An intelligent search strategy for solving the symmetric traveling salesman problem

An intelligent search strategy for solving the symmetric traveling salesman problem is proposed. The strategy captures two additional features over traditional heuristic search methods: sustained exploration and learning while searching. Sustained exploration refers to a strategy of not stopping when a local optimum is reached but continuing the search process until some prespecified criteria are satisfied. Learning while searching refers to the mechanism of learning, during sustained exploration, about characteristics of edges which are likely to be in an optimal tour. The authors focus on learning while searching, i.e., the ability to discover interesting characteristics of solutions generated by an unconstrained search strategy. The discovery of interesting characteristics of solutions can be used to improve system performance.<>