IEEE Trans Cybern. 2016 Aug;46(8):1735-48. doi: 10.1109/TCYB.2014.2382666. Epub 2015 Jan 23.
In the big data era, systems reliability is critical to effective systems risk management. In this paper, a novel multiobjective approach, with hybridization of a known algorithm called NSGA-II and an adaptive population-based simulated annealing (APBSA) method is developed to solve the systems reliability optimization problems. In the first step, to create a good algorithm, we use a coevolutionary strategy. Since the proposed algorithm is very sensitive to parameter values, the response surface method is employed to estimate the appropriate parameters of the algorithm. Moreover, to examine the performance of our proposed approach, several test problems are generated, and the proposed hybrid algorithm and other commonly known approaches (i.e., MOGA, NRGA, and NSGA-II) are compared with respect to four performance measures: 1) mean ideal distance; 2) diversification metric; 3) percentage of domination; and 4) data envelopment analysis. The computational studies have shown that the proposed algorithm is an effective approach for systems reliability and risk management.
在大数据时代，系统可靠性对于有效的系统风险管理至关重要。在本文中，开发了一种新的多目标方法，该方法将一种称为 NSGA-II 的已知算法与自适应基于种群的模拟退火（APBSA）方法进行了混合，以解决系统可靠性优化问题。在第一步中，为了创建一个好的算法，我们使用了协同进化策略。由于所提出的算法对参数值非常敏感，因此使用响应面方法来估计算法的适当参数。此外，为了检验所提出方法的性能，生成了几个测试问题，并针对四个性能指标将所提出的混合算法与其他常用方法（即 MOGA、NRGA 和 NSGA-II）进行了比较：1）平均理想距离；2）多样性度量；3）占主导地位的百分比；和 4）数据包络分析。计算研究表明，所提出的算法是系统可靠性和风险管理的有效方法。
