First published 2 September 2013
Enhancing the learning capacity of immunological algorithms: a comprehensive study of learning operators
Shangce Gao, Tao Gong, Weiya Zhong, Fang Wang, Beibei Chen
Immunological algorithms are a kind of bio-inspired intelligence methods which draw inspiration from natural immune systems. The problem-solving performance of immunological algorithms mainly lies on the utilization of learning (i.e. mutation) operators. In this paper, nine different learning operators in a standard immune algorithmic framework are investigated. These learning operators consist of eight existing operators and a newly proposed search direction based operator. Experiments are conducted based on nine variants of immunological algorithms that use different learning operators. Simulation results on a large number of benchmark optimization problems give a deep insight into the characteristics of these operators, and further verify that the proposed new learning operator can greatly improve the performance of immunological algorithms.