More and more complex multi-objective optimization problems have emerged in the real world.Inspired by the idea of hybrid components of multi-objective optimization algorithms

a method of fireworks explosion optimization and a strategy of elite opposition-based learning were introduced into the field of multi-objective optimization.A multi-objective fireworks optimization algorithm using elite opposition-based learning (MOFAEOL) was proposed in the paper.The MOFAEOL utilized the elite opposition-based learning strategy to strengthen the global search ability

and adopted the fireworks explosion optimization approach to improve the local search ability and the accuracy of the algorithm.These two learning mechanisms collaborated to balance the global exploration and the local exploitation

in order to solve some hard multi-objective optimization problems efficiently.The MOFAEOL was compared with other five typical multi-objective optimization algorithms on a benchmark test set including 12 multi-objective optimization test problems composed by ZDT and DTLZ series functions.Experimental results show that the MOFAEOL is superior or competitive to the other peer algorithms in convergence

diversity and stability.