Hi Christian, > Sorry for the delay but I did not really know what to answer ;-)
we also took some time, because we do not know (exactly) HOW to ask :-) Therefore we decided to slightly tidy up our current version of the code and put it in a bitbucket repository. You can find it at https://bitbucket.org/tunnuz/gecode-lns together with a small adaptation of the TSP example for LNS full of workarounds (including a patch to gecode/search.hh for granting friendship of BaseEngine to LNS as it was done with RBS). Consider that some of the code is taken verbatim from the RBS engine (sometimes comments included), and adapted for LNS. > So, maybe you could summarize: > - what does the engine do The engine is performing a Large Neighborhood Search, i.e., starting from a complete initial solution obtained by a "start_engine"(1), it will iteratively explore the neighborhood obtained by relaxing some of the variables of the current solution and using an engine to find a better new solution with only those variables changed. The strategy for relaxing the variables should be problem-specific (it will be implemented in a required addition to Space) and also the branching strategy for exploring that neighborhood (idem). The abstract pseudo-code of LNS (1) is the following [it is really pseudo-code, all the technicalities for dealing with cloning, pointers and so on is taken care in the actual code]: AbstractLNS() { current->postBranchingForInitialSolution(); current = start_engine->next(); // find initial solution best = current; intensity = initialIntensity; while (!StopCondition) { neighbor = current->cloneAndRelax(intensity); neighbor->constrain(current, specificConstrainStrategy); // constrain neighborhoods to have a solution cost according to a specific constrain strategy (e.g., strictly better than current, or based on Simulated Annealing) neighborhood_engine->reset(neighbor); // let the neighborhood_engine start from the relaxed solution nextSolution = neighborhood_engine->next(); if (nextSolution->improving(best)) { best = nextSolution; current = nextSolution; intensity = initialIntensity; idle_iterations = 0; } else if (nextSolution->improving(current) || constrainStrategy == SimulatedAnnealing) { current = nextSolution; intensity = initialIntensity; idle_iterations = 0; } else idle_iterations++; if (idle_iterations > max_iterations_per_intensity) intensity = increaseIntensity; } return best; } > - what does it require from a space (you already mentioned two additional > member functions) Actually they grow up, although some of them have a tentative definition. The augmented interface of Space for being used in LNS is currently defined in the LNSModel class (in hybrid_model.hh). There is another quite impacting requirement for a space to be used in the LNS engine: the post of branchers MUST be completely taken away from model posting (i.e., it should not be performed in the Space constructor) because LNS requires a fresh root model (free of branching) for cloning(3), since in the LNS overall strategy the branching for searching the initial solution is in principle different from the branching for neighborhood exploration. Since we do not have access to any method for removing branchers once they are posted we are forced to impose that branching must be posted through some functions. > - what other parameters are needed to control the engine Actually there are a bunch of parameters, you can find in LNSOptions class, namely: the range of variation of the intensity parameter (more or less related to the number of variables to be relaxed), the number of non improving iterations before intensity must be increased, the strategy for constraining the cost value in neighbors solutions (none, strictly or loosely improving over current, simulated annealing), for simulated annealing there are a few other specific parameters related to the temperature, cooling rate and the number of neighbors accepted before decreasing the temperature. We will be happy if you can give a look at the engine and provide us with suggestion for integrating it. Consider that we also aim at contributing other hybrid metaheuristics that will blend with CP by driving the search (at least an ACO implementation and possibly some genetic algorithms), therefore these issues are quite general from this point of view. Thanks and have a nice weekend (considered the organization issues of the CP conference :-) Luca and Tommaso ---- Notes: (1) the start engine, is actually needed because it will be not possible to change the options object (more precisely the stop object) passed to an engine after it has been created. Namely, we need two different stop objects for the first solution (which is the "global" meta-engine stop object) and for the neighbors (which is a timestop object). (2) the actual LNS code, tough, is implemented differently in the engine because we wanted to comply with the "next()" semantics. (3) we must confess that we are not completely happy of this solution, but it was the one with the least impact in the "jungle" _______________________________________________ Gecode users mailing list users@gecode.org https://www.gecode.org/mailman/listinfo/gecode-users
