Complex Maintenance of Aircraft Engine Structural Components Using Genetic Algorithm and Multi-Objective Optimization


Article Preview

The GA and multi-objective optimization approach is flexible in application and extremely reliable, providing optimal results for all optimization problems attempted. Aircraft engine maintenance using nonlinear analyses. This considerably slows the entire production and maintenance process. There are three approaches for this purpose. GA with a gradient-based method to improve quality enhancements and moderate convergence efficiency; the multi-objective framework will integrate the analysis codes for multiple disciplines, instead of relying on one code to perform the analysis for all disciplines; using optimize a multi-objective model by implementation of GA instead the computationally expensive functions. Results indicate that is to provide a capability to yield a better production and maintenance in much reduced time and cost.



Edited by:

Ran Chen






J. Cai et al., "Complex Maintenance of Aircraft Engine Structural Components Using Genetic Algorithm and Multi-Objective Optimization", Applied Mechanics and Materials, Vols. 44-47, pp. 2988-2992, 2011

Online since:

December 2010




[1] Vanderplaats, G.N.: Numerical Optimization Techniques for Engineering Design, 4th edn. Vanderplaats Research and Development, Inc., Colorado Springs (2005).

[2] Haupt, R.L., Haupt, S.E.: Pratical Genetic Algorithms, 2nd edn. Wiley-Interscience Publication, New York, USA (2004).

[3] Felipe A. C. Viana , Valder Steffen Jr. , Sergio Butkewitsch: Optimization of aircraft structural components by using nature-inspired algorithms and multi-fidelity approximations, J Glob Optim (2009) 45: 427–449 DOI 10. 1007/s10898-008-9383-x.

DOI: 10.1007/s10898-008-9383-x

[4] Terry L. Holst and Thomas H. Pulliam :Evaluation of Genetic Algorithm Concepts Using Model Problems,NASA/TM–2003-212813.

[5] Chan-gi Pak, Wesley Li: Multidisciplinary Design, Analysis, and Optimization Tool Development Using a Genetic Algorithm , NASA Dryden Flight Research Edwards, California, NASA/TM-2009-214645.

[6] Haritha Saranga, U. Dinesh Kumar: Optimization of aircraft maintenance/support infrastructure using genetic algorithms—level of repair analysis, Ann Oper Res (2006) 143: 91–106 DOI 10. 1007/s10479-006-7374-1.

DOI: 10.1007/s10479-006-7374-1

[7] Kleeman, M.P., Lamont, G.B.: Scheduling of Flow-Shop, Job-Shop, and Combined Scheduling Problems using MOEAs with Fixed and Variable Length Chromosomes. SCI, vol. 49, p.49–99. Springer, Heidelberg (2007).

DOI: 10.1007/978-3-540-48584-1_3

[8] Garcia-Cascales, M.S.: Selection of a cleaning system for engine maintenance based on the analytic hierarchy process. Computers and Industrial Engineering 56(4), 1442–1451 (2009).

DOI: 10.1016/j.cie.2008.09.015

[9] Mohamad, M.S., Omatu, S., Deris, S., Misman, M.F., Yoshioka, M.: A multi-objective strategy in genetic algorithms for gene selection of gene expression data. Artificial Life and Robotics 13(2), 410–413 (2009).

DOI: 10.1007/s10015-008-0533-5

In order to see related information, you need to Login.