Negative selection based immune optimization

摘要

An immune optimization algorithm is proposed in this paper based on the immune negative selection. The algorithm NSIOA is motivated by the negative selection mechanism in biological immune recognition. Different from the existing immune optimization methods, NSIOA constantly removes the worst solutions to get the optimal solution. Considering that removal of poor members of a population might lead to the loss of design information that may actually help identify better solutions in the search space, the proposed NSIOA is designed to keep the diversity of antibodies while removing poor members, therefore the algorithm will converge to global optimal solution with high probability. The convergence property and the complexity of the algorithm have also been analyzed. To illustrate the efficiency of the algorithm is used in solving the travel salesman problem. The theoretical analysis and experimental results show that the algorithm is of a strong potential in solving practical problems.