کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
395084 | 665928 | 2008 | 21 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Integrated multiobjective optimization and a priori preferences using genetic algorithms Integrated multiobjective optimization and a priori preferences using genetic algorithms](/preview/png/395084.png)
One of the tasks of decision-making support systems is to develop methods that help the designer select a solution among a set of actions, e.g. by constructing a function expressing his/her preferences over a set of potential solutions. In this paper, a new method to solve multiobjective optimization (MOO) problems is developed in which the user’s information about his/her preferences is taken into account within the search process. Preference functions are built that reflect the decision-maker’s (DM) interests and use meaningful parameters for each objective. The preference functions convert these objective preferences into numbers. Next, a single objective is automatically built and no weight selection is performed. Problems found due to the multimodality nature of a generated single cost index are managed with Genetic Algorithms (GAs). Three examples are given to illustrate the effectiveness of the method.
Journal: Information Sciences - Volume 178, Issue 4, 15 February 2008, Pages 931–951