基于求解旅行商问题( TSP) ，提出了一种多策略离散型和声搜索算法． 文中通过引入-opt 算法设计了一种离散型即兴创作过程，并结合3 种策略来提高全局寻优能力:采取教学优化策略给出了产生和声的新方式，以改善和声记忆库的质量; 采用精英扰动策略探索最优和声的邻域进行精细搜索，以提高算法的收敛精度; 通过排序选择更新策略保持和声记忆库的多样性，避免算法早熟收敛． 实验结果分析表明，该算法能够有效求解TSP，具有可靠的全局收敛性和较快的收敛速度．

Proposed in this paper is a multi-strategy discrete harmony search ( MDHS) algorithm for solving traveling salesman problem ( TSP) .In the algorithm,a discrete improvisation is designed by combining a -opt algorithm,and three strategies are employed together to improve its global optimization ability.The teaching-learningbased optimization strategy is adopted to provide a new way of producing new harmonies,so as to improve the quality of harmony memory ( HM) .The elite perturbation strategy is constructed to explore the neighborhoods of the best harmony constantly to perform a fine local search,so that the convergence precision of the proposed algorithm can be increased.The sort-selection-based update strategy is designed to preserve the diversity of HM for the sake of avoiding premature convergence.Experimental results show that MDHS can solve TSP effectively,and it holds an excellent search performance no matter in convergence speed or precision.