Adaptive temperature rising simulated annealing algorithm for traveling salesman problem

被引：0
|
作者：
Chen K.-S. [1 ]
Xian S.-D. [1 ]
Guo P. [1 ]
机构：
[1] Key Laboratory of Intelligent Analysis and Decision on Complex Systems, Chongqing University of Posts and Telecommunications, Chongqing
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2021年 / 38卷 / 02期
关键词：
Adaptive; Adaptive temperature rise simulated annealing algorithm; Travelling salesman problem (TSP); TSPLIB;
D O I：
10.7641/CTA.2020.00090
中图分类号：
学科分类号：
摘要：
In view of the situation that the traditional simulated annealing (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 traveling salesman problem (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. © 2021, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
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页码：245 / 254
页数：9
相关论文
共 15 条
  • [1] YANG Chunhua, TANG Xiaolin, ZHOU Xiaojun, Et al., A discrete state transition algorithm for traveling salesman problem, Control Theory & Applications, 30, 8, pp. 1040-1046, (2013)
  • [2] ZHOU Yongquan, HUANG Zhengxin, LIU Hongxia, Discrete glowworm swarm optimization algorithm for TSP problem, Acta Electronica Sinica, 40, 6, pp. 1164-1170, (2012)
  • [3] ZHANG Changsheng, SUN Jigui, OUYANG Dantong, A selfadaptive discrete particle swarm optimization algorithm, Acta Electronica Sinica, 37, 2, pp. 299-304, (2009)
  • [4] WU Qinghong, ZHANG Jihui, XU Xinhe, An ant colony algorithm with mutation features, Journal of Computer Research and Development, 36, 10, pp. 1240-1245, (1999)
  • [5] CUI Zhihua, ZHANG Maoqing, CHANG Yu, Et al., NSGA-II with average distance clustering, Acta Automatica Sinica, (2020)
  • [6] GENG Zhiqiang, QIU Dahong, HAN Yongming, Max-min ant system algorithm based on chaos and its application in TSP, Computer Engineering, 42, 3, pp. 192-197, (2016)
  • [7] ZHANG Yanxiang, QI Yuxian, An improved genetic simulated annealing algorithm to solve TSP, Intelligent Computer and Applications, 7, 3, pp. 52-54, (2017)
  • [8] HE Qing, WU Yile, XU Tongwei, Application of improved genetic simulated annealing algorithm in TSP optimization, Control and Decision, 33, 2, pp. 219-225, (2018)
  • [9] ZHANG Xinlong, CHEN Xiuwan, XIAO Han, Et al., A new imperialist competitive algorithm for solving TSP problem, Control and Decision, 31, 4, pp. 586-592, (2016)
  • [10] WU Husheng, ZHANG Fengming, LI Hao, Et al., Discrete wolf pack algorithm for traveling salesman problem, Control and Decision, 30, 10, pp. 1861-1867, (2015)
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