Hybrid learning clonal selection algorithm

Artificial immune system is a class of computational intelligence methods drawing inspiration from human immune system. As one type of popular artificial immune computing model, clonal selection algorithm (CSA) has been widely used for many optimization problems. CSA mainly generates new schemes by hyper-mutation operators which simulate the immune response process. However, these hyper-mutation operators, which usually perturb the antibodies in population, are semi-blind and not effective enough for complex optimization problems. In this paper, we propose a hybrid learning clonal selection algorithm (HLCSA) by incorporating two learning mechanisms, Baldwinian learning and orthogonal learning, into CSA to guide the immune response process. Specifically, (1) Baldwinian learning is used to direct the genotypic changes based on the Baldwin effect, and this operator can enhance the antibody information by employing other antibodies' information to alter the search space; (2) Orthogonal learning operator is used to search the space defined by one antibody and its best Baldwinian learning vector. In HLCSA, the Baldwinian learning works for exploration (global search) while the orthogonal learning for exploitation (local refinement). Therefore, orthogonal learning can be viewed as the compensation for the search ability of Baldwinian learning. In order to validate the effectiveness of the proposed algorithm, a suite of sixteen benchmark test problems are fed into HLCSA. Experimental results show that HLCSA performs very well in solving most of the optimization problems. Therefore, HLCSA is an effective and robust algorithm for optimization.