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Combining (integer) linear programming techniques and metaheuristics for combinatorial optimization. (English) Zbl 1415.90054

Blum, Christian (ed.) et al., Hybrid metaheuristics. An emerging approach to optimization. Berlin: Springer. Stud. Comput. Intell. 114, 31-62 (2008).
Summary: Several different ways exist for approaching hard optimization problems. Mathematical programming techniques, including (integer) linear programming based methods, and metaheuristic approaches are two highly successful streams for combinatorial problems. These two have been established by different communities more or less in isolation from each other. Only over the last years a larger number of researchers recognized the advantages and huge potentials of building hybrids of mathematical programming methods and metaheuristics. In fact, many problems can be practically solved much better by exploiting synergies between these different approaches than by “pure” traditional algorithms. The crucial issue is how mathematical programming methods and metaheuristics should be combined for achieving those benefits. Many approaches have been proposed in the last few years. After giving a brief introduction to the basics of integer linear programming, this chapter surveys existing techniques for such combinations and classifies them into ten methodological categories.
For the entire collection see [Zbl 1143.90001].

MSC:

90C05 Linear programming
90C59 Approximation methods and heuristics in mathematical programming
90C25 Convex programming
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