Results 61 to 70 of about 5,689,395 (335)

Synergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment

IEEE Transactions on Power Systems
Unit Commitment (UC) is important for power system operations. With increasing challenges, e.g., growing intermittent renewables and intra-hour net load variability, traditional mathematical optimization could be time-consuming.
Jianghua Wu, P. Luh, Yonghong Chen, B. Yan, Mikhail A. Bragin   +4 more
semanticscholar   +1 more source

An Introduction to Optimization on Smooth Manifolds

, 2023
Optimization on Riemannian manifolds-the result of smooth geometry and optimization merging into one elegant modern framework-spans many areas of science and engineering, including machine learning, computer vision, signal processing, dynamical systems ...
Nicolas Boumal
semanticscholar   +1 more source

Optimum contribution for mate selection in Santa Inês sheep

Revista Brasileira de Zootecnia, 2021
The objective of this research was to simulate the genetic gains expected comparing random mating strategies and mate selection by optimum contribution with different penalty levels in the inbreeding rate of Santa Inês sheep.
José Lindenberg Rocha Sarmento, Leandro Teixeira Barbosa, Arnaldo Dantas Barreto Neto, Alcigeimes Batista Celeste, Natanael Pereira da Silva Santos   +4 more
doaj   +1 more source

Energy valley optimizer: a novel metaheuristic algorithm for global and engineering optimization

Scientific Reports, 2023
In this paper, Energy Valley Optimizer (EVO) is proposed as a novel metaheuristic algorithm inspired by advanced physics principles regarding stability and different modes of particle decay.
M. Azizi, U. Aickelin, Hadi A. Khorshidi, Milad Baghalzadeh Shishehgarkhaneh   +3 more
semanticscholar   +1 more source

Data‐driven forecasting of ship motions in waves using machine learning and dynamic mode decomposition

International Journal of Adaptive Control and Signal Processing, EarlyView.
Summary Data‐driven forecasting of ship motions in waves is investigated through feedforward and recurrent neural networks as well as dynamic mode decomposition. The goal is to predict future ship motion variables based on past data collected on the field, using equation‐free approaches.
Matteo Diez, Mauro Gaggero, Andrea Serani   +2 more
wiley   +1 more source

A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems

International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.
A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam, Anthony Siming Chen, Guido Herrmann   +2 more
wiley   +1 more source

An L_1 then L_0 approach to the cardinality constrained mean-variance and mean-CVaR portfolio optimization problems [PDF]

Mathematics and Modeling in Finance
Cardinality constrained portfolio optimization problems are widely used portfolio optimization models which incorporate restriction on the number of assets in the portfolio.
Maziar Salahi, Tahereh Khodamoradi
doaj   +1 more source

Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics

Advanced Engineering Materials, EarlyView.
Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani, Aditi Singh, Behnam Ashrafi   +2 more
wiley   +1 more source

Multi-Attribute Decision-Making Methods as a Part of Mathematical Optimization

Mathematics, 2019
Optimization problems are relevant to various areas of human activity. In different cases, the problems are solved by applying appropriate optimization methods. A range of optimization problems has resulted in a number of different methods and algorithms
Irina Vinogradova
semanticscholar   +1 more source

Mathematical Modelling and Optimal Control of Anthracnose [PDF]

BIOMATH, 2014
In this paper we propose two nonlinear models for the control of anthracnose disease. The first one is an ordinary differential equation (ODE) model which represents the whithin host evolution of the disease. The second model includes spatial diffusion of the disease in a bounded domain О©. We show well formulation of those models checking existence of
Chris Thron, David Fotsa, Elvis Houpa, David Békollé, Michel Ndoumbe   +4 more
openaire   +5 more sources