An Efficient Imperialist Competitive Algorithm for Solving the QFD Decision Problem
An Efficient Imperialist Competitive Algorithm for Solving the QFD Decision Problem
复制标题
解决QFD决策问题的高效帝国主义竞争算法
DOI：
10.1155/2016/2601561
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发表时间：
2016-11
影响因子：
--
通讯作者：
Hengdong Guo
中科院分区：
文献类型：
--
作者：
Xue Ji;Qi Gao;Fupeng Yin;Hengdong Guo
确定工程特性及其相应的实际实现水平是一个重要的QFD决策问题。随着实际工程问题的日益复杂，相应的QFD矩阵变得越来越胡格庞大，分析这些矩阵并做出决策所花费的时间将是不可接受的。本文提出了一种有效解决QFD决策问题的方法。将QFD决策问题转化为一个混合整数非线性规划（MINLP）模型，该模型的目标是在考虑企业能力、成本和资源约束的情况下，最大化总体顾客满意度。然后提出了一种改进的算法G-ICA，该算法是帝国主义竞争算法（伊卡）和遗传算法（GA）的结合。通过求解7个数值MINLP问题和4个不同尺度的自适应QFD决策问题，将G-ICA与其他成熟算法进行了比较。实验结果验证了G-ICA具有较好的全局寻优性能和时间性能。同时，该算法在保证决策精度和效率方面也有较好的性能。
It is an important QFD decision problem to determine the engineering characteristics and their corresponding actual fulfillment levels. With the increasing complexity of actual engineering problems, the corresponding QFD matrixes become much huger, and the time spent on analyzing these matrixes and making decisions will be unacceptable. In this paper, a solution for efficiently solving the QFD decision problem is proposed. The QFD decision problem is reformulated as a mixed integer nonlinear programming (MINLP) model, which aims to maximize overall customer satisfaction with the consideration of the enterprises’ capability, cost, and resource constraints. And then an improved algorithm G-ICA, a combination of Imperialist Competitive Algorithm (ICA) and genetic algorithm (GA), is proposed to tackle this model. The G-ICA is compared with other mature algorithms by solving 7 numerical MINLP problems and 4 adapted QFD decision problems with different scales. The results verify a satisfied global optimization performance and time performance of the G-ICA. Meanwhile, the proposed algorithm’s better capabilities to guarantee decision-making accuracy and efficiency are also proved.
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影响因子：
2.5
作者：
Alireza Soroudi;M. Ehsan
通讯作者：
Alireza Soroudi;M. Ehsan
影响因子：
9.2
作者：
H. Yamashina;Takaaki Ito;Hiroshi Kawada
通讯作者：
H. Yamashina;Takaaki Ito;Hiroshi Kawada
影响因子：
7.8
作者：
T. Park;Kwang-Jae Kim
通讯作者：
T. Park;Kwang-Jae Kim
DOI：
10.1109/sis.2014.7011793
发表时间：
2014-12
期刊：
2014 IEEE Symposium on Swarm Intelligence
影响因子：
--
作者：
A. Engelbrecht
通讯作者：
A. Engelbrecht
影响因子：
--
作者：
G. Wasserman
通讯作者：
G. Wasserman