Complex Mechatronic System Reliability Optimization Using Hybrid Genetic Algorithm

Abstract:

Reliability allocation optimization problem of a complex mechatronic system is a highly nonlinear constrained optimization problem, and hence solution to this kind of problem is of NP-hardness even with moderate scale. Due to the nonlinearity combined with multiple local extreme values, traditional optimization techniques fail to arrive at the global or nearly global optimal solution to the problem. Genetic algorithm incorporated with neighboring domain traversal searching technique is utilized in this paper to solve the complex mechatronic system reliability optimization allocation problem. Reliability allocation optimization of the life-support system in a space capsule, being a typical non serial-parallel system, is specifically demonstrated to show the satisfactory convergence performance as well as the important practical value of hybrid genetic algorithm. The simulation results show that the proposed method may gain better precision in solving the complex mechatronic system reliability optimization problem.

• Periodical: Applied Mechanics and Materials (Volumes 138-139)
• Pages: 1296-1301
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.138-139.1296
• Online since: November 2011
• Authors: J. C. Wang, H. Qiu, J. M. Chen, G. D. Ji
• Keywords: Complex System, Hybrid Genetic Algorithm, Mechatronic System, Neighboring Domain Traversal Searching, Non Serial-Parallel System, Reliability Optimization

References

[1] W.C. Yeh, Y.C. Lin, Y.Y. Chung and M.C. Chih: IEEE Trans. Reliability, Vol. 59, No. 1 (2010), p.212–221.

[2] K. Aggarwal, J. Gupta and K. Misra: IEEE Trans. Communications, Vol. 23, No. 5 (1975), p.563–566.

[3] W.J. Ke and S.D. Wang: IEEE Trans. Reliability, Vol. 46, No. 3 (1997), p.342–349.

[4] C. Srivaree-ratana, A. Konak and A.E. Smith: Computers and Operations Research, Vol. 29, No. 7 (2002), p.849–868.

DOI: 10.1016/s0305-0548(00)00088-5

[5] D.W. Coit and A.E. Smith: IEEE Trans. Reliability, Vol. 45, No. 2 (1996), p.254–260.

[6] B. Dengiz, F. Altiparmak and A.E. Smith: IEEE Trans. Evolutionary Computation, Vol. 1, No. 3 (1997), p.179–188.

[7] Y.C. Liang and A.E. Smith: IEEE Trans. Reliability, Vol. 53, No. 3 (2004), p.417–423.

[8] R. Meziane, Y. Massim, A. Zeblah, A. Ghoraf and R. Rahli: Electric Power Systems Research, Vol. 76, No. 1–3 (2005), p.1–8.

DOI: 10.1016/j.epsr.2005.02.008

[9] M. Ouzineba, M. Nourelfatha and M. Gendreau: Reliability Engineering and System Safety, Vol. 93, No. 8 (2008), p.1257–1272.

[10] W.C. Yeh: Expert Systems with Applications, Vol. 36, No. 5 (2009), p.9192–9200.

[11] V. Ravi, B.S.N. Murty and P.J. Reddy: IEEE Trans. Reliability, Vol. 46, No. 2 (1997), p.233–239.