An improved ant colony algorithm with diversified solutions based on the immune strategy.
BACKGROUND
Ant colony algorithm has emerged recently as a new meta-heuristic method, which is inspired from the behaviours of real ants for solving NP-hard problems. However, the classical ant colony algorithm also has its defects of stagnation and premature. This paper aims at remedying these problems.


RESULTS
In this paper, we propose an adaptive ant colony algorithm that simulates the behaviour of biological immune system. The solutions of the problem are much more diversified than traditional ant colony algorithms.


CONCLUSIONS
The proposed method for improving the performance of traditional ant colony algorithm takes into account the polarization of the colonies, and adaptively adjusts the distribution of the solutions obtained by the ants. This makes the solutions more diverse so as to avoid the stagnation and premature phenomena.
Qin
Ling
L
Department of Computer Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 210096, China. qllynne@nuaa.edu.cn
Pan
Yi
Y
Chen
Ling
L
Chen
Yixin
Y
D000465:Algorithms; D000818:Animals; D000936:Antigen-Antibody Complex / Q000276:immunology*; D001000:Ants / Q000502:physiology*; D001522:Behavior, Animal / Q000502:physiology*; D032701:Biomimetics / Q000379:methods*; D018448:Models, Immunological; D012919:Social Behavior
D000936:Antigen-Antibody Complex