基于遗传算法的系统可靠性优化

利用简单遗传算法(simple genetic algorithms,SGAs)研究了系统的可靠性优化仿真技术;具体讨论了由5个子系统、每个子系统由4种不同可靠度和寿命周期费用的部件组成的串-并联系统冗余模型,建立了该模型对应的非线性规划(nonlinear integer programming,NIP)问题;在给出该问题精确解的同时,重点阐明了SGAs求解该问题的研究方法。数值结果表明:SGAs是一种具有全局寻优能力的系统可靠性优化方法,该方法在研究一类系统,特别是复杂大系统可靠性优化中具有重要作用。同时,该方法具有高效性、实用性,并可扩展用于求解其他大型复杂拓扑结构系统的多目标优化问题。

System Reliability Optimization Based on Genetic Algorithms

Simple genetic algorithms (SGAs) is utilized to study the simulation technique of the system reliability optimization of a serial-parallel redundancy model. The model is made up of five subsystems, each containing four components with possibly different reliability and life-span fee. The corresponding nonlinear integer programming(NIP) is formulated. The research method of solution to the SGAs is illustrated with the precision solution given. The numerical results show that the SGAs is a method of system reliability optimization which has the global optimum seeking ability. The method plays an important role in studying the system reliability optimization, especially the complex huge one. At the same time, the method has validity and practicability, and can be extended to solve the multi-object optimization concerning huge networks with complex topological structures.