Abstract: A novel eccentric dynamic immune clone algorithm (EDICA) was proposed based on the analysis of antibody behavior features in existed clone selection algorithm (CSA). Heuristic information implicates that descendant antibodies are always better than their parents during evolution, which derive an eccentric mutation strategy, and let the mutation center shift a proper distance along the direction which is from parent to descendant, antibodies may search towards optima more quickly. A dynamic mutation radial adjustment method was proposed with some introduced control factors. The search speed was accelerated by enlarged mutation radial at initial stage. Then the search granularity was gradually diminished so as to improve optimization precision at later stage. A hyper sphere chaos mutation strategy was adopted to avoid the adverse effects of anisotropy and ensure the ability to successfully find global optima. Experiment results show that the EDICA could not only accurately discover most optima of static function but also hit and follow optima of dynamic function with high precision.