title: An improved ant colony algorithm with diversified solutions based on the immune strategy.
abstract: 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.
authors.affiliation: Department of Computer Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 210096, China. qllynne@nuaa.edu.cn
authors.forename: Ling
authors.initials: L
authors.lastname: Qin
authors.forename: Yi
authors.initials: Y
authors.lastname: Pan
authors.forename: Ling
authors.initials: L
authors.lastname: Chen
authors.forename: Yixin
authors.initials: Y
authors.lastname: Chen
mesh_terms: 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
chemical_list: D000936:Antigen-Antibody Complex