| 引用本文: | 陈科胜,鲜思东,郭鹏.求解旅行商问题的自适应升温模拟退火算法[J].控制理论与应用,2021,38(2):245~254.[点击复制] |
| CHEN Ke-sheng,XIAN Si-dong,GUO Peng.Adaptive temperature rising simulated annealing algorithm for Traveling Salesman Problem[J].Control Theory and Technology,2021,38(2):245~254.[点击复制] |
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| 求解旅行商问题的自适应升温模拟退火算法 |
| Adaptive temperature rising simulated annealing algorithm for Traveling Salesman Problem |
| 摘要点击 2584 全文点击 792 投稿时间：2020-02-18 修订日期：2020-09-14 |
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| DOI编号 10.7641/CTA.2020.00090 |
| 2021,38(2):245-254 |
| 中文关键词 自适应升温模拟退火算法 旅行商问题(TSP) TSPLIB 自适应 |
| 英文关键词 Adaptive temperature rise simulated annealing algorithm Travelling salesman problem（TSP） TSPLIB adaptive |
| 基金项目 重庆市教委研究生教学改革研究项目(YJG183074), 重庆市社会科学规划项目(2018YBSH085), 重庆邮电大学大学生科研训练项目(A2019–25, R2019–85). |
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| 中文摘要 |
| 针对传统模拟退火算法在求解问题时容易陷入局部最优解的情况, 本文通过设计一种自适应的升温控制
因子, 提出了一种求解旅行商问题(TSP)的自适应升温模拟退火算法, 有效地控制局部寻优达到全局寻优能力, 并证
明了改进的自适应模拟退火算法收敛性. 通过TSPLIB数据库对改进算法全局寻优效果的测试, 结果表明改进后的
算法具有全局寻优能力、泛化性强等特点: 即在TSPLIB提供的绝大部分TSP问题数据中, 均能找到全局最优解, 且
收敛速度快. |
| 英文摘要 |
| In view of the situation that the traditional SA algorithm is easy to fall into the local optimal solution when solving the problem, this paper designs an adaptive temperature rise control factor, and proposes an adaptive temperature rise SA algorithm for solving TSP problem, which effectively controls the local optimization to achieve the global optimization ability, and proves the convergence of the improved adaptive SA algorithm. Through the test of TSPLIB database on the global optimization effect of the improved algorithm, the results show that the improved algorithm has the characteristics of global optimization ability and strong generalization: that is, in most of the TSP problem data provided by TSPLIB, the global optimal solution can be found, and the convergence speed is fast. |
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