Multiclass feature selection with metaheuristic optimization algorithms: a review
Selecting relevant feature subsets is vital in machine learning, and multiclass feature
selection is harder to perform since most classifications are binary. The feature selection …
selection is harder to perform since most classifications are binary. The feature selection …
Design and performance analysis of PID controller for an automatic voltage regulator system using simplified particle swarm optimization
This paper presents the design and performance analysis of Proportional Integral Derivate
(PID) controller for an Automatic Voltage Regulator (AVR) system using recently proposed …
(PID) controller for an Automatic Voltage Regulator (AVR) system using recently proposed …
Tuning of PID controller based on a multiobjective genetic algorithm applied to a robotic manipulator
Most controllers optimization and design problems are multiobjective in nature, since they
normally have several (possibly conflicting) objectives that must be satisfied at the same …
normally have several (possibly conflicting) objectives that must be satisfied at the same …
A review of foundational methods for checking the structural identifiability of models: Results for rainfall-runoff
Checking for model identifiability has several advantages as outlined in the paper. We
illustrate the use of several screening methods for assessing structural identifiability that …
illustrate the use of several screening methods for assessing structural identifiability that …
Graph neural networks for simulating crack coalescence and propagation in brittle materials
High-fidelity fracture mechanics simulations of multiple microcracks interaction via physics-
based models can become computationally demanding as the number of microcracks …
based models can become computationally demanding as the number of microcracks …
Advanced strategies on update mechanism of Sine Cosine Optimization Algorithm for feature selection in classification problems
Abstract Sine Cosine Algorithm (SCA) that is one of the population-based metaheuristic
optimization algorithms basically consists of the updating mechanism based on sine and …
optimization algorithms basically consists of the updating mechanism based on sine and …
Optimization of multistage fractured horizontal well in tight oil based on embedded discrete fracture model
Optimizing multistage fractured horizontal wells (MFHW) can tap the full potential of tight oil
reservoirs. Although recent studies have introduced various frameworks, most of the …
reservoirs. Although recent studies have introduced various frameworks, most of the …
Improved antlion optimization algorithm via tournament selection and its application to parallel machine scheduling
In this paper, we consider the parallel machine scheduling which is combinatorial
optimization problem. An improved meta-heuristic method is proposed to solve this problem …
optimization problem. An improved meta-heuristic method is proposed to solve this problem …
Machine learning optimization of Majorana hybrid nanowires
As the complexity of quantum systems such as quantum bit arrays increases, efforts to
automate expensive tuning are increasingly worthwhile. We investigate machine learning …
automate expensive tuning are increasingly worthwhile. We investigate machine learning …