An immune genetic algorithm based on immune regulation

Using immune regulation mechanisms that include density regulation and network regulation, this paper proposes a novel immune genetic algorithm. Its core idea is that all individuals compose an antibody network, and it utilizes a density regulation mechanism to adjust individual diversity at an individual level and network regulation mechanism to achieve dynamic balance between individual diversity and population convergence. The dynamic regulative ability of this algorithm is analyzed and the approach to choosing the parameters is also given. As a novel adaptive resolving algorithm, it can be used to solve many complex optimization problems. This paper discusses solution of the frequency assignment problem and analyzes the parameters´ influence upon the performance of this algorithm. The experimental results prove that this algorithm has good performance and can properly maintain the balance between individual diversity and population convergence