Chaotic Immune Genetic Hybrid Algorithms and Its Application

Weijian Ren, Chaohai Kang, Yingying Li, Liying Gong


To solve the shortage in genetic algorithms, such as slow convergence speed, poor local searching capability and easy prematurity, firstly,the immune memory recognition function was introduced, to speed up the searching speed and improve the overall searching capabilities of genetic algorithm. Secondly,the Hénon chaotic map was introduced into the generation of the initial population, made the generated initial population uniformly distributed in the solution space, to reduce data redundancy, increase the diversity of antibody population and the search range of initial population manipulation , prevent the defect of falling into local optimum. Finally, Logistic map was introduced into manipulation of crossover and mutation, meanwhile the map was used to produce the chaotic disturbance strategy on the memory and populations antibodies , to improve the quality of optimal solution and the searching speed of the algorithm, increase efficient of searching. It was proved that the above hybrid algorithm is convergence by mathematics method. The results of function optimization show that the above hybrid algorithm is valid and has better performance than other algorithms.



Full Text:



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License