In reality

the diversification and complexity of the multi-objective optimization problems (MOPs) require the development of some novel multi-objective optimization algorithms.Inspired by the hybrid multi-objective evolutionary algorithms (MOEAs) and new evolutionary instances

an enhanced multi-objective fireworks explosion optimization algorithm (eMOFEOA for short) is proposed to solve the hard MOPs efficiently in the paper.Firstly

the proposed approach uses the approach of combining uniformization and randomization to generate an initial population that are scattered uniformly over the feasible search space

so that the algorithm can acquire a good beginning for the subsequent iterations.Secondly

a fine control strategy of explosion radius is adopted in the eMOFEOA

that is to say

different generation of population has different radius

and the different firework in the same generation have different radius based on its strength of Pareto dominace

so as to save the computation resource to the maximum extent.Thirdly

a simplified k-nearest neighbor approach is employed to maintain the diversity of external archive in the eMOFEOA.The proposed eMOFEOA is compared with the other five peer comparison algorithms in the performance of convergence and diversity based on 12 benchmark multi-objective test functions

and the experimental results show that our eMOFEOA has the overall performance advantages in convergence

diversity and stability.