Hybrid algorithm of immune algorithm，genetic algorithm and ant colony system

doi:10.3778/j.issn.1002-8331.2010.23.012

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (23): 44-46.DOI: 10.3778/j.issn.1002-8331.2010.23.012

• 研究、探讨 • Previous Articles Next Articles

Hybrid algorithm of immune algorithm，genetic algorithm and ant colony system

GENG Qiang¹，WANG Cheng-liang²

1.College of Computer Science，Chongqing University，Chongqing 400044，China
2.College of Software Engineering，Chongqing University，Chongqing 400044，China

Received:2009-02-24 Revised:2009-04-13 Online:2010-08-11 Published:2010-08-11
Contact: GENG Qiang

免疫遗传蚁群融合算法

耿强¹，王成良²

1.重庆大学计算机学院，重庆 400044
2.重庆大学软件学院，重庆 400044

通讯作者: 耿强

Abstract

Abstract: A new hybrid algorithm combined with ant colony system，immune algorithm and genetic algorithm is presented.To introduce the immune algorithm and genetic algorithm into the process of ant colony iterations and to take the advantages of the local optimization of immune algorithm and the global search of genetic algorithm can speed up the convergence of the ant colony system.This hybrid algorithm can effectively overcome the shortcomings of ant colony system which easily “trap into” local optimal solution or degradation by way of selecting，intersecting and mutating of genetic algorithm along with the self-adaptation immune vaccination of immune algorithm.The simulation test for solving travelling salesman problem has shown that this new hybrid algorithm is very excellent in convergence and optimal solution for global search.

Key words: ant colony system, immune algorithm, Genetic Algorithm, immune vaccination, Traveling Salesman Problem

摘要： 提出了一种融合蚁群系统、免疫算法和遗传算法的混合算法。将免疫算法和遗传算法引入到每次蚁群迭代的过程中，利用免疫算法的局部优化能力和遗传算法的全局搜索能力，来提高蚁群系统的收敛速度。该算法通过遗传算法的选择、交叉、变异操作和免疫算法的自适应疫苗接种操作，有效地解决了蚁群系统的易陷入局部最优和易退化的缺点。通过对旅行商问题的仿真实验表明该算法具有非常好的收敛速度和全局最优解的搜索能力。

关键词: 蚁群系统, 免疫算法, 遗传算法, 疫苗接种, 旅行商问题

CLC Number:

TP181

GENG Qiang¹，WANG Cheng-liang². Hybrid algorithm of immune algorithm，genetic algorithm and ant colony system[J]. Computer Engineering and Applications, 2010, 46(23): 44-46.

耿强¹，王成良². 免疫遗传蚁群融合算法[J]. 计算机工程与应用, 2010, 46(23): 44-46.

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Hybrid algorithm of immune algorithm，genetic algorithm and ant colony system

免疫遗传蚁群融合算法

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