Enhancing the learning capacity of immunological algorithms: a comprehensive study of learning operators

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.This work is partially supported by the National Natural Science Foundation of China under Grants 61203325, 61271114 and 61003205, Genguang Project of Shanghai Educational Development Foundation (No. 12CG35), Ph.D. Program Foundation of Ministry of Education of China (No.20120075120004), and the Fundamental Research Funds for the Central Universities