Immune secondary response and clonal selection inspired optimizers

Abstract

The immune system’s ability to adapt its B cells to new types of antigen is powered by processes known as clonal selection and affinity maturation. When the body is exposed to the same antigen, immune system usually calls for a more rapid and larger response to the antigen, where B cells have the function of negative adjustment. Based on the clonal selection theory and the dynamic process of immune response, two novel artificial immune system algorithms, secondary response clonal programming algorithm (SRCPA) and secondary response clonal multi-objective algorithm (SRCMOA), are presented for solving single and multi-objective optimization problems, respectively. Clonal selection operator (CSO) and secondary response operator (SRO) are the main operators of SRCPA and SRCMOA. Inspired by the clonal selection theory, CSO reproduces individuals and selects their improved maturated progenies after the affinity maturation process. SRO copies certain antibodies to a secondary pool, whose members do not participate in CSO, but these antibodies could be activated by some external stimulations. The update of the secondary pool pays more attention to maintain the population diversity. On the one hand, decimal-string representation makes SRCPA more suitable for solving high-dimensional function optimization problems. Special mutation and recombination methods are adopted in SRCPA to simulate the somatic mutation and receptor editing process. Compared with some existing evolutionary algorithms, such as OGA/Q, IEA, IMCPA, BGA and AEA, SRCPA is shown to be able to solve complex optimization problems, such as high-dimensional function optimizations, with better performance. On the other hand, SRCMOA combines the Pareto-strength based fitness assignment strategy, CSO and SRO to solve multi-objective optimization problems. The performance comparison between SRCMOA, NSGA-II, SPEA, and PAES based on eight well-known test problems shows that SRCMOA has better performance in converging to approximate Pareto-optimal fronts with wide distributions.