中文摘要：

现实中的多目标优化问题越来越多,而且日益复杂.受混合多目标优化算法设计思想的启发,将烟花爆炸方法和精英反向学习机制引入至多目标优化领域,提出一种应用精英反向学习的多目标烟花爆炸算法（Multi-Objective Fireworks Optimization Algorithm Using Elite Opposition-Based Learning,MOFAEOL）.该算法利用精英反向学习策略加强算法的全局搜索能力,利用烟花爆炸方法增强算法的局部搜索能力并提高求解的精度.这两种搜索机制相互协同以更好地平衡算法的全局勘探和局部开采的能力.MOFAEOL算法与另外5种代表性多目标优化算法一同在由ZDT系列和DTLZ系列组成的测试集上进行性能比较.实验表明,MOFAEOL算法在收敛性、多样性和稳定性方面均优于或部分优于其他对比算法.

英文摘要：

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 MO- FAEOL 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-objec- tive optimization problems efficiently. The MOFAEOL was compared with other five typical multi-objective optimization al- gorithms 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 conver- gence, diversity and stability.