Embryonic array configuration optimization method based on reliability and hardware consumption
| Autor: | Yafeng Meng, Tao Wang, Zhu Sai, Jinyan Cai |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
education.field_of_study Computer science business.industry Mechanical Engineering Crossover Population ComputingMethodologies_MISCELLANEOUS Stability (learning theory) Process (computing) Aerospace Engineering TL1-4050 02 engineering and technology 01 natural sciences 010305 fluids & plasmas 020901 industrial engineering & automation Operator (computer programming) Differential evolution 0103 physical sciences education business Reliability (statistics) Computer hardware Configuration design Motor vehicles. Aeronautics. Astronautics |
| Zdroj: | Chinese Journal of Aeronautics, Vol 32, Iss 3, Pp 639-652 (2019) |
| ISSN: | 1000-9361 |
| Popis: | Embryonic Array (EA) with different configuration methods will directly affect its reliability and hardware consumption. At present, EA configuration design is lack of quantitative analysis method. In order to reasonably optimize EA configuration design, an EA configuration optimization design method is proposed, which is based on the constraints of EA hardware consumption and reliability. Through the analysis of EA working process and composition, quantitative analysis of EA reliability and hardware consumption are completed. Based on the constraints of EA hardware consumption and reliability, the mathematical model of EA configuration optimization design is established, which transfers EA configuration optimization design into an integer nonlinear programming model problem. According to the difference of the fitness value of individual waiting for mutation in population, adaptive mutation operator and crossover operator are selected, and a novel Modified Adaptive Differential Evolution (MADE) algorithm is proposed, which is used to solve EA configuration optimization design problem. Simulation experiments and analysis indicate that the MADE is able to effectively improve the speed, accuracy and stability of algorithm. Moreover, the proposed EA configuration optimization design method can select the most reasonable EA configuration design, and play an important guiding role in EA optimization design. Keywords: Configuration optimization, Differential evolution, Embryonic electronic, Hardware consumption, Integer nonlinear programming model, Reliability, Self-repairing |
| Databáze: | OpenAIRE |
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