Search · four archives
Search · four archives
24 papers · ranked by Valyu relevance
Yadira Boada, Fernando N. Santos-Navarro, Jesús Picó, Alejandro Vignoni
'Alejandro Vignoni'] Achieving optimal production in microbial cell factories, robustness against changing intracellular and environmental perturbations requires the dynamic feedback regulation of the pathway of interest. Here, we consider a merging metabolic pathway motif, which appears in a wide range of metabolic…
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…
Junhui Hu, Hongxiang Cai, Shiyong Zhang, Chuanxun Pei + 2 more
'Bilal Alatas'] The electric power infrastructure is the cornerstone of contemporary society’s sustenance and advancement. Within the intelligent electric power financial system, substantial inefficiency and waste in information management persist, leading to an escalating depletion of resources. Addressing diverse…
Jiyizhe Zhang, Daria Semochkina, Naoto Sugisawa, David Woods + 1 more
Multi-objective Bayesian optimization (MOBO) has shown to be a promising tool for reaction development. However, noise is usually unavoidable during experiments and makes it challenging to find reliable solutions. In this study, we focus on finding a set of optimal reaction conditions using multi-objective Euclidian…
Ruochen Zhang, Bin Zhu, Zhaoxia Guo
Small-world effect plays an important role in the field of network science, and optimizing the small-world property has been a focus, which has many applications in computational social science. In the present study, we model the problem of optimizing small-world property as a multiobjective optimization, where the…
Trong-The Nguyen, Thi-Kien Dao, Van-Thien Nguyen, Duc-Tinh Pham + 1 more
'Heming Jia'] Designing effective drug therapies requires balancing competing objectives, such as therapeutic efficacy, safety, and cost efficiency-a task that poses significant challenges for conventional optimization methods. To address this, we propose the multi-objective spider-wasp optimizer (MOSWO), a novel…
Noor A. Rashed, Yossra H. Ali, Tarik A. Rashid, Ahmed Salih
Algorithms, Applications, and Trends for Solving Complex Real-World Problems Authors: ['Noor A. Rashed' 'Yossra H. Ali' 'Tarik A. Rashid' 'Ahmed Salih'] Multi-Objective Optimization (MOO) techniques have become increasingly popular in recent years due to their potential for solving real-world problems in various…
Tianyu Liu, Junjie Zhu, Lei Cao, Shu-Chuan Chu
For large-scale multiobjective evolutionary algorithms based on the grouping of decision variables, the challenge is to design a stable grouping strategy to balance convergence and population diversity. This paper proposes a large-scale multiobjective optimization algorithm with two alternative optimization methods…
Laurence Legon, Christophe Corre, Declan G. Bates, Ahmad A. Mannan
A widely applicable strategy for developing evolutionarily robust cell factories is to knock out (KO) genes or reactions to couple chemical synthesis with cell growth. Genome-scale metabolic models enable their rational design, but KOs that provide growth-coupling (gc) are rare in the immense design space, making…
Amin Ibrahim, Azam Asilian Bidgoli, Shahryar Rahnamayan, Kalyanmoy Deb
—As the interest in multi- and many-objective optimization algorithms grows, the performance comparison of these algorithms becomes increasingly important. A large number of performance indicators for multi-objective optimization algorithms have been introduced, each of which evaluates these algorithms based on a…
Jaza M. Abdullah, Tarik A. Rashid, Bestan B. Maaroof, Seyedali Mirjalili
'Seyedali Mirjalili'] This paper proposes the multi-objective variant of the recently-introduced fitness dependent optimizer (FDO). The algorithm is called a Multi-objective Fitness Dependent Optimizer (MOFDO) and is equipped with all five types of knowledge (situational, normative, topographical, domain, and…
Tyler H. Chang, Stefan M. Wild
Multiobjective simulation optimization (MOSO) problems are optimization problems with multiple conflicting objectives, where evaluation of at least one of the objectives depends on a black-box numerical code or real-world experiment, which we refer to as a simulation. This paper describes the design goals driving the…
Sevil Zanjani Miyandoab, Shahryar Rahnamayan, Azam Asilian Bidgoli
— Feature selection is an expensive challenging task in machine learning and data mining aimed at removing irrelevant and redundant features. This contributes to an improvement in classification accuracy, as well as the budget and memory requirements for classification, or any other postprocessing task conducted after…
Ke Bao, Wei Fang, Yourong Ding
Although the integrated model has good convergence ability, it is difficult to solve the multimodal problem and noisy problem due to the lack of uncertainty evaluation. Radial basis function model performs best for different degrees of nonlinear problems with small-scale and noisy training datasets but is insensitive…
Raimundo, Marcos M., Ferreira, Paulo A. V. + 2 more
This work proposes a novel multi-objective optimization approach that globally finds a representative non-inferior set of solutions, also known as Pareto-optimal solutions, by automatically formulating and solving a sequence of weighted sum method scalarization problems. The approach is called MONISE (Many-Objective…
Nicola Hallmann, Catalina Guerra-Cornejo, Karl Burgess, Charlotte Merzbacher + 1 more
The optimization of culture media is critical for improving the efficiency and cost of cellular production systems. Traditional approaches often rely on extensive experimental trials or statistical methods, which can be both costly and time-consuming. Here, we present gsMOBO, a computational approach for media design…
Anusree Kanadath, J. Angel Arul Jothi, Siddhaling Urolagin, Luminiţa Moraru + 1 more
Histopathology image analysis is considered as a gold standard for the early diagnosis of serious diseases such as cancer. The advancements in the field of computer-aided diagnosis (CAD) have led to the development of several algorithms for accurately segmenting histopathology images. However, the application of swarm…
Lu Hong, Tanja Kortemme
With recent methodological advances in the field of computational protein design, in particular those based on deep learning, there is an increasing need for frameworks that allow for coherent, direct integration of different models and objective functions into the generative design process. Here we demonstrate how…
Ma Long, Dong Zhikui, Liu Chunjiang, Gao Peng + 4 more
'Hou Binfeng' 'Qi Xiangdong' 'Jiang Yunhong'] The safety performance and structural stiffness of a rim, which is the main load-bearing structure of the loader during operation, influence the overall performance, stability, and braking capabilities of the machine. In the industry, researchers are currently pursuing…
Rohan Chandraghatgi, Hai-Feng Ji, Gail L. Rosen, Bahrad A. Sokhansanj
Recent advances in computational methods provide the promise of dramatically accelerating drug discovery. While math-ematical modeling and machine learning have become vital in predicting drug-target interactions and properties, there is untapped potential in computational drug discovery due to the vast and complex…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
Tatsuya Yoshizawa, Shoichi Ishida, Tomohiro Sato, Masateru Ohta + 2 more
Molecular design using data-driven generative models has emerged as a promising technology, impacting various fields such as drug discovery and the development of functional materials. However, this approach is often susceptible to optimization failure due to reward hacking, where prediction models fail to accurately…
Authors not listed
Optimizing the synthesis conditions of advanced materials is challenging, especially when outcomes are subject to inherent experimental uncertainties. Bayesian optimization is a popular tool for accelerating materials discovery, but its standard risk-neutral framework overlooks the variability of outcomes under…
Ken Jenewein, Luca Torresi, Navid Haghmoradi, Attila Kormányos + 2 more
Experimental catalyst optimization is plagued by slow and laborious efforts. Finding innovative materials is key to advancing research areas for sustainable energy conversion, such as electrocatalysis. Artificial intelligence (AI)-guided optimization bears great potential to autonomously learn from data and plan new…