An Artificial Immune System-based Many-Objective Optimization Algorithm with Network Activation Scheme

Abstract

In the research of multi-objective optimization algorithm, evolutionary algorithms have considered to be very successful tools. Artificial Immune System (AIS)-based algorithms as one of the viable alternative have also be widely developed in this domain. Over the years, researchers of evolutionary algorithms have extended their interest to many-objective situations; however works in AIS-based algorithms is rather scattered. This paper extends an AIS-based optimization algorithm to solve such many-objective optimization problems. The idea of ε-dominance and the holistic model of the immune network theory have been adopted to enhance the exploitation ability aiming for a quick convergence.