IM-ASFA based on self-adaptive mechanism on bioinformatics

The advantages of artificial fish swarm algorithm lie in less accuracy of objective function, initial value and parameter selection, thus getting a wide application in the field of swarm intelligence optimization. However, the algorithm has disadvantages of poor balance between exploration and development, blindness to searching in the last runs, low accuracy of optimization results and low operation speed, decreasing its searching quality and efficiency. So, by introducing the theory of biological immune system and utilizing the avidity between antibody and antigen and antibody diversity embodied by antibody concentration, this paper puts forward immune memory artificial fish swarm algorithm based on self-adaptive mechanism improving artificial fish swarm algorithm through combining with immune memory. At last, a simulation experiment is conducted to solve the minimum value of three groups of different test functions. The result of the experiment shows that improved algorithm has obvious advantages in optimization. Copyright © 2014 American Scientific Publishers.