Hybrid Optimization
Hybrid Optimization
Hybrid optimization is a set of multi-disciplinary approaches for solving decision and optimization problems that merge constraint reasoning, applied mathematics, computational logics, metaheuristics and statistics. The motivations for using hybrid optimization is that real problems present a very complex structure, with side constraints and uncertain data and are composed by distinct, yet tightly connected subproblems. Thus, a single approach is in general ineffective and hybrid solvers are more effective.
Specific topics of interest are
- Cost Based filtering
- Logic Based Benders Decomposition
- Hybrid tree search and local search methods
- Search and bounding methods
- Deriving information from sampling and diving
Related links
CPAIOR Conference Series: CPAIOR started as a Workshop in Ferrara in 1999 and became a conference in 2004. Now it is the major forum for researchers interested in combining operations research and artificial intelligence methods in Constraint Programming.