24 papers · ranked by Valyu relevance
Ahadi, Pouya, Marzban, Reza + 4 more
Bayesian optimization is widely used for optimizing expensive black box functions, but most existing approaches focus on scalar responses. In many scientific and engineering settings the response is functional, varying smoothly over an index such as time or wavelength, which makes classical formulations inadequate.…
Philippe Preux, Rémi Munos, Michal Valko
We consider function optimization as a sequential decision making problem under budget constraint. This constraint limits the number of objective function evaluations allowed during the optimization. We consider an algorithm inspired by a continuous version of a multi-armed bandit problem which attacks this…
Huang, Jingru, Haifeng Xu, Jiang + 2 more
Bayesian optimization (BO) has been widely used to optimize expensive and gradient-free objective functions across various domains. However, existing BO methods have not addressed the objective where both inputs and outputs are functions, which increasingly arise in complex systems as advanced sensing technologies. To…
Marius Pille, Leon Martin, Emilius Richter, Dionysios Perdikis + 2 more
Personalized brain modeling at clinically relevant scales requires integrating biophysical models with empirical neuroimaging data, yet high-dimensional parameter estimation in whole-brain network models remains computationally prohibitive. We present TVB-Optim, an open-source Python library providing a general and…
Elliot Xie, Lingxin Cheng, Yujia Cai, Jack Shireman + 1 more
Performance bottlenecks in widely used genomics and bioinformatics software present a substantial and growing burden as biological datasets continue to increase in size and number. Relieving these bottlenecks relies largely on expert manual optimization and therefore remains difficult to scale. Here we present…
Simon Klüttermann
The development of gradient descent [[1]] made it possible to create and improve many data mining algorithms, for example, by allowing methods based on neural networks to be trained efficiently. Still, gradient descent methods have their limits. Most importantly, gradient descent requires a continuous loss function and…
Thomas Bartz-Beielstein
The `spotoptim` package implements surrogate-model-based optimization of expensive black-box functions in Python. Building on two decades of Sequential Parameter Optimization (SPO) methodology, it provides a Kriging-based optimization loop with Expected Improvement, support for continuous, integer, and categorical…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…
Serena Landers, Sahil Pontula, Shiekh Zia Uddin, Sachin Vaidya + 2 more
We introduce the CLUSTER algorithm (\textbf{c}oordinate-\textbf{l}evel \textbf{u}pdate \textbf{s}trategy for \textbf{t}rust-region step \textbf{e}valuation \textbf{r}efinement) for local derivative-free optimization problems where there is a cost to changing each parameter (or clusters of parameters). For example, this…
Odin Zhang, Jiaqi Wang, Tuscan Rock Thompson, Ziyi You + 3 more
Biomolecular interactions, including protein–protein interactions, protein–nucleic acid recognition, and protein–small molecule binding, underlie a wide range of biological processes and therapeutic mechanisms. Although recent de novo design methods can generate candidate binders for diverse molecular targets…
Yuanqing Zhang, Baolin Xiong, Le Feng, Liang Li + 4 more
Quadrupole mass spectrometers are highly sensitive and specific analytical instruments, widely used in pharmaceuticals, clinical diagnostics, and other fields. Their performance depends on a tuning process to optimize key parameters, which has traditionally relied on engineers’ expertise or simple univariate search…
Shuxin Guo, Chenxu Guo, Jianhua Jiang, Heming Jia
A Multi-Layer Perceptron (MLP), as the basic structure of neural networks, is an important component of various deep learning models such as CNNs, RNNs, and Transformers. Nevertheless, MLP training faces significant challenges, with a large number of saddle points and local minima in its non-convex optimization space…
Authors not listed
Developing a transferable classical force field (FF) has historically been a lengthy, expert-informed process. In this work, we integrate optimization, machine learning, and data science techniques to accelerate the systematic design and parameterization of transferable FF models. As a demonstration, we create…
Serdar Ekinci, Davut Izci, Mostafa Jabari, Mohit Bajaj + 2 more
Achieving precise and stable engine speed regulation in spark-ignition (SI) systems remains a challenging task because of the inherent nonlinearities, time-varying characteristics, and external disturbances of internal combustion engines (ICEs). Conventional proportional-integral-derivative (PID) controllers often fail…
Mohamad Hosein Rabiei, Elnaz Eilbeigi, Siamak Talatahari, Mohammadtaghi Alami + 2 more
In this paper, we introduce CSSRank, an improved version of the charged system search (CSS) algorithm, designed to address complex optimization problems more efficiently. CSSRank integrates a rank-based reduction selection strategy to enhance exploitation by progressively reducing the number of charged particles used…
Ali Asghari, Mohammadhossein Mohammadi, Heming Jia
Metaheuristic algorithms are widely used to find optimal or near-optimal solutions for complex problems by taking inspiration from natural behaviors and processes. Although many different methods have been developed, a common problem in many of them is maintaining a good balance between exploration and exploitation and…
Authors not listed
Finding the most stable adsorption geometry of a flexible molecule on a catalytic surface remains a key challenge due to the high dimensionality and ruggedness of the potential energy surface. We present a Gradient-Enhanced Genetic Algorithm (GE-GA) for the global optimization of adsorbate–surface configurations…
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…
Aiman H. H. Al-Masoodi, Nasir Shafiq, Abtisam Hasan Hamood Al-Masoodi
Recently, there has been advancement in tall building design to achieve lighter structures, which led to an increase in the challenges that are related to wind resistance and cost efficiency. This study is to provide multi-optimization gathering between the aerodynamic optimization with a radial basis function…
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…
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…
Mrinal Kanti Rajak, Rajen Pudur
This paper presents a novel Mitochondrial Energy Production Optimization (MEPO) algorithm for enhancing grid-connected inverter control under weak grid conditions. The proposed bio-inspired approach addresses critical challenges in maintaining power quality and system stability in low Short Circuit Ratio (SCR)…
Assem F. Alabu-Husain, Mostafa A. ElBahloul, Mahmoud M. Saafan, Eman M. El-Gendy
This study introduces the Pray Optimization Algorithm (POA), a novel metaheuristic inspired by the procedural rituals of Islamic pray, designed to solve complex engineering and robotic manipulator problems. The mathematical model is structured into three distinct phases: Phase I simulates searching for a suitable…
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…