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

Abstract

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.

A Multi-Learning Immune Algorithm for Numerical Optimization

“…In other words, each learning operator is designed for specific problems with distinct characteristics. For example, the gaussian mutation is more capable of exploitation in small regions of a smooth decision space, while cauchy mutation enables the search to carry out long distance jumps, thus specializing in exploring unvisited regions [32], [37]. As a result, cauchy mutation is more suit to optimize the problems with plenty of local optima.…”