Search · four archives
Search · four archives
27 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…
Nermina Zaimović-Uzunović, Samir Lemeš, Damir Ćurić, Alan Topčić
This paper deals with combination of two modern engineering methods in order to optimise the shape of a representative casting product. The product being analysed is a sling, which is used to attach pulling rope in timber transportation. The first step was 3D modelling and static stress/strain analysis using CAD/CAE…
Mojtaba Ghasemi, Abolfazl Rahimnejad, Ebrahim Akbari, Ravipudi Venkata Rao + 4 more
'Ravipudi Venkata Rao' 'Pavel Trojovský' 'Eva Trojovská' 'Stephen Andrew Gadsden' 'Yilun Shang'] Many important engineering optimization problems require a strong and simple optimization algorithm to achieve the best solutions. In 2020, Rao introduced three non-parametric algorithms, known as Rao algorithms, which have…
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…
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…
Sergio Garcia, Cong Trinh
A large space of chemicals with broad industrial and consumer applications could be synthesized by engineered microbial biocatalysts. However, the current strain optimization process is prohibitively laborious and costly to produce one target chemical and often requires new engineering efforts to produce new molecules.…
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…
Sergio Garcia, Cong T. Trinh
Living cells optimize their fitness against constantly changing environments to survive. Goal attainment optimization is a mathematical framework to describe the simultaneous optimization of multiple conflicting objectives that must all reach a performance above a threshold or goal. In this study, we applied goal…
Babita K. Verma, Ahmad A. Mannan, Fuzhong Zhang, Diego A. Oyarzún
Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to their ability to self-regulate gene expression in response to bioreactor perturbations. The…
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…
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…
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…
Tobias Seidel, Lena-Marie Ränger, Thomas Grützner, Michael Bortz
In this work we present a new approach that we use to simulate and optimize multiple dividing wall columns at the same time. Instead of considering all model equations as constraints and all process variables as optimization variables in a large and highly nonlinear optimization problem we only incorporate a subset of…
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…
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…
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…
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…