167
Views
19
CrossRef citations to date
0
Altmetric
Original Articles

A multi-objective metamodel-assisted memetic algorithm with strength-based local refinement

&
Pages 909-923 | Received 30 Sep 2008, Published online: 18 Sep 2009

References

  • Bonataki , E. , Georgoulis , L. and Giannakoglou , C. G.K.C. Optimal design of combined cycle power plants based on gas turbine performance data . ERCOFTAC design optimization: methods & applications . March–April 31–2 2004 , Athens, Greece.
  • Bosman , A. and de Jong , D. E. Exploiting gradient information in numerical multi-objective evolutionary optimization . GECCO ’05: Genetic and evolutionary computation conference . 2005 , New York, DC. pp. 755 – 762 . ACM .
  • Buche , D. , Schraudolph , N. N. and Koumoutsakos , P. 2005 . Accelerating evolutionary algorithms with gaussian process fitness function models . IEEE Transactions on Systems, Man and Cybernetics – Part C: Applications and Reviews , 35 ( 2 ) : 183 – 194 .
  • Dawkins , R. 1976 . The selfish gene , Oxford : Oxford University Press .
  • Drela , M. and Giles , M. 1987 . Viscous–inviscid analysis of transonic and low Reynolds number airfoils . AIAA Journal , 25 ( 10 ) : 1347 – 1355 .
  • Giannakoglou , K. C. 2002 . Viscous–inviscid analysis of transonic and low Reynolds number airfoils . Progress in Aerospace Sciences , 38 : 43 – 76 .
  • Giannakoglou , K. , Giotis , A. and Karakasis , M. 2001 . Low-cost genetic optimization based on inexact pre-evaluations and the sensitivity analysis of design parameters . Journal of Inverse Problems in Engineering , 9 ( 4 ) : 389 – 412 .
  • Hacker , K. Efficient global optimization using hybrid genetic algorithms . 9th AIAA/ISSMO: Symposium on Multidisciplinary Analysis and Optimization . Atlanta, GA.
  • Hart , W. 1994 . “ Adaptive global optimization with local search ” . USA : University of California . Thesis (PhD)
  • Haykin , S. 1999 . Neural networks: a comprehensive foundation , NJ : Prentice-Hall Upper Saddle River .
  • Huang , V. L. 2007 . Problem definitions for performance assessment of multi-objective optimization algorithms . Technical report. Nanyang Technological University, Singapore Special Session on Constrained Real-Parameter Optimization
  • Ishibuchi , H. and Murata , T. Multiobjective genetic local search algorithm . IEEE International Conference on Evolutionary Computation . May 20–22 1996 , Nagoya, Japan. pp. 119 – 124 .
  • Ishibuchi , H. , Yoshida , T. and Murata , T. 2003 . Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling . IEEE Transactions on Evolutionary Computation , 7 ( 2 ) : 204 – 223 .
  • Jaszkiewicz , A. 2002 . Genetic local search for multi-objective combinatorial optimization . European Journal of Operational Research , 137 ( 22 ) : 50 – 71 .
  • Jin , R. and Chen , W. 2001 . Comparative studies of metamodeling techniques under multiple modeling criteria . Structural and Multidisciplinary Optimization , 23 ( 1 ) : 1 – 13 .
  • Jin , Y. , Olhofer , M. and Sendhoff , B. On evolutionary optimization with approximate fitness functions . GECCO’00: Genetic and Evolutionary Computation Conference . Edited by: Whitley , D. , Goldberg , D. E. Cantu-Paz , E. Vol. 1 , pp. 786 – 793 . Morgan Kaufmann
  • Jin , Y. , Olhofer , M. and Sendhoff , B. 2002 . A framework for evolutionary optimization with approximate fitness functions . IEEE Transactions on Evolutionary Computation , 6 ( 5 ) : 481 – 494 .
  • Karakasis , M. and Giannakoglou , K. 2005 . On the use of metamodel-assisted, multi-objective evolutionary algorithms . Engineering Optimization , 38 ( 8 ) : 941 – 957 .
