Results 101 to 110 of about 83,757 (226)
En este artículo se presenta una metodología que pretende minimizar de forma simultánea, en un ambiente de producción tipo "job shop" correspondiente a una empresa metalmecánica, las siguientes variables: tiempo de proceso, costo de mano de obra directa ...
Germán Augusto Coca Ortegón +2 more
doaj
El presente artículo introduce una variante de la metaheurística simulated annealing, para la resolución de problemas de optimización multiobjetivo. Este enfoque se demonina MultiObjective Simulated Annealing with Random Trajectory Search, MOSARTS.
Felipe Baesler +2 more
doaj
In this study, a multiobjective nonfragile control is proposed for a class of stochastic Takagi and Sugeno (T–S) fuzzy systems with mixed time delays to guarantee the optimal H2 and H∞ performance simultaneously.
Yang Yang +4 more
doaj +1 more source
Abstract Biomass gasification technology has been extensively researched around the world; however, there is a need to evaluate the current research landscape and evolutionary direction of research in the broader context of energy transition. A systematic bibliometric analysis of the Web of Science database was performed for articles that fall within ...
Olasunkanmi Opeoluwa Adeoye +5 more
wiley +1 more source
Tuning PID Controller Using Multiobjective Ant Colony Optimization
This paper treats a tuning of PID controllers method using multiobjective ant colony optimization. The design objective was to apply the ant colony algorithm in the aim of tuning the optimum solution of the PID controllers (Kp, Ki, and Kd) by minimizing ...
Ibtissem Chiha +2 more
doaj +1 more source
A Quantum Adiabatic Algorithm for Multiobjective Combinatorial Optimization
In this work we show how to use a quantum adiabatic algorithm to solve multiobjective optimization problems. For the first time, we demonstrate a theorem proving that the quantum adiabatic algorithm can find Pareto-optimal solutions in finite-time ...
Benjamín Barán, Marcos Villagra
doaj +1 more source
A machine‐learning framework integrating multimodel prediction, feature selection, and SHAP interpretability is developed to uncover structure–performance relationships of Cu‐based CO2‐to‐methanol catalysts. The optimized XGBoost model and an online prediction platform enable accurate selectivity prediction and data‐driven catalyst design.
Conglong Su +11 more
wiley +1 more source
Machine‐Learning‐Enabled Wood with Nanopump Functionalization for Solar Interfacial Evaporation
This study employed machine learning to design an iron‐cobalt‐carbon‐wood photothermal material, achieving high‐efficiency evaporation at 2.807 kg m−2 h−1 and excellent salt resistance. The integrated system increased the daily water production efficiency of solar distillation by 1.5 times, providing an innovative solution for sustainable seawater ...
Chaohai Wang +10 more
wiley +1 more source
POWER: Performance Optimization With Evaluated Results for HEV Battery Selection via MCDM‐TOPSIS
ABSTRACT The increasing transportation demands and environmental concerns in India necessitate the selection of optimal battery technologies for hybrid electric vehicles (HEVs). As the fifth‐largest car market globally, India faces rising vehicle demand, while the transportation sector remains a major contributor to air pollution.
Rinku Kumar Roy +5 more
wiley +1 more source
Ensemble Deep Learning–Based Wind Power Forecasting With Self‐Adaptive Osprey Optimization Algorithm
Design of Self‐Adaptive Osprey (SAO) algorithm: The novel SAO algorithm is designed by integrating the exploration capability of the conventional Osprey algorithm by including the self‐adaptiveness for enhancing the convergence rate. Ensemble Deep Learning for wind power forecasting: The wind forecasting is employed using the proposed Ensemble learning
Johncy Bai Johnson +3 more
wiley +1 more source

