptive coverage. However, traditional RSU deployment…
Ke Shang, Zhiyun Xiao, Yuxuan Liu, Jianguo Li + 2 more
Dynamic multi-objective optimization with a changing number of objectives has recently attracted increasing attention due to its relevance to real-world problems whose evaluation criteria may evolve over time. However, existing benchmark test suites for this problem setting suffer from a fundamental limitation: when…
GilHwan Kim, Fabrizio Sergi
Human-in-the-loop optimization (HILO) is an established method for identifying subject-specific optimal controllers for performance augmentation. For HILO algorithms to be useful in rehabilitation, however, the optimization algorithm may need to account for how the human response changes over time in response to…
C. K. Revathi, H. Santhi, Asaad Ahmed Gad Elrab Ahmed
Coronary Artery Disease (CAD) is a leading cause of mortality worldwide and is primarily associated with atherosclerotic plaque formation, resulting in coronary artery stenosis. The accurate prediction of CAD using deep learning models is often constrained by the limitations of conventional optimization techniques…
Yiming Zhou, Jun Jiang, Qining Shi, Maojie Fu + 4 more
The Job Shop Scheduling Problem (JSP), a classical NP-hard challenge, has given rise to various complex extensions to accommodate modern manufacturing requirements. Among them, the Dynamic Flexible Job Shop Scheduling Problem (DFJSP) remains particularly challenging, due to its stochastic task arrivals, heterogeneous…
Authors not listed
Inverse molecular design aims to generate novel chemical structures that satisfy multiple property constraints, yet reinforcement-learning (RL) fine-tuning can be sensitive to how objectives are converted into a scalar reward. Here, we systematically analyze how scalarization choices and stabilization mechanisms shape…
Ignacio Tapia García, Cristóbal Torrealba, Ricardo Luna, José Ricardo Pérez-Correa + 1 more
Dynamic Flux Balance Analysis (DFBA) enables simulation of microbial culture dynamics under changing environmental conditions, but remains computationally expensive for tasks such as parameter calibration and fermentation optimization when applied using genome-scale metabolic models (GEMs). To address this challenge…
Hui Li, Jianfu Li, Xuewei Xiang, Peng Jiang + 3 more
Aiming at addressing the problem whereby the traditional time-optimal trajectory planning based on the steady-state torque-speed characteristic cannot fully exploit the short-term dynamic output performance of the servo permanent magnet synchronous motor (SPMSM), a time-optimal trajectory planning method for the SPMSM…
Wei Lv, Yueshun He, Yuankun Yang, Xiaohui Ma + 3 more
Although the Dung Beetle Optimizer (DBO) is a promising new metaheuristic for global optimization, it often struggles with premature convergence and lacks the necessary precision when applied to complex optimization challenges. Therefore, we developed the Multi-Strategy Improved Dung Beetle Optimizer (MIDBO), an…
Niklas Neubrand, Timo Rachel, Tim Litwin, Jens Timmer + 2 more
Systems biology strives to unravel the complex dynamics of cellular processes, often with the help of ordinary differential equations (ODEs). However, the sparsity of measured data and the strong non-linearity of common ODEs introduce severe numerical problems in typical modeling tasks. This gave rise to the…
Authors not listed
Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…
Authors not listed
Continuous manufacturing processes offer significant advantages over batch processes, including easier scalability, reduced costs, lower raw material and solvent consumption, and improved energy efficiency. A robust techno-economic assessment is therefore essential to evaluate and facilitate the adoption of such…
Tinghan Ye, Shuaicheng Tong, Changkun Guan, Beste Basciftci + 1 more
Sequential contextual stochastic programs model real-time decision systems in which each time epoch commits to an action under uncertainty whose consequences propagate into future decisions. In many practical contexts, these programs require obtaining solutions rapidly as new information becomes available. These…
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We demonstrate the numerical optimization and design of thermal emission metasurfaces that selectively radiate mid-infrared (mid-IR) circularly-polarized light. The proposed three-dimensional chiral structure, formed by the multilayer stacking of antenna elements, inherently exhibits circular polarization selectivity…
Authors not listed
Molecular dynamics simulations model chemical reactions as continuous changes in molecular structure over time in-stead of static minima and transition states. This perspective argues that time-dependent structural change is a crucial, but often overlooked, mechanistic feature as many reactions simply do not follow a…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…