一种基于免疫蚁群混合算法的TSP求解模型

刘勇; 刘念; 刘孙俊

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一种基于免疫蚁群混合算法的TSP求解模型

信息工程 | 更新时间：2023-11-14

- 一种基于免疫蚁群混合算法的TSP求解模型
- A TSP Solving Model Based on Immune Ant Colony Hybrid Algorithm
- 工程科学与技术 2010年42卷第3期页码：121-126
- 作者机构：
  
  1. 中国科学院成都计算机应用研究所
  2. 中国空气动力研究与发展中心
  3. 四川大学电气信息学院
  4. 成都信息工程学院软件工程学院
- 作者简介：
- 基金信息：
  
  国家863计划基金资助项目(2008AAO1Z402);四川省技术创新基金资助项目（2008PT013）
- DOI：
  中图分类号：
- 纸质出版日期：2010，
  
  网络出版日期：2010-1-13，
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刘勇,刘念,刘孙俊.一种基于免疫蚁群混合算法的TSP求解模型[J].工程科学与技术,2010,42(3):121-126.

Liu Yong, Liu Nian, Liu Sunjun. A TSP Solving Model Based on Immune Ant Colony Hybrid Algorithm[J]. Advanced Engineering Sciences, 2010,42(3):121-126.
刘勇,刘念,刘孙俊.一种基于免疫蚁群混合算法的TSP求解模型[J].工程科学与技术,2010,42(3):121-126. DOI：

Liu Yong, Liu Nian, Liu Sunjun. A TSP Solving Model Based on Immune Ant Colony Hybrid Algorithm[J]. Advanced Engineering Sciences, 2010,42(3):121-126. DOI：

摘要

中文摘要: 为了解决传统蚁群算法搜索速度慢、容易出现早熟、停滞的缺点，以及传统免疫算法由于反馈信息利用不足存在大量无为的冗余迭代导致求解效率低的缺点，提出了一种蚁群与免疫克隆相结合的混合算法，该算法在前期采用免疫算法来产生蚁群算法的初始信息分布，在后期根据路径浓度抑制机制调整路径上的信息量，从而保持了蚁群多样性，并将该算法用于求解旅行商问题进行计算机仿真，从实验结果可以看出，该算法具有针对性的改进，是一种收敛速度和寻优能力都较好的优化方法。

Abstract

Abstract:In order to solve the problems caused by the traditional ant colony algorithm

such as searching slowly

cling to prematurity

standstill

and the inefficient and useless redundant iteration caused by feedback underutilized in traditional immune algorithm，an algorithm combined ant colony algorithm and Immune Clone hybrid algorithm was proposed. With this algorithm

initial information distribution of ant colony algorithm was generated by immune algorithm in the early work

and the information on the path was adjusted by path concentration of inhibition mechanism at latter work

which kept diversity of ant colony. The algorithm was used to solve the traveling salesman problem for computer simulation.The experimental results showed that the algorithm has targeted improvements

good convergence speed and optimization capabilities.

关键词

人工免疫蚁群算法旅行商问题

Keywords

artificial immunityant colony algorithmTSP

references

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Dorigo M,蚁群优化,北京:清华大学出版社,2006.

Nitesh Khilwani ；Anoop Prakash ；Ravi Shankar ；M. K. Tiwari,Fast clonal algorithm,Engineering Applications of Artificial Intelligence ,2008, 21(1).

M.H. Afshar,Partially constrained ant colony optimization algorithm for the solution of constrained optimization problems: Application to storm water network design,Advances in Water Resources ,2007, 30(4).

Adean Acan,An external partial permutation memory for ant colony optimization,Berlin Heidelberg; Springer Verlag,2005.

Bonabeau E,Swarm intelligence:From natural to artificial systems,Oxford:Oxford University Press,1999.

Jiejin Cai ；Xiaoqian Ma ；Lixiang Li ；Yixian Yang ；Haipeng Peng ；Xiangdong Wang,Chaotic ant swarm optimization to economic dispatch,Electric Power Systems Research ,2007, 77(10).

Min Kong ；Peng Tian ；Yucheng Kao,A new ant colony optimization algorithm for the multidimensional Knapsack problem,Computers & Operations Research ,2008, 35(8).

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