Experiments of a New Meta-Heuristics to Reduce the Search Space by Partially Fixing of the Solution Values
Kazumiti NUMATA, Yukinobu IWAKURA
In this paper we present a new meta-heuristic approach to the traveling Salesman problem (TSP) and evaluate its performance. Proposed method reproduces and selects a population of local optima searched by random start modified Lin-Kernighan (mLK) method. It enhances the search power and efficiency of mLK by fixing the part of the solution whose values coincide each other and thus reducing the search space of solutions. Results of numerical experiments on TSPLIB95 (500 or so cities instances) show that it is superiorly competitive to existing meta-heuristic methods for TSP. The reason of enhanced search power is also investigated through obserbation of its search processes.