Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions
The development and application of evolutionary algorithms (EAs) and other metaheuristics
for the optimisation of water resources systems has been an active research field for over …
for the optimisation of water resources systems has been an active research field for over …
A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms
Evolutionary algorithms are widely used for solving multiobjective optimization problems but
are often criticized because of a large number of function evaluations needed …
are often criticized because of a large number of function evaluations needed …
SOCEMO: surrogate optimization of computationally expensive multiobjective problems
J Müller - INFORMS Journal on Computing, 2017 - pubsonline.informs.org
We present the algorithm SOCEMO for optimization problems that have multiple conflicting
computationally expensive black-box objective functions. The computational expense …
computationally expensive black-box objective functions. The computational expense …
Multiobjective adaptive surrogate modeling‐based optimization for parameter estimation of large, complex geophysical models
Parameter specification is an important source of uncertainty in large, complex geophysical
models. These models generally have multiple model outputs that require multiobjective …
models. These models generally have multiple model outputs that require multiobjective …
[HTML][HTML] Progressive-fidelity computation of the genetic algorithm for energy-efficient virtual machine placement in cloud data centers
Energy efficiency is a critical issue in the management and operation of data centers, which
form the backbone of cloud computing. Virtual machine placement has a significant impact …
form the backbone of cloud computing. Virtual machine placement has a significant impact …
Multi-objective optimization using surrogates
I Voutchkov, A Keane - Computational Intelligence in Optimization …, 2010 - Springer
Until recently, optimization was regarded as a discipline of rather theoretical interest, with
limited real-life applicability due to the computational or experimental expense involved …
limited real-life applicability due to the computational or experimental expense involved …
Multiobjective optimization using surrogates
I Voutchkov, AJ Keane - 2006 - eprints.soton.ac.uk
Until recently, optimization was regarded as a discipline of rather theoretical interest, with
limited real-life applicability due to the comutational or experimental expense involved …
limited real-life applicability due to the comutational or experimental expense involved …
A multi-objective adaptive surrogate modelling-based optimization algorithm for constrained hybrid problems
Many multi-objective optimization problems in integrated environmental modelling and
management involve not only continuous decision variables but also variables like integers …
management involve not only continuous decision variables but also variables like integers …
An evolutionary algorithm with spatially distributed surrogates for multiobjective optimization
In this paper, an evolutionary algorithm with spatially distributed surrogates (EASDS) for
multiobjective optimization is presented. The algorithm performs actual analysis for the initial …
multiobjective optimization is presented. The algorithm performs actual analysis for the initial …
Surrogate assisted evolutionary algorithm for multi-objective optimization
Evolutionary algorithms (EAs) are population based approaches that start with an initial
population of candidate solutions and evolve them over a number of generations to finally …
population of candidate solutions and evolve them over a number of generations to finally …