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 didnt 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.
Artificial immune Optimization Artificial immune network algorithm Tabu search algorithm
Yunfeng Zhao Yixin Yin Dongmei Fu Zhun Zhou Ping Yin Jia Wang
School of Information Engineering University of Science and Technology Beijing Beijing,100083 China Institute of Economy and Information China Coal Research Institute Beijing,100013 China
国际会议
深圳
英文
619-624
2008-12-10（万方平台首次上网日期，不代表论文的发表时间）