A Novel Clonal Selection Algorithm for Global Optimization Problems

In order to increase the diversity of immune algorithm when solving high-dimensional global optimization problems, a novel clonal selection algorithm with randomized clonal expansion strategy(RCSA) is proposed. The main characteristic of RCSA is clonal expansion. In addition, a novel performance evaluation criterion is constructed in this paper, by which the performance of different population-based algorithms can be compared easily. In the experimental study, firstly we obtain an appropriate value of the ratio of clonal expansion through some traditional test functions. Next several conventional clonal selection algorithms are used to validate the performance of proposed RCSA. The experimental results of the RCSA are significantly better than that of the conventional CSAs.