How to Apply the Multi-Objective Optimizer MOON<sup>2</sup>/MOON<sup>2R</sup> for Many-Objective Optimization Problems

Abstract

Multi-objective optimization that can support agile and flexible decision-making has been highly required to deal with complex and global decision environment. We have proposed, in this paper, a general idea for solving many-objective optimization problem using MOON2/MOON2R. Those are multi-objective optimization methods relying on a prior articulation in tradeoff analysis among conflicting objectives. To overcome stiffness and shortcomings of the conventional interactive and prior methods, it is developed by the virtue of simple subjective judgment and neural networks for identifying value function. As a great advantage, thus identified value function is amenable to a variety of conventional and recent simulation-based optimization methods. In spite of requiring simple and relative responses, DM's tradeoff analysis becomes pretty difficult for many-objective optimization problem. To overcome this difficulty, we present a stage-wise process that is popular in AHP (Analytic Hierarchy Process). Eventually, the proposed idea makes those methods more general and practical toward recent qualified decision making. After showing a general procedure, a few illustrative applications are provided to verify effectiveness of the proposed method. Finally, total discussion is presented to make the points definitely.