Application of improved artificial immune network algorithm to optimization

Tabu search artificial immune algorithm (TS-aiNet) is proposed based on aiNet inspired by mechanism of tabu search algorithm. It introduced a tabu list that taboos cells whose affinities didnpsilat increase no longer in the network. In some phrase the tabooed excellent cells were released according to aspiration criteria. It added a memory table applied to save mature memory cells. Moreover it improved the expression of Gauss mutation for diversity search in the process of global optimization. Markov chain was applied to prove global convergence. Convergence analysis was based on random simulation of some typical systems and compared with that of CLONALG and aiNet algorithms. The simulation results show that the presented approach has preferable global convergent ability and stability in multi-modal search space, and can avoid prematurity effectively. So it is demonstrated a global optimized algorithm with feasible and high efficiency.