In this paper, a new multi-objective evolution- ary algorithm (MOEA) named hybrid MOEA with adaptive multi-population strategy (HMOEA-AMP) is proposed for multi-objective optimization problems (MOPs).In the frame- work of HMOEA-AMP, the particle swarm optimization and differential evolution are hybridized to guide the exploita- tion of the Pareto optimal solutions and the exploration of the optimal distribution of the achieved solutions, respec-
tively. Multiple subpopulations are constructed in an adaptive fashion according to a number of scalar subproblems, which are decomposed from a MOP through a set of predefined weight vectors. Comprehensive experiments using a set of benchmark are conducted to investigate the performance of
HMOEA-AMP in comparison with several state-of-the-art MOEAs. The experimental results show the advantage of the proposed algorithm.