A Novel Artificial Immune System for Multiobjective Optimization Problems

This study presents a novel weight-based multiobjective artificial immune system (WBMOAIS) based on opt-aiNET. The proposed algorithm follows the elementary structure of opt-aiNET, but has the following distinct characteristics: At first, a randomly weighted sum of multiple objectives is used as a fitness function; Secondly, the individuals of the population are chosen from the memory, which is a set of elite solutions. Lastly, in addition to the clonal suppression algorithm similar to that used in opt-aiNET, a new truncation algorithm with similar individuals (TASI) is presented in order to eliminate the similar individuals in memory and obtain a well-distributed spread of non-dominated solutions. Simulation results show WBMOAIS outperforms the vector immune algorithm (VIS) and the elitist non-dominated sorting genetic system (NSGA-II).