Fractal Decomposition Approach for Continuous Multi-Objective Optimization Problems

Abstract

Multi-objective optimization problems (MOPs) have been widely studied during the last decades. In this article, we present a new intrinsically parallel approach based on Fractal decomposition (FDA) to solve MOPs. The key contribution of the proposed approach is to divide recursively the decision space using hyperspheres. Two different methods were investigated: the first one is based on scalarization that has been distributed on a parallel multi-node architecture virtual environments and taking profit from the FDA’s properties, while the second method is based on Pareto dominance sorting. A comparison with state of the art algorithms on different well known benchmarks shows the efficiency and the robustness of the proposed decomposition approaches.

Keywords

• Multi-objective optimization
• large-scale optimization
• metaheuristics
• geometric fractal decomposition
• local search
• continuous optimization