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Reliability–redundancy optimization by means of a chaotic differential evolution approach

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  • Coelho, Leandro dos Santos

Abstract

The reliability design is related to the performance analysis of many engineering systems. The reliability–redundancy optimization problems involve selection of components with multiple choices and redundancy levels that produce maximum benefits, can be subject to the cost, weight, and volume constraints. Classical mathematical methods have failed in handling nonconvexities and nonsmoothness in optimization problems. As an alternative to the classical optimization approaches, the meta-heuristics have been given much attention by many researchers due to their ability to find an almost global optimal solution in reliability–redundancy optimization problems. Evolutionary algorithms (EAs) – paradigms of evolutionary computation field – are stochastic and robust meta-heuristics useful to solve reliability–redundancy optimization problems. EAs such as genetic algorithm, evolutionary programming, evolution strategies and differential evolution are being used to find global or near global optimal solution. A differential evolution approach based on chaotic sequences using Lozi’s map for reliability–redundancy optimization problems is proposed in this paper. The proposed method has a fast convergence rate but also maintains the diversity of the population so as to escape from local optima. An application example in reliability–redundancy optimization based on the overspeed protection system of a gas turbine is given to show its usefulness and efficiency. Simulation results show that the application of deterministic chaotic sequences instead of random sequences is a possible strategy to improve the performance of differential evolution.

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  • Coelho, Leandro dos Santos, 2009. "Reliability–redundancy optimization by means of a chaotic differential evolution approach," Chaos, Solitons & Fractals, Elsevier, vol. 41(2), pages 594-602.
    • Handle: RePEc:eee:chsofr:v:41:y:2009:i:2:p:594-602
    DOI: 10.1016/j.chaos.2008.02.028
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    References listed on IDEAS

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    1. Yang, Dixiong & Li, Gang & Cheng, Gengdong, 2007. "On the efficiency of chaos optimization algorithms for global optimization," Chaos, Solitons & Fractals, Elsevier, vol. 34(4), pages 1366-1375.
    2. Liu, Bo & Wang, Ling & Jin, Yi-Hui & Huang, De-Xian & Tang, Fang, 2007. "Control and synchronization of chaotic systems by differential evolution algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 34(2), pages 412-419.
    3. Gen, Mitsuo & Yun, YoungSu, 2006. "Soft computing approach for reliability optimization: State-of-the-art survey," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 1008-1026.
    4. Zuo, X.Q. & Fan, Y.S., 2006. "A chaos search immune algorithm with its application to neuro-fuzzy controller design," Chaos, Solitons & Fractals, Elsevier, vol. 30(1), pages 94-109.
    5. Chang, Wei-Der, 2007. "Parameter identification of Chen and Lü systems: A differential evolution approach," Chaos, Solitons & Fractals, Elsevier, vol. 32(4), pages 1469-1476.
    6. Li, Lixiang & Yang, Yixian & Peng, Haipeng & Wang, Xiangdong, 2006. "Parameters identification of chaotic systems via chaotic ant swarm," Chaos, Solitons & Fractals, Elsevier, vol. 28(5), pages 1204-1211.
    7. Ali, M.M., 2007. "Differential evolution with preferential crossover," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1137-1147, September.
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