In this paper various metamodel-assisted multiobjective evolutionary algorithms (M-MOEA) for optimisation with time-consuming function evaluations are proposed and studied. Gaussian field (Kriging) models fitted by results from previous evaluations are used in order to pre-screen candidate solutions and decide whether they should be rejected or evaluated precisely. The approximations provide upper and lower bound estimations for the true function values. Three different rejection principles are proposed, discussed and integrated into recent MOEA variants (NSGA-II and ∈-MOEA). Experimental studies on a theoretical test case and in airfoil design demonstrate the improvements in diversity of solutions and convergence to the pareto fronts that can be achieved by using metamodels for pre-screening.
[1]
Thomas Bäck,et al.
Metamodel-Assisted Evolution Strategies
,
2002,
PPSN.
[2]
Thomas J. Santner,et al.
Design and analysis of computer experiments
,
1998
.
[3]
Yaochu Jin,et al.
A comprehensive survey of fitness approximation in evolutionary computation
,
2005,
Soft Comput..
[4]
Kalyanmoy Deb,et al.
Computationally effective search and optimization procedure using coarse to fine approximations
,
2003,
The 2003 Congress on Evolutionary Computation, 2003. CEC '03..
[5]
Thomas Bäck,et al.
Evaluating Multi-criteria Evolutionary Algorithms for Airfoil Optimisation
,
2002,
PPSN.