کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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4637328 | 1340739 | 2006 | 13 صفحه PDF | دانلود رایگان |
To effectively reduce the search space of GAs on large-scale MIP problems, this paper proposed a new variable grouping method based on structure properties of a problem. Taking the capacity expansion and technology selection problem as a typical example, this method groups problem’s decision variables over time period and machine line. Based on this new variable grouping method, we developed a variable-grouping based genetic algorithm according to problem’s structure properties (VGGA-S). We tested the performance of VGGA-S by applying it on the capacity expansion and technology selection problem. Numerical experiments suggested that, VGGA-S outperforms the standard GA and variable-grouping based GAs without considering problem’s structure properties, both on computation time and solution quality. Although VGGA-S is proposed based on structure properties of a specific MIP problem, it is a general optimization algorithm and theoretically applicable to other large scale MIP problems.
Journal: Applied Mathematics and Computation - Volume 174, Issue 2, 15 March 2006, Pages 897–909