  • Keane , A. and Nair , P. 2005 . Computational approaches for aerospace design: the pursuit of excellence , Chichester, , UK : Wiley .
  • Knowles , J. and Corne , D. M-PAES: A memetic algorithm for multiobjective optimization . 2000 Congress on Evolutionary Computation – CEC ’00 . July 16–19 2000 , La Jolla, CA. pp. 325 – 332 . IEEE Press .
  • Krasnogor , N. 2002 . “ Studies on the theory and design space of memetic algorithms ” . Bristol, , UK : University of the West of England . Thesis (PhD)
  • Krasnogor , N. and Gustafson , S. 2002 . “ Toward truly ‘memetic’ memetic algorithms: discussion and proof of concepts ” . In Advances in nature-inspired computation: the PPSN VII workshops , Edited by: Corne , D. , Fogel , G. Hart , W. 21 – 22 . Reading, , UK : PEDAL (Parallel, Emergent and Distributed Architectures Laboratory), University of Reading . ISBN 0-9543481-0-9. icalp.tex; 9/12/2003; 16:52
  • Liang , K. , Yao , X. and Newton , C. Combining landscape approximation and local search in global optimization . CEC’99, Congress on evolutionary computation . Washington, DC. pp. 1514 – 1520 . IEEE Press .
  • Liang , K. , Yao , X. and Newton , C. 2000 . Evolutionary search of approximated n-dimensional landscapes . International Journal of Knowledge-based Intelligent Engineering Systems , 4 ( 2 ) : 172 – 183 .
  • Lim , D. 2009 . Generalizing surrogate-assisted evolutionary computation . IEEE Transactions on Evolutionary Computation , in press
  • Madsen , J. , Shyy , W. and Haftka , R. 2000 . Response surface techniques for diffuser shape optimization . AIAA Journal , 38 ( 9 ) : 1512 – 1518 .
  • Moscato , P. 1999 . Memetic algorithms: a short introduction , Maidenhead, , UK : McGraw-Hill .
  • Nocedal , J. and Wright , S. J. 1999 . Numerical optimization , New York, , USA : Springer-Verlag .
  • Ong , Y. S. 2006 . Classification of adaptive memetic algorithms: a comparative study . IEEE Transactions on Systems, Man, and Cybernetics – Part B , 36 ( 1 ) : 141 – 152 .
  • Ong , Y. and Keane , A. 2004 . Meta-Lamarckian learning in memetic algorithms . IEEE Transactions on Evolutionary Computation , 8 ( 2 ) : 99 – 110 .
  • Ong , Y. , Nair , P. and Keane , A. 2000 . Evolutionary optimization of computationally expensive problems via surrogate modeling . AIAA Journal , 41 ( 4 ) : 687 – 696 .
  • Shan , S. and Wang , G. G. 2005 . An efficient Pareto set identification approach for multiobjective optimization on black-box functions . Journal of Mechanical Design , 127 ( 5 ) : 866 – 874 .
  • Ulmer , H. , Streichert , F. and Zell , A. Model-assisted steady-state evolution strategies . GECCO’03: Genetic and evolutionary computation conference . Chicago, MI. pp. 610 – 621 .
  • Ulmer , H. , Streichert , F. and Zell , A. Evolution strategies with controlled model assistance . CEC’04, Congress on evolutionary computation . Portland, OR.
  • Zhou , Z. 2007a . Memetic algorithm using multi-surrogates for computationally expensive optimization problems . Journal of Soft Computing , 11 ( 10 ) : 957 – 971 .
  • Zhou , Z. 2007b . Combining global and local surrogate models to accelerate evolutionary optimization . IEEE Transactions on Systems, Man, and Cybernetics – Part C: Applications and Reviews , 37 ( 1 ) : 66 – 76 .
  • Zitzler , E. , Deb , K. and Thiele , L. 2000 . Comparison of multiobjective evolutionary algorithms: empirical results . Evolutionary Computation , 8 ( 2 ) : 173 – 195 .
  • Zitzler , E. , Laumans , M. and Thiele , L. 2001 . SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization . Technical report. Zurich TIK–Report 103

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.