Search · four archives
Search · four archives
25 papers · ranked by Valyu relevance
Junhao Wei, Wenxuan Zhu, Qingyang Xu, Yanxiao Li + 10 more
Metaheuristic algorithms have been widely applied to complex optimization problems due to their independence from gradient information, strong global search capability, and robust performance. The Sparrow Search Algorithm (SSA), characterized by its simple structure and ease of implementation, nevertheless suffers from…
Junhao Wei, Yanzhao Gu, Baili Lu, Ngai Cheong + 1 more
Whale Optimization Algorithm (WOA) is a biologically inspired metaheuristic algorithm with a simple structure and ease of implementation. However, WOA suffers from issues such as slow convergence speed, low convergence accuracy, reduced population diversity in the later stages of iteration, and an imbalance between…
Junhao Wei, Yanzhao Gu, Yuzheng Yan, Zikun Li + 4 more
'Shirou Pan' 'Ngai Cheong' 'Dionisis Kandris'] The Whale Optimization Algorithm (WOA) is a bio-inspired metaheuristic algorithm known for its simple structure and ease of implementation. However, WOA suffers from issues such as premature convergence, low population diversity in the later stages of iteration, slow…
Florian Felten, Gabriel Apaza, Gerhard Bräunlich, Cashen Diniz + 8 more
Engineering design optimization seeks to automatically determine the shapes, topologies, or parameters of components that optimize performance under given conditions. This process often depends on physics-based simulations, which are difficult to install, computationally expensive, and require domain-specific…
Rosen Ting-Ying Yu, Cyril Picard, Faez Ahmed
Bayesian Optimization (BO) is a foundational strategy in engineering design optimization for efficiently handling black-box functions with many constraints and expensive evaluations. This paper introduces a novel constraint-handling framework for Bayesian Optimization (BO) using Prior-data Fitted Networks (PFNs), a…
Neil Vaz, Kisung Lim, Jaeyoo Choi, Hyunchul Ju + 1 more
Polymer Electrolyte Membrane Fuel Cells (PEMFCs) have emerged as a pivotal technology in the automotive industry, significantly contributing to the reduction of greenhouse gas emissions. However, the high material costs of the gas diffusion layer (GDL) and bipolar plate (BP) create a barrier for large scale commercial…
Bach Do, Ruda Zhang
Resided at the intersection of multi-fidelity optimization (MFO) and Bayesian optimization (BO), MF BO has found a niche in solving expensive engineering design optimization problems, thanks to its advantages in incorporating physical and mathematical understandings of the problems, saving resources, addressing…
Junjie Tang, Lianguo Wang
The whale optimization algorithm has received much attention since its introduction due to its outstanding performance. However, like other algorithms, the whale optimization algorithm still suffers from some classical problems. To address the issues of slow convergence, low optimization precision, and susceptibility…
Rosen, Yu, Cyril Picard, Faez Ahmed
for Constrained Engineering Problems Authors: ['Rosen' 'Yu' 'Cyril Picard' 'Faez Ahmed'] Bayesian Optimization (BO) is a foundational strategy in the field of engineering design optimization for efficiently handling black-box functions with many constraints and expensive evaluations. This paper introduces a fast and…
Pawan Mishra, Musrrat Ali, Pooja, Safiqul Islam + 1 more
Differential evolution (DE) stands out as a prominent algorithm for addressing global optimization challenges. The efficacy of DE hinges crucially upon its mutation operation, which serves as a pivotal mechanism in generating diverse and high-quality solutions. This article explores various mutation operations aimed at…
Xiaoqiu Ma
The traditional methods deal with large sample data sets of architectural engineering drawings and they have high time complexity and space complexity as well. Their searching time is long and sometimes the results are unsatisfactory. Therefore, this paper proposes an optimization method designed for architectural…
Hansani Weeratunge, Dominic Robe, Elnaz Hajizadeh
We developed an interpretability informed Bayesian optimization framework to optimize underwater acoustic coatings based on polyurethane elastomers with embedded metamaterial features. A data driven model was employed to analyze the relationship between acoustic performance, specifically sound absorption and the…
Authors not listed
This article presents an overview about the state of the art in the development of structured packings for distillation applications. The focus is on highlighting different approaches including heuristic development cycles, the development of new packing structures, 3D-printing as tool for manufacturing, and…
Jasper H. Bussemaker, Paul Saves, Nathalie Bartoli, Thierry Lefèbvre + 1 more
'Rémi Lafage'] Choosing the right system architecture for the problem at hand is challenging due to the large design space and high uncertainty in the early stage of the design process. Formulating the architecting process as an optimization problem may mitigate some of these challenges. This work investigates…
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…
Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras + 3 more
By combining machine learning with design of experiments, so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible, and are better at investigating the processes spanning all length scales of chemical engineering. While the active machine…
Authors not listed
Designing efficient photoreactors remains challenging due to the complex interplay of light transport phenomena, shaped by reflection, scattering and absorption processes. Here, we introduce a workflow that integrates ray-tracing digital twins with multi-objective Bayesian optimization to autonomously design…
Jason Yang, Ravi G. Lal, James C. Bowden, Raul Astudillo + 5 more
Directed evolution (DE) is a powerful tool to optimize protein fitness for a specific application. However, DE can be inefficient when mutations exhibit non-additive, or epistatic, behavior. Here, we present Active Learning-assisted Directed Evolution (ALDE), an iterative machine learning-assisted DE workflow that…
Charlotte Merzbacher, Oisin Mac Aodha, Diego A. Oyarzún
Recent advances in synthetic biology have enabled the construction of molecular circuits that operate across multiple scales of cellular organization, such as gene regulation, signalling pathways and cellular metabolism. Computational optimization can effectively aid the design process, but current methods are…
R Bardini, S Di Carlo
Biofabrication is the generation of biologically functional products from living cells and biomaterials through bioprinting and subsequent maturation processes. Technological and scientific domains are underlying biofabrication ranging from biology to automated manufacturing and culture systems. Among its application…
M. Zaki Jawaid, Robin W. Yeo, Aayushma Gautam, T. Blair Gainous + 2 more
Designing novel functional proteins remains a slow and expensive process due to a variety of protein engineering challenges; in particular, the number of protein variants that can be experimentally tested in a given assay pales in comparison to the vastness of the overall sequence space, resulting in low hit rates and…
Andre KY Low, Flore Mekki-Berrada, Aleksandr Ostudin, Jiaxun Xie + 7 more
The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. When solving optimization problems, Bayesian-based optimizers are often…
Authors not listed
Experimental design plays an important role in efficiently acquiring informative data for system characterization and deriving robust conclusions under resource limitations. Recent advancements in high-throughput experimentation coupled with machine learning have notably improved experimental procedures. While Bayesian…
Riley Hickman, Matteo Aldeghi, Alán Aspuru-Guzik
Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility. The combination of such strategies with automated laboratory equipment and/or high-performance computing in a…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…