[HTML][HTML] Sources of hydrological model uncertainties and advances in their analysis

E Moges, Y Demissie, L Larsen, F Yassin - Water, 2021 - mdpi.com
Water | Free Full-Text | Review: Sources of Hydrological Model Uncertainties and Advances
in Their Analysis Next Article in Journal Assessing the Influence of Compounding Factors to …

Performance-based screening of porous materials for carbon capture

AH Farmahini, S Krishnamurthy, D Friedrich… - Chemical …, 2021 - ACS Publications
Computational screening methods have changed the way new materials and processes are
discovered and designed. For adsorption-based gas separations and carbon capture, recent …

Bio-inspired computation: Where we stand and what's next

J Del Ser, E Osaba, D Molina, XS Yang… - Swarm and Evolutionary …, 2019 - Elsevier
In recent years, the research community has witnessed an explosion of literature dealing
with the mimicking of behavioral patterns and social phenomena observed in nature towards …

[HTML][HTML] Designing diversified renewable energy systems to balance multisector performance

JM Gonzalez, JE Tomlinson, EA Martínez Ceseña… - Nature …, 2023 - nature.com
Renewable energy system development and improved operation can mitigate climate
change. In many regions, hydropower is called to counterbalance the temporal variability of …

Large-scale many-objective deployment optimization of edge servers

B Cao, S Fan, J Zhao, S Tian, Z Zheng… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
The development of the Internet of Vehicles (IoV) has made transportation systems into
intelligent networks. However, with the increase in vehicles, an increasing number of data …

Pareto multi-task learning

X Lin, HL Zhen, Z Li, QF Zhang… - Advances in neural …, 2019 - proceedings.neurips.cc
Multi-task learning is a powerful method for solving multiple correlated tasks simultaneously.
However, it is often impossible to find one single solution to optimize all the tasks, since …

A reference vector guided evolutionary algorithm for many-objective optimization

R Cheng, Y Jin, M Olhofer… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
In evolutionary multiobjective optimization, maintaining a good balance between
convergence and diversity is particularly crucial to the performance of the evolutionary …

A decision variable clustering-based evolutionary algorithm for large-scale many-objective optimization

X Zhang, Y Tian, R Cheng, Y Jin - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
The current literature of evolutionary many-objective optimization is merely focused on the
scalability to the number of objectives, while little work has considered the scalability to the …

An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints

K Deb, H Jain - IEEE transactions on evolutionary computation, 2013 - ieeexplore.ieee.org
Having developed multiobjective optimization algorithms using evolutionary optimization
methods and demonstrated their niche on various practical problems involving mostly two …

Introductory overview: Optimization using evolutionary algorithms and other metaheuristics

HR Maier, S Razavi, Z Kapelan, LS Matott… - … modelling & software, 2019 - Elsevier
Environmental models are used extensively to evaluate the effectiveness of a range of
design, planning, operational, management and policy options. However, the number of …