Results 71 to 80 of about 1,840,782 (294)
An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization
Complexity, 2020 Conventional optimization methods are not efficient enough to solve many of the naturally complicated optimization problems. Thus, inspired by nature, metaheuristic algorithms can be utilized as a new kind of problem solvers in solution to these types of
Feng Qian +4 more
doaj +1 more source
Application of optimization algorithms in the design of a superconducting A.C. generator rotor [PDF]
, 1990 The superconducting a.c. generator is expected to be the optimum choice among a.c. generation systems in future because of its reduced size, high efficiency, high terminal voltage and its contribution to the stability of the power system.
Safi, Sabah K.
core
Advanced Science, EarlyView.A DLN dataset was built to analyze MABS composition versus in vitro/in vivo osteogenesis and angiogenesis. An MLP neural network, taking BG morphological parameters as input, extracts bioactive features from these datasets. A rabbit tibial defect model then validates 4D‐printed MABS for adaptability and bone regeneration in critical defects.
Xiongjie Liang +12 more
wiley +1 more source
Advanced Science, EarlyView.Using grilled lamb skewers as a model system, this work builds a multiscale coupling framework from oral processing to retronasal aroma perception, reveals dual‐kinetic release patterns and Electroencephalogram‐characterized central encoding features, and proposes an interpretable physics‐guided deep learning model validated by multiphysics simulation,
Che Shen +12 more
wiley +1 more source
Objective acceleration for unconstrained optimization [PDF]
Numerical Linear Algebra with Applications, 2018 SummaryAcceleration schemes can dramatically improve existing optimization procedures. In most of the work on these schemes, such as nonlinear generalized minimal residual (N‐GMRES), acceleration is based on minimizing the ℓ2 norm of some target on subspaces of .
openaire +3 more sources
Second-order mollified derivatives and optimization [PDF]
The class of strongly semicontinuous functions is considered. For these functions the notion of mollified derivatives, introduced by Ermoliev, Norkin and Wets, is extended to the second order.
Rocca Matteo +2 more
core
, 2011 Our interest lies in solving sum of squares (SOS) relaxations of large-scale unconstrained polynomial optimization problems. Because interior-point methods for solving these problems are severely limited by the large-scale, we are motivated to explore ...
Sun, Xu Andy +2 more
core +1 more source
Advanced Science, EarlyView.An empirical‐aided active learning framework is developed to optimize high‐throughput laser‐induced photothermal annealing of silicon suboxide anodes. By integrating probabilistic machine learning with empirical domain knowledge, this approach achieves optimal electrochemical performance using limited experiments.
Chaeyoung Park +3 more
wiley +1 more source
Distributed Inexact Consensus-Based ADMM Method for Multi-Agent Unconstrained Optimization Problem
IEEE Access, 2019 Recently, the alternating direction method of multipliers (ADMM) has been used effectively to solve the multi-agent unconstrained optimization problems, where the objective function is the sum of privately known local objective functions of agents.
Long Jian +3 more
doaj +1 more source
Constrained Optimization Involving Expensive Function Evaluations: A Sequential Approach [PDF]
This paper presents a new sequential method for constrained non-linear optimization problems.The principal characteristics of these problems are very time consuming function evaluations and the absence of derivative information.
Brekelmans, R.C.M. +3 more
core