bounded number
2005-01-01
optimization
multiobjective optimization algorithm
better performance
convergence
method
chapter
field
set of solutions
criteria
means
hypervolume
standard benchmark problems
redesign problem
region
selection criteria
false
new algorithm
solution
new approach
set
good convergence
standard methods
chapters
airfoil redesign problem
number
evaluation
measures
metamodel
Pareto
en
algorithm
https://doi.org/10.1007/978-3-540-31880-4_5
EMO algorithms
applicability
sorting
computation
62-76
interesting regions
2005
order
important field
Pareto front
problem
benchmark problems
The hypervolume measure is one of the most frequently applied measures for comparing the results of evolutionary multiobjective optimization algorithms (EMOA). The idea to use this measure for selection is self-evident. A steady-state EMOA will be devised, that combines concepts of non-dominated sorting with a selection operator based on the hypervolume measure. The algorithm computes a well distributed set of solutions with bounded size thereby focussing on interesting regions of the Pareto front(s). By means of standard benchmark problems the algorithm will be compared to other well established EMOA. The results show that our new algorithm achieves good convergence to the Pareto front and outperforms standard methods in the hypervolume covered. We also studied the applicability of the new approach in the important field of design optimization. In order to reduce the number of time consuming precise function evaluations, the algorithm will be supported by approximate function evaluations based on Kriging metamodels. First results on an airfoil redesign problem indicate a good performance of this approach, especially if the computation of a small, bounded number of well-distributed solutions is desired.
steady-state EMOA
concept
time
2022-01-01T19:06
function evaluations
hypervolume measure
non-dominated sorting
front
design optimization
performance
operators
selection operator
optimization algorithm
number of times
evolutionary multiobjective optimization algorithms
An EMO Algorithm Using the Hypervolume Measure as Selection Criterion
Kriging metamodel
approach
https://scigraph.springernature.com/explorer/license/
first results
idea
results
selection
precise function evaluations
approximate function evaluations
size
Chair of Systems Analysis, University of Dortmund, 44221, Dortmund, Germany
Chair of Systems Analysis, University of Dortmund, 44221, Dortmund, Germany
Beume
Nicola
Zitzler
Eckart
Springer Nature - SN SciGraph project
Naujoks
Boris
Emmerich
Michael
978-3-540-31880-4
978-3-540-24983-2
Evolutionary Multi-Criterion Optimization
Computation Theory and Mathematics
Hernández Aguirre
Arturo
Springer Nature
Leiden Institute for Advanced Computer Science, University of Leiden, 2333 CA, Leiden, NL
Leiden Institute for Advanced Computer Science, University of Leiden, 2333 CA, Leiden, NL
Information and Computing Sciences
doi
10.1007/978-3-540-31880-4_5
Coello Coello
Carlos A.
dimensions_id
pub.1022230444