Imitating the antibody diversity maintaining mechanism of immune system to realize the global optimization is a target that the immune algorithm try to achieve. Based on the in-depth study of inhibition concentration mechanism, the global optimization characteristic of existing immune algorithm is analyzed, then a global conservation strategy for colony is proposed. Based on the strategy, the improved algorithm is of more outstanding global and fast convergence ability. Simulation is implemented based on Matlab, the algorithm is applied to train a neural network prediction model and it is compared with an existing typical immune algorithm. Simulation results show that the immune algorithm improved by the strategy in this paper is better than the previous algorithms in global evolution, fast convergence and other key indicators.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Improved immune algorithm based on a global strategy


    Contributors:


    Publication date :

    2014-08-01


    Size :

    105312 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





    Autopilot Strategy Based on Improved DDPG Algorithm

    Zuo, Xiaochao / Li, Xiaoning / Tian, Zhewen | SAE Technical Papers | 2019



    Application of improved artificial immune network algorithm to optimization

    Yunfeng Zhao, / Yixin Yin, / Dongmei Fu, et al. | IEEE | 2008