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An effective genetic algorithm approach to large scale mixed integer programming problems
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AI
DOI：10.1016/j.amc.2005.05.017.png)
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Abstract
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To effectively reduce the search space of GAs on large-scale M I P 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. (c) 2005 Elsevier Inc. All rights reserved.
Keywords:
mixed integer programming
genetic algorithm
variable grouping
structure property
search space
Journal
IF:
3.4
Papers:
2.3W
Citations:
3.3W
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