Articulating Decision Maker's Preference Information within Multiobjective Artificial Immune Systems

During the two last decades, evolutionary algorithms have been successfully used to solve multiobjective optimization problems. Several works have been established to improve convergence and diversity. Recently, several multiobjective artificial immune systems have shown their ability to solve multiobjective optimization problems. However, in reality, decision makers are not interested with the whole optimal Pareto front rather than the portion of the Pareto front that matches at most their preferences, i.e, the region of interest. In this paper, we propose a new dominance relation inspired from several ideas of the danger theory, called Danger Zone-based dominance (DZ-dominance), which guides the search process towards the preferred part of the Pareto front. The DZ-dominance is incorporated within the Nondominated Neighbor Immune Algorithm (NNIA). The new preference-based algorithm, named DZ-NNIA, has demonstrated its ability to guide the search based on decision maker's preferences. Moreover, comparative experiments show that our algorithm outperforms the most recent preference-based immune algorithm HMIA and the preference-based multiobjective evolutionary algorithm g-NSGA-II.