解の部分固定により探索空間を縮小するメタ戦略の検討
巡回セールスマン問題への適用
2004 年 17 巻 3 号 p. 103-112
詳細
2004 年 17 巻 3 号 p. 103-112
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.
