ADEMO/D: Adaptive Differential Evolution for Multiobjective Problems

This paper proposes a method for continuous optimization based on Differential Evolution (DE). The approach named Adaptive Differential Evolution for Multiobjective Problems (ADEMO/D) incorporates concepts of Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) and mechanisms of mutation strategies adaptation inspired by the adaptive DE named Self-adaptive Differential Evolution (SaDE). Additionally a new mutation strategy, based on MOEA/D neighborhood concept, is proposed to be used in the strategy candidate pool. ADEMO/D is compared with three multi-objective optimization approaches using a set of benchmarks. The preliminary results are very promising and stand the proposed approach as a candidate to the State-of-art for multi-objective optimization.