Decomposition with ensemble neighborhood size multi-objective adaptive differential evolutionary algorithm

Decomposition is a conventional optimization method, and the differential evolutionary algorithm is widely applied in the multi-objective optimization problems (MOP). A novel algorithm-ADEMO/D-ENS which combines the two algorithms, the adaptive differential evolutionary algorithm and the decomposition with variable neighborhood size, is proposed to overcome the drawbacks of the classical differential evolution algorithm and the decomposition method. The approach makes use of the Tchebycheff method to decompose the multi-objective optimization problems into scalar optimization sub-problems. And the sub-problems are optimized by neighborhood relations among them. The adaptive selection approach based on ensemble of neighborhood size is used to determine the neighborhood size. Meanwhile, the probability match adaptive method is used to select differential strategy from the differential strategy pool. Moreover, the complexity of the algorithm is analyzed. Finally, compared with the classical non-dominated sorting genetic algorithms II (NSGA-II) algorithm and the multi-objective differential evolution algorithm (MODE), simulation results verified that the ADEMO/D-ENS approach can deal with the multi-objective optimization problems more effectively.