Abstract :
Ant colony optimization (ACO) is a new natural computation method from mimic the behaviors of ant colony.It is a very good combination optimization method. To extend the ant colony optimization, some continuous ant colony optimizations have been proposed. To improve the searching performance, the principles of evolutionary algorithm and artificial immune algorithm have been combined with the typical continuous ant colony optimization, and one new immunized ant colony optimization is proposed here. In this new algorithm, the ant individual is transformed by adaptive Cauchi mutation and thickness selection. To verify the new algorithm, the typical functions, such as Schaffer function and "needle-in-a-haystack" function, are all used. And then, the results of immunized ant colony optimization are compared with that of continuous ant colony optimization. The results show that, the convergent speed and computing precision of new algorithm are all very good.
Keywords :
evolutionary computation; optimisation; adaptive Cauchi mutation; artificial immune algorithm; combination optimization; continuous ant colony optimizations; evolutionary algorithm; immunized ant colony optimization; natural computation; Ant colony optimization; Biochemistry; Distributed computing; Evolutionary computation; Feedback; Flowcharts; Genetic mutations; Immune system; Optimization methods; Roads;
