Results 51 to 60 of about 46,149 (202)
Mathematics, 2021 Combinatorial optimization problems are often considered NP-hard problems in the field of decision science and the industrial revolution. As a successful transformation to tackle complex dimensional problems, metaheuristic algorithms have been ...
Md Ashikur Rahman +5 more
doaj +1 more source
A comparison of general-purpose optimization algorithms forfinding optimal approximate experimental designs [PDF]
, 2019 Several common general purpose optimization algorithms are compared for findingA- and D-optimal designs for different types of statistical models of varying complexity,including high dimensional models with five and more factors.
Garcia-Garcia, Jose Carlos +4 more
core
Advanced Intelligent Discovery, EarlyView.A machine learning‐guided self‐driving laboratory screened over 500 nickel‐based layered double‐hydroxide catalysts for alkaline oxygen evolution. Out of the eight metals, the robot uncovered a quaternary Ni–Fe–Cr–Co catalysts requiring only 231 mV overpotential to reach 20 mA cm−2.
Nis Fisker‐Bødker +3 more
wiley +1 more source
Automatic Data Clustering Using Hybrid Firefly Particle Swarm Optimization Algorithm
IEEE Access, 2019 The firefly algorithm is a nature-inspired metaheuristic optimization algorithm that has become an important tool for solving most of the toughest optimization problems in almost all areas of global optimization and engineering practices.
Moyinoluwa B. Agbaje +2 more
doaj +1 more source
Metaheuristics for pharmacometrics
CPT: Pharmacometrics & Systems Pharmacology, 2021 Metaheuristics is a powerful optimization tool that is increasingly used across disciplines to tackle general purpose optimization problems. Nature‐inspired metaheuristic algorithms is a subclass of metaheuristic algorithms and have been shown to be ...
Seongho Kim +4 more
doaj +1 more source
Slime Mould Algorithm-Based Tuning of Cost-Effective Fuzzy Controllers for Servo Systems
International Journal of Computational Intelligence Systems, 2021 This paper suggests five new contributions with respect to the state-of-the-art. First, the optimal tuning of cost-effective fuzzy controllers represented by Takagi–Sugeno–Kang proportional-integral fuzzy controllers (TSK PI-FCs) is carried out using a ...
Radu-Emil Precup +4 more
doaj +1 more source
An Attention‐Assisted Machine Learning System for Deep Microorganism Image Classification
Advanced Intelligent Systems, EarlyView.An attention‐assisted DenseNet201 framework was developed for the classification of eight microorganism classes from microscopic images. The proposed model improved classification performance and achieved an accuracy of 87.38%. Advances in microbiology and environmental health fundamentally depend on precise and timely microorganism identification ...
Yujie Li +6 more
wiley +1 more source
International Journal of Turkish Education SciencesThis paper explores the integration of metaheuristic artificial intelligence (AI) optimization algorithms into the process of curriculum evaluation, proposing a novel approach that could enhance educational outcomes.
Volkan Duran, Gülay Ekici
doaj +1 more source
AI &Innovation, EarlyView.ABSTRACT Nonlinear control systems are an integral part of complex engineering systems. The main difference from linear systems is their ability to adapt to changes and unpredictable conditions. These systems exhibit behaviors that cannot be predicted by simple linear equations, making them essential for applications requiring precise control over a ...
Cinthia Peraza +3 more
wiley +1 more source
Sensors, 2023 Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration
Farkhanda Abbas +6 more
doaj +1 more source