Abstract：A twostage tabu search algorithm based on rough set theory is proposed for combinatorial optimization problems which are represented by TSP. For most of the adaptive tabu search algorithms, the balance between intensification and diversification is achieved by tuning tabu search parameters dynamically. Unlike them, a twostage search strategy is used in the proposed approach. The aim of the first stage is diversification. In this stage, the search area is stimulated to move away from the initial solution, and the whole solution space is explored to a certain degree. Then, based on the solutions obtained in diversification, a promising area decision table is constructed and the corresponding promising area is found. The goal of the second stage is intensification. In this stage, the search procedure begins with the best solution which contains the promising area. In the search procedure, the selection of the new current solution is limited so as to utilize the useful information obtained in the first stage. The proposed algorithm is tested by TSP benchmark problems. The results show that it is feasible and effective.
李凡，刘启和，杨国纬. 一种基于Rough集理论的两阶段禁忌搜索算法*[J]. 模式识别与人工智能, 2007, 20(4): 478-484.
LI Fan, LIU QiHe, YANG GuoWei. A TwoStage Tabu Search Algorithm Based on Rough Set Theory. , 2007, 20(4): 478-484.
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