Some immune optimization algorithms inspired by the biological immune system, for example the clonal selection theory, the immune network algorithm have been presented, but these algorithms are lack of fast convergence speed, high robustness and are difficult for attaining the optimal solution of optimization problems. The complement system, which represents a chief component of innate immunity, not only participates in inflammation but also acts to enhance the adaptive immune response. In order to improve the capability of immune system resolving optimization problems, a novel immune algorithm based on the complement activation theory-an immune complement optimization algorithm (ICOA) was presented. In ICOA, two complement operators: cleave operator and bind operator are presented firstly, cleave operator cleaved a complement individual into two sub-individuals, while bind operator binded two complement individuals together to form a big complement individual. Then,the convergence, robustness of ICOA were analyzed theoretically,which proved that ICOA could converge to the optimal solution and had high robustness.Finally, the experiments results of ICOA compared with the standard clonal selection algorithm (CSA) showed that the optimal solution,convergence rate, robustness of ICOA were better than of CSA.
