Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Local search with constraint propagation and conflict-based heuristics
Artificial Intelligence
EasyLocal++: an object-oriented framework for the flexible design of local-search algorithms
Software—Practice & Experience
Constraint-Based Local Search
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
MiniZinc: towards a standard CP modelling language
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A 25-year perspective on logic programming
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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.