DocumentCode
3373094
Title
Diversity Guaranteed Ant Colony Algorithm Based on Immune Strategy
Author
Qin, Ling ; Chen, Yixin ; Luo, Jianli ; Chen, Ling ; Guo, Jing
Author_Institution
Dept. of Comput. Sci., Nanjing Univ. of Aeronaut. & Astronaut.
Volume
2
fYear
2006
fDate
20-24 June 2006
Firstpage
217
Lastpage
223
Abstract
A diversity guaranteed ant colony algorithm is presented by simulating the behavior of biological immune system. The algorithm adopts the immunogenic methods immune selection, immune memory, immune metabolism, density control and isolation niche technique. In each iteration of the algorithm, the solutions of the ants are selected to have crossover and mutation operations according to their quality and the distribution of the solutions. The mutation probability is determined by the diversity of the solutions. Experimental results on the traveling salesman problem show that our algorithm can obtain high quality of solutions, high convergence speed. It can avoid the stagnation and premature phenomena and has strong capability of optimization
Keywords
artificial life; convergence; optimisation; biological immune system; convergence speed; density control; diversity guaranteed ant colony algorithm; immune memory; immune metabolism; immune selection; immunogenic methods; isolation niche technique; mutation probability; premature phenomena; traveling salesman problem; Ant colony optimization; Biological system modeling; Computational modeling; Computer science; Educational institutions; Engineering management; Genetic mutations; Immune system; Learning systems; Traveling salesman problems;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Computational Sciences, 2006. IMSCCS '06. First International Multi-Symposiums on
Conference_Location
Hanzhou, Zhejiang
Print_ISBN
0-7695-2581-4
Type
conf
DOI
10.1109/IMSCCS.2006.214
Filename
4673705
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