A Variable Neighborhood Immune Algorithm for Solving Complex Function Optimization Problems

In this paper, based on the immune network theory, a novel variable neighborhood immune algorithm is proposed for complex function optimization. In the algorithm, a two-level immune network mechanism is suggested to keep the large diversity of populations during the process of population evolutionary, and a variable neighborhood strategy is introduced to overcome the conflict of local search and global search. The algorithm is verified by several complex benchmark functions, and the experimental results demonstrate the algorithm is very effective