Optimization for bidirectional ant colony algorithm based on microcanonical annealing

ZHOU Hao-li; LI Tai-jun; XIAO Sha; XU Ning-min

doi:10.13873/J.1000-9787(2016)04-0127-03

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Optimization for bidirectional ant colony algorithm based on microcanonical annealing

更新时间：2025-09-11

- Optimization for bidirectional ant colony algorithm based on microcanonical annealing
- Vol. 35, Issue 4, Pages: 127-129+133(2016)
- 作者机构：
  
  1. 海南大学信息科学技术学院
  2. 海南省公安厅科技通信处
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2016)04-0127-03
  CLC： TP18
- Published：2016
- 稿件说明：
移动端阅览
[1]周浩理,李太君,肖沙,徐宁敏.微正则退火的双向蚁群优化算法[J].传感器与微系统,2016,35(04):127-129+133.

ZHOU Hao-li, LI Tai-jun, XIAO Sha, et al. Optimization for bidirectional ant colony algorithm based on microcanonical annealing[J]. 2016, 35(4): 127-129+133.
[1]周浩理,李太君,肖沙,徐宁敏.微正则退火的双向蚁群优化算法[J].传感器与微系统,2016,35(04):127-129+133. DOI： 10.13873/J.1000-9787(2016)04-0127-03.

ZHOU Hao-li, LI Tai-jun, XIAO Sha, et al. Optimization for bidirectional ant colony algorithm based on microcanonical annealing[J]. 2016, 35(4): 127-129+133. DOI： 10.13873/J.1000-9787(2016)04-0127-03.

摘要

双向蚁群搜索算法可以提高算法的搜索速度

并可以选择搜索的空间;微正则退火算法具有准确度高、速度快等优点

可以实现全局路径优化搜索。结合两种算法的优点

提出了双向蚁群微正则退火算法

用来求解海量数据网络下的旅行商问题。通过实验表明:双向蚁群微正则退火算法不容易陷入局部最优解

且在寻找全局最优解和运行效率上都比其他算法更有优势。

Abstract

Bidirectional ant colony search algorithm can improve search speed and select search space;microcanonical annealing algorithm can realize global path optimization search with high speed and accuracy.Combine advantages of the two algorithms and propose bi-ACO microcanonical annealing algorithm for solving traveling salesman problem in massive data network. Experiments show that bi-ACO microcanonical annealing algorithm is not easy to fall into local optimal solution

and it has more advantages in searching for globally optimal solution and operating efficiency than other algorithms.

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references

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基于蚁群粒子群融合的机器人路径规划算法 [J]. 王宪,王伟,宋书林,平雪良,彭力. 计算机系统应用 . 2011(09)

模拟退火与蚁群混合并行算法解旅行商问题 [J]. 许智宏,宋勃,郭艳艳. 河北工业大学学报 . 2010(02)

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一种求解连续空间优化问题的改进蚁群算法 [J]. 段海滨,马冠军,王道波,于秀芬. 系统仿真学报 . 2007(05)

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Mobile Agent Routing Algorithm Based on Improved Ant Colony Algorithm [J] . Yueshun, He,Ping, Du. Sensors & Transducers . 2013 (6)

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