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智能优化算法求解TSP问题 被引量：119

Reviews of the Meta-heuristic Algorithms for TSP
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摘要 TSP(旅行商)问题代表组合优化问题,具有很强的工程背景和实际应用价值,但至今尚未找到非常有效的求解方法.为此,讨论了最近研究比较热门的使用各种智能优化算法(蚁群算法、遗传算法、模拟退火算法、禁忌搜索算法、Hopfield神经网络、粒子群优化算法、免疫算法等)求解TSP问题的研究进展,指出了各种方法的优缺点和改进策略.最后总结并提出了智能优化算法求解TSP问题的未来研究方向和建议. Traveling salesman problem（TSP） is the representation of a kind of combination optimization problems, possessing a strong engineering background and practical application value. However, there is no effective corresponding solution to it. Aim at that, the research and application of the most popular recta-heuristic methods such as ant colony algorithm, genetic algorithm, simulated annealing, tabu search, hopfield neural network, particle swarm optimization and immune algorithm, etc. are reviewed. The advantages and disadvantages of each method and the improvement strategies are discussed. The future research direction and suggestion are also given.
作者 高海昌 冯博琴 朱利b
机构地区 西安交通大学电子与信息工程学院 西安交通大学软件学院
出处 《控制与决策》 EI CSCD 北大核心 2006年第3期241-247,252,共8页 Control and Decision
基金 国家863高技术研究发展计划基金项目(2003AA1Z2610)
关键词 旅行商问题 蚁群算法 遗传算法 模拟退火算法 禁忌搜索算法 粒子群优化算法 TSP Ant colony algorithm Genetic algorithm Simulated annealing Tabu search Particle swarm optimization
分类号 TP301 [自动化与计算机技术—计算机系统结构]
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参考文献51

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二级参考文献47

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共引文献1134

同被引文献899

引证文献119

二级引证文献854

控制与决策
2006年 第3期

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