Multi-objective optimization using a hybrid differential evolution algorithm

Abstract

This paper proposes a hybrid differential evolution algorithm for multi-objective optimization problems. One major feature of this hybrid multi-objective differential evolution (HMODE) algorithm is that it adopts subpopulations whose sizes are dynamically adapted during the evolution process. The second feature is that the HMODE adopts a new solution update mechanism instead of the standard one used in the traditional differential evolution. The HMODE uses multiple operators and assigns an operator to each subpopulation. The update of each subpopulation is based on the assigned operator. The third feature of the HMODE is that a self-adapt local search method is used to improve the external archive. Computational study on benchmark problems shows that the HMODE is competitive or superior to previous multi-objective algorithms in the literature.