Abstract
We present a hybrid solver (called \(\mathbb{GELATO}\)) that exploits the potentiality of a Constraint Programming (CP) environment (Gecode) and of a Local Search (LS) framework (EasyLocal + + ). \(\mathbb{GELATO}\) allows to easily develop and use hybrid meta-heuristic combining CP and LS phases (in particular Large Neighborhood Search). We tested some hybrid algorithms on different instances of the Asymmetric Traveling Salesman Problem: even if only naive LS strategies have been used, our meta-heuristics improve the standard CP search, in terms of both goodness of the solution reached and execution time. \(\mathbb{GELATO}\) will be integrated into a more general tool to solve Constraint Satisfaction/Optimization Problems. Moreover, it can be seen as a new library for approximate and efficient searching in Gecode.
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Cipriano, R., Di Gaspero, L., Dovier, A. (2009). A Hybrid Solver for Large Neighborhood Search: Mixing Gecode and EasyLocal + + . In: Blesa, M.J., Blum, C., Di Gaspero, L., Roli, A., Sampels, M., Schaerf, A. (eds) Hybrid Metaheuristics. HM 2009. Lecture Notes in Computer Science, vol 5818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04918-7_11
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DOI: https://doi.org/10.1007/978-3-642-04918-7_11
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