An Ensemble of Scalarizing Functions and Weight Vectors for Evolutionary Multi-Objective Optimization

Ensembles have been used in the evolutionary computation literature to evolve several populations in an independent manner, using different search approaches. Moreover, each population's parents compete with their offspring and the other population's offspring to improve diversity. It has been shown that ensemble algorithms improve the performance of the techniques embedded within them, when considered independently. Furthermore, scalarizing functions have been successfully used in decomposition-based and some indicator-based Multi-objective Evolutionary Algorithms (MOEAs). However, it has been shown that the performance of scalarizing function tends to be tied to the geometrical shape of the Pareto front. In this work, we propose a new ensemble algorithm that adopts different scalarizing functions and weight vectors using Hungarian Differential Evolution as the baseline multi-objective optimizer. Our experimental study shows that our proposed approach outperforms the original HDE, and it is competitive with respect to modern MOEAs.