24 papers · ranked by Valyu relevance
Huayuan Sun
—The choices of hyperparameters have critical effects on the performance of machine learning models. In this paper, we present a general framework that is able to construct an adaptive optimizer, which automatically adjust the appropriate algorithm and parameters in the process of optimization. Examining the method of…
Nishant Gadde
Gaussian Process (GP) models have also become extremely useful for optimization under uncertainty algorithms, especially where the objective functions are costly to compute. Yet, the more classical methods usually adopt strategies that, in certain circumstances, might be effective but not flexible to be applied to a…
Bestoun S. Ahmed
—The rapidly changing landscapes of modern optimization problems require algorithms that can be adapted in real-time. This paper introduces an Adaptive Metaheuristic Framework (AMF) designed for dynamic environments. It is capable of intelligently adapting to changes in the problem parameters. The AMF combines a…
Xiaojie Tang, Chengfen Jia, Pengju Qu, Qian Zhang + 2 more
Addressing the challenges of high dimensionality, strong nonlinearity, and multiple constraints in multi-UAV cooperative path planning, this paper proposes a Behavior-Adaptive Aquila Optimizer (EAO) achieved by enhancing Aquila Optimizer (AO). EAO constructs a multi-strategy cooperative framework that integrates a…
Esraa Hassan, Mahmoud Y. Shams, Noha A. Hikal, Samir Elmougy
Optimization algorithms are used to improve model accuracy. The optimization process undergoes multiple cycles until convergence. A variety of optimization strategies have been developed to overcome the obstacles involved in the learning process. Some of these strategies have been considered in this study to learn more…
Sina Dabiri, Eric R. Cole, Robert E. Gross
Brain stimulation has become an important treatment option for a variety of neurological and psychiatric diseases. A key challenge in improving brain stimulation is selecting the optimal set of stimulation parameters for each patient, as parameter spaces are too large for brute-force search and their induced effects…
Shuxin Wang, Yejun Zheng, Li Cao, Mengji Xiong + 2 more
'Xuewen Xia'] In this study, a brand-new algorithm called the Comprehensive Adaptive Enterprise Development Optimizer (CAED) is proposed to overcome the drawbacks of the Enterprise Development (ED) algorithm in complex optimization tasks. In particular, it aims to tackle the problems of slow convergence and low…
Sarit Maitra
The existing variants of the Differential Evolution (DE) algorithm come with certain limitations, such as poor local search and susceptibility to premature convergence. This study introduces Adaptive Differential Evolution with Diversification (ADED), a method that dynamically modifies the neighborhood structure by…
Mohamed Elhosseny, Mahmoud Abdel-Salam, Ibrahim M. El-Hasnony
The Crayfish Optimization Algorithm (COA) is a recent powerful algorithm that is sometimes plagued by poor convergence speed and a tendency to rapidly converge to the local optimum. This study introduces a variation of the COA called Adaptive Dynamic COA with a Locally enhanced escape operator (AD-COA-L) to tackle…
Mandana Kariminejad, T. David, Caitríona M. Ryan, Christopher O’Hara + 2 more
Injection Moulding Process via a Bayesian Adaptive Design of Experiment Approach Authors: ['Mandana Kariminejad' 'T. David' 'Caitríona M. Ryan' 'Christopher O’Hara' 'Albert Weinert' 'Marion McAfee'] Minimising cycle time without inducing quality defects is a major challenge in the injection moulding (IM). Design of…
Honglin Kan, Yaping Xiao, Zhiliang Gao, Xuan Zhang + 2 more
The Aquila Optimizer (AO) is a novel and efficient optimization algorithm inspired by the hunting and searching behavior of Aquila. However, the AO faces limitations when tackling high-dimensional and complex optimization problems due to insufficient search capabilities and a tendency to prematurely converge to local…
Sumedh S Nagrale, Alik S Widge
The use of Deep Brain Stimulation (DBS) on the ventral capsule/ventral striatum (VCVS) has therapeutic potential for patients with refractory psychiatric disorders, but clinical success is impeded by the need for a time-consuming and trial-and-error process when setting the parameters, this process relying on…
Authors not listed
For applications in gas sensing, purification, and capture, we often wish to search a large set of metal-organic frameworks (MOFs) for the top-K in terms of their Henry coefficient of an adsorbate. A molecular simulation to predict the Henry coefficient of a MOF constitutes a Monte Carlo integration where each sample…
Finlay Clark, Graeme Robb, Daniel Cole, Julien Michel
Alchemical absolute binding free energy (ABFE) calculations have substantial potential in drug discovery, but are often prohibitively computationally expensive. To unlock their potential, efficient automated ABFE workflows are required to reduce both computational cost and human intervention. We present a…
Sarada Mohapatra, Prabhujit Mohapatra
A novel bio-inspired meta-heuristic algorithm, namely the American zebra optimization algorithm (AZOA), which mimics the social behaviour of American zebras in the wild, is proposed in this study. American zebras are distinguished from other mammals by their distinct and fascinating social character and leadership…
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…
Máté Mohácsi, Márk Patrik Török, Sára Sáray, Luca Tar + 1 more
Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. Recently, manual model tuning has been replaced by automated parameter search using a variety of different tools and methods. However, using most of these software tools and choosing the most appropriate…
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…
Harini Narayanan, Joshua Hinckley, Rachel Barry, Brendan Dang + 4 more
Optimizing operational conditions for complex biological systems used in life sciences research and biotechnology is an arduous task. Here, we have applied a Bayesian Optimization-based iterative framework for experimental design to accelerate cell culture media development for two applications. First, we show this…
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…
Maneeshika M. Madduri, Momona Yamagami, Si Jia Li, Sasha Burckhardt + 2 more
Neural interfaces can restore or augment human sensorimotor capabilities by converting high-bandwidth biological signals into control signals for an external device via a decoder algorithm. Leveraging user and decoder adaptation to create co-adaptive interfaces presents opportunities to improve usability and…
AKHIL SHAJAN, Madushanka Manathunga, Andreas Goetz, Kenneth Merz
Based on a series of energy minimizations with starting structures obtained from the Baker test set of 30 organic molecules, a comparison is made between various open-source geometry optimization codes that are interfaced with the open-source QUantum Interaction Computational Kernel (QUICK) program for gradient and…
Ning Fang, Cheng Xu, Xuxiong Gong, Zhouhua Wu
A novel human-inspired metaheuristic algorithm, termed Offensive Defensive Optimization, has been introduced to address single-objective optimization problems. This algorithm draws inspiration from the varied strategies utilized by players in board games, emulating and conceptualizing offensive and defensive behaviors…
Riley Hickman, Malcolm Sim, Sergio Pablo-García, Ivan Woolhouse + 6 more
Self-driving laboratories (SDLs) are next-generation research and development platforms for closed-loop, autonomous experimentation that combine ideas from artificial intelligence, robotics, and high-performance computing. A critical component of SDLs is the decision-making algorithm used to prioritize experiments to…