Title :
New Computational Model from Ant Colony
Author_Institution :
Wuhan Polytech. Univ., Wuhan
Abstract :
The computational model from life system has become a main intelligent algorithm. Ant colony algorithm is a new computational model from mimic the swarm intelligence of ant colony behavior. And it is a very good combination optimization method. To extend the ant colony algorithm, some continuous ant colony algorithms have been proposed. To improve the searching performance, the principles of evolutionary algorithm and immune system have been combined with the typical continuous ant colony algorithm, and one new computational model is proposed here. In this new model, the ant individual is transformed by adaptive Cauchi mutation and thickness selection. To verify the new computational model, the typical functions, such as Schaffer function is used. And then, the results of new algorithm are compared with that of ant colony algorithm and immunized evolutionary programming which is proposed by author. The results show that, the convergent speed and computing precision of new algorithm are all very good.
Keywords :
artificial immune systems; artificial life; convergence of numerical methods; evolutionary computation; adaptive Cauchi mutation; combination optimization; computational model; continuous ant colony algorithms; evolutionary algorithm; immune system; immunized evolutionary programming; intelligent algorithm; life system; swarm intelligence; Ant colony optimization; Biochemistry; Computational intelligence; Computational modeling; Evolutionary computation; Genetic mutations; Genetic programming; Immune system; Optimization methods; Particle swarm optimization;
Conference_Titel :
Granular Computing, 2007. GRC 2007. IEEE International Conference on
Conference_Location :
Fremont, CA
Print_ISBN :
978-0-7695-3032-1
DOI :
10.1109/GrC.2007.26