Title :
Ant Colony Optimization Algorithm Based on Immune Strategy
Author :
Zheng, Xiaoxia ; Fu, Yang
Author_Institution :
Shanghai Univ. of Electr. Power, Shanghai, China
Abstract :
Ant Colony Optimization (ACO) is inspired by the ability of ant colonies to find shortest paths between their nest and a food source. The paper proposed a modified ACO based on artificial immune strategy. The mechanism of the vaccination, antibody diversity and clonal deletion theory in artificial immune system are introduced to improve the ways of artificial ants search solution space and the elite ant´s capability. Also it can solve the conflict between the diversity of the solutions searched by ant colony and the convergence speed. The simulation results by examples of traveling salesman problem(TSP) show that adding immune strategy to ants group can find better solution in shorter time than ACO.
Keywords :
artificial immune systems; convergence; search problems; ACO; ant colony optimization algorithm; ant search solution space; antibody diversity; artificial immune strategy; clonal deletion theory; convergence speed; food source; traveling salesman problem; vaccination; Ant colony optimization; Cities and towns; Immune system; Optimization; Search problems; Traveling salesman problems; Writing; ant colony optimization; antibody; artificial immune; traveling salesman problem; vaccination;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2011 Fourth International Symposium on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4577-1085-8
DOI :
10.1109/ISCID.2011.171
