A survey of artificial immune algorithms for multi-objective optimization

Abstract

Multi-objective immune algorithm (MOIA) is a heuristic algorithm based on artificial immune system model. Due to its characteristics of antibody clonal selection, automatic antigen recognition and immune memory in the immune system, artificial immune algorithm has become a research hotspot in the field of multi-objective optimization after the evolutionary algorithms. In this paper, most MOIAs can be classified into three main categories according to the type of problem solving, i.e., they are mostly designed to solve multi-objective optimization problems (MOPs), dynamic MOPs, and constrained MOPs. In this paper, a comprehensive survey is presented to summarize most existing MOIAs, in which their corresponding characteristics, principles and theoretical analyses are discussed in details. Moreover, the performance of MOIAs on solving various kinds of MOPs and many-objective optimization problems is also studied in our experimental comparisons. Finally, a brief conclusion is given to summarize the current drawbacks, challenges, and some future directions for MOIAs.