New decision-making approaches for ranking of non-dominated points for multi-objective optimization problems

A multi-objective optimization problem (MOP) is a simultaneous of more than one real-valued conflicting objective function subject to some constraints. Most MOP algorithms try provide set Pareto optimal solutions which are equally good in terms the functions. The can be infinite, and hence, analysis choice task or several among hard for decision maker (DM). In this paper, new scalarization approach proposed select solution convex MOPs such that relative importance assigned its functions very close together. addition, two decision-making methods developed analyze non-convex based on evaluating These support DM rank obtain them real implementation without having any familiarity about MOPs.