Describes a parallel search-and-learn technique for obtaining high quality solutions to the traveling salesperson problem (TSP). The combinatorial search space is decomposed so that multiple processors can simultaneously look for local optimal solutions in the subspaces. The local optima are then compared to learn which moves are good-a move is defined to be good if all the search processes have voted in consensus for the move. Based on this learning, the original problem is transformed into a constrained optimization; a constraint requires a specific edge to be included in the final tour. The constrained optimization problem is modeled as a TSP of smaller size, and is again solved using the parallel search technique. This process is repeated until a TSP of manageable size is reached, which can be solved effectively; the tour obtained at this last stage is then expanded retrogressively until the tour for the original problem is obtained. The results of parallel implementation on a 32-node transputer are described.
['C. P. Ravikumar']
A parallel search-and-learn technique for solving large scale TSP