IM-MOEA/D: An Inverse Modeling Multi-Objective Evolutionary Algorithm Based on Decomposition

Abstract

The inverse modeling multi-objective evolutionary algorithm (IM-MOEA) is a method to solve multi-objective optimization problems (MOP) that samples candidate solutions straight from the objective space, making it easier to control the diversity of the solutions. In the literature, the objective space is partitioned into several subregions by predefining a set of reference vectors, and the selection criterion adopted is based on dominance. These features can cause difficulties to deal with large-scale MOPs (LSMOPs) and with many-objective optimization problems (MaOPs). To address such an issue, this paper proposes the IM-MOEA based on decomposition (IM-MOEA/D) which uses a new scheme for grouping in the objective space based on k-means and a selection criterion based on decomposition, global replacement, that chooses the most appropriate reference vector from the whole population. The experimental results on 45 LSMOPs for 2 to 6 objectives suggest that IM-MOEA/D reached better performance than the compared state-of-the-art MOEAs.