The emergence of nature-inspired algorithms (NIA) is a great milestone in the field of computational intelligence community. As one of the NIAs, the artificial immune algorithm (AIS) mimics the principles of the biological immune system, and has exhibited its effectiveness, implicit parallelism, flexibility and applicability when solving various engineering problems. Nevertheless, AIS still suffers from the issues of evolution premature, local minima trapping and slow convergence due to its inherent stochastic search dynamics. Much effort has been made to improve the search performance of AIS from different aspects, such as population diversity maintenance, adaptive parameter control, etc. In this paper, we propose a novel multi-learning operator into the AIS to further enrich the search dynamics of the algorithm. A framework of embedding multiple commonly used mutation operators into the antibody evolution procedure is also established. Four distinct learning operators including baldwinian learning, cauchy mutation, gaussian mutation and lateral mutation are selected to merge together as a multi-learning operator. It can be expected that the multi-learning operator can effectively balance the exploration and exploitation of the search by enriched dynamics. To verify its performance, the proposed algorithm, which is called multi-learning immune algorithm (MLIA), is applied on a number of benchmark functions. Experimental results demonstrate the superiority of the proposed algorithm in terms of convergence speed and solution quality.