A parallel double-level multiobjective evolutionary algorithm for robust optimization
A parallel double-level multiobjective evolutionary algorithm for robust optimization
复制标题
鲁棒优化的并行双级多目标进化算法
DOI：
10.1016/j.asoc.2017.06.008
复制
发表时间：
2017-10
期刊：
影响因子：
--
通讯作者：
Jun Zhang
中科院分区：
文献类型：
--
作者：
Wei-Jie Yu;Jin-Zhou Li;Wei-Neng Chen;Jun Zhang
鲁棒优化是解决不确定优化问题的一种常用方法。然而，传统的鲁棒优化只能在一次运行中找到一个单一的解决方案，这是不够灵活的决策者选择一个满意的解决方案，根据自己的喜好。此外，传统的鲁棒优化往往需要大量的蒙特卡罗模拟来获得数值解，这是相当耗时的。为了解决这些问题，本文提出了一种并行双层多目标进化算法（PDL-MOEA）。PDL-MOEA算法通过将期望和方差作为两个目标，将单目标不确定优化问题转化为双目标问题，从而为决策者提供一组具有不同稳定性的解。在此基础上，提出了一种基于消息传递接口（MPI）的并行进化机制来实现算法的并行化。该并联机构采用双层设计，全局级和子问题级。全球层面充当主机，维护全球人口信息。在子问题级，优化问题被分解成一组子问题，可以并行求解，从而减少了计算时间。实验结果表明，PDL-MOEA一般优于几个国家的最先进的串行/并行MOEA的准确性，效率和可扩展性。
Robust optimization is a popular method to tackle uncertain optimization problems. However, traditional robust optimization can only find a single solution in one run which is not flexible enough for decision-makers to select a satisfying solution according to their preferences. Besides, traditional robust optimization often takes a large number of Monte Carlo simulations to get a numeric solution, which is quite time-consuming. To address these problems, this paper proposes a parallel double-level multiobjective evolutionary algorithm (PDL-MOEA). In PDL-MOEA, a single-objective uncertain optimization problem is translated into a bi-objective one by conserving the expectation and the variance as two objectives, so that the algorithm can provide decision-makers with a group of solutions with different stabilities. Further, a parallel evolutionary mechanism based on message passing interface (MPI) is proposed to parallel the algorithm. The parallel mechanism adopts a double-level design, i.e., global level and sub-problem level. The global level acts as a master, which maintains the global population information. At the sub-problem level, the optimization problem is decomposed into a set of sub-problems which can be solved in parallel, thus reducing the computation time. Experimental results show that PDL-MOEA generally outperforms several state-of-the-art serial/parallel MOEAs in terms of accuracy, efficiency, and scalability.
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DOI：
10.7737/msfe.2015.21.2.001
发表时间：
2015-11
期刊：
Management Science and Financial Engineering
影响因子：
--
作者：
Kyungchul Park
通讯作者：
Kyungchul Park
一种用于多目标优化的双层档案进化算法
影响因子：
11.8
作者：
Ni Chen;Wei-Neng Chen;Yue-Jiao Gong;Zhi-Hui Zhan;Jun Zhang;Yu-Song Tan;Yun Li
通讯作者：
Yun Li
DOI：
10.1145/2598394.2605342
发表时间：
2014-07
期刊：
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
影响因子：
--
作者：
A. Engelbrecht
通讯作者：
A. Engelbrecht
影响因子：
0.9
作者：
S. Iakovidis;C. Apostolidis;Theodoros Samaras
通讯作者：
S. Iakovidis;C. Apostolidis;Theodoros Samaras
DOI：
10.1016/b978-0-12-409547-2.14581-0
发表时间：
2020
期刊：
Comprehensive Chemometrics
影响因子：
--
作者：
Federico Marini;Beata Walczak
通讯作者：
Federico Marini;Beata Walczak