A General Architecture for Generating Interactive Decomposition-Based MOEAs

Abstract

Evolutionary algorithms have been widely applied for solving multiobjective optimization problems. Such methods can approximate many Pareto optimal solutions in a population. However, when solving real-world problems, a decision maker is usually involved, who may only be interested in a subset of solutions that meet their preferences. Several methods have been proposed to consider preference information during the solution process. Among them, interactive methods support the decision maker in learning about the trade-offs among objectives and the feasibility of solutions. Also, such methods allow the decision maker to provide preference information iteratively. Typically, interactive multiobjective evolutionary algorithms are modifications of existing a priori or a posteriori algorithms. However, they mainly focus on finding a region of interest and do not support the decision maker finding the most preferred solution. In addition, the cognitive load imposed on the decision maker is usually not considered. This article proposes an architecture for developing interactive decomposition-based evolutionary algorithms that can support the decision maker during the solution process. The proposed architecture aims to improve the applicability of interactive methods in solving real-world problems by considering the needs of a decision maker. We apply our proposal to generate an interactive decomposition-based algorithm utilizing a reference vector re-arrangement procedure and MOEA/D. We demonstrate the performance of our proposal with a real-world problem and multiple benchmark problems.