Abstract
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.
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References
K. Deb, M. Mohan, and S. Mishra. A fast multiobjective evolutionary algorithm for finding well-spread pareto-optimal solutions. Technical Report 2003002, KanGAL, Kanpur, India, 2003.
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multi-bjective genetic algorithm nsga-ii. Technical Report 2000001, KanGAL, Kanpur, India, 2000.
M. Emmerich, A. Giotis, M. Özdemir, Th. Bäck, and K. Giannakoglou. Metamodel-Assisted Evolution Strategies. In J. J. Merelo Guervós, P. Adamidis, H.-G. Beyer, J. L. Fernández-Villacañas, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature-PPSN VII, Proc. Seventh Int’l Conf., Granada, pages 361–371, Berlin, 2002. Springer.
A. P. Giotis, K. C. Giannakoglou, and J. Périaux. A Reduced-Cost Multi-Objective Optimization Method Based On The Pareto Front Technique, Neural Networks And PVM. In Proc. European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS’00), (CD-ROM), Barcelona, 2000. Center for Numerical Methods in Engineering (CIMNE).
Y. Jin. A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing Journal (in press), 2003.
P. K. Nain and K. Deb. Computationally effective search and optimization procedure using coarse to fine approximations. In Proc. of the Congress on Evolutionary Computation CEC 2003, Canberra, Australia, pages 2081-2088, 2003.
B. Naujoks, L. Willmes, Th. Bäck, and W. Haase. Evaluating multi-criteria evolutionary algorithms for airfoil optimisation. In J. J. Merelo Guervós, P. Adamidis, H.-G. Beyer, J. L. Fernández-Villacañas, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature-PPSN VII, Proc. Seventh Int’l Conf., Granada, pages 841–850, Berlin, 2002. Springer.
J. Sacks, W. J. Welch, W. J. Mitchell, and H.-P. Wynn. Design and analysis of computer experiments. Statistical Science, 4:409–435, 1989.
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Emmerich, M., Naujoks, B. (2004). Metamodel Assisted Multiobjective Optimisation Strategies and their Application in Airfoil Design. In: Parmee, I.C. (eds) Adaptive Computing in Design and Manufacture VI. Springer, London. https://doi.org/10.1007/978-0-85729-338-1_21
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DOI: https://doi.org/10.1007/978-0-85729-338-1_21
Publisher Name: Springer, London
Print ISBN: 978-1-85233-829-9
Online ISBN: 978-0-85729-338-1
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