摘要:现实中不断涌现出数目众多且日益复杂的多目标优化问题，迫切需要发展新型多目标优化算法以应对挑战.将基本萤火虫算法拓展至多目标优化领域，提出一种混合型多目标萤火虫算法HMOFA（hybrid multi-objective firefly algorithm）.该算法提出使用混合水平正交实验设计和连续决策空间量化的方法生成接近于用户指定规模且均匀分布于搜索空间的初始种群，为后续的进化提供良好的起始点；利用外部档案中的精英解个体引导萤火虫移动，促使算法较快收敛；运用3点最短路径方法维持外部档案的多样性.HMOFA算法与另外5种代表性多目标进化算法一同在17个基准多目标测试题上进行性能比较，实验结果表明，HMOFA算法在收敛性、多样性和鲁棒性方面总体上具有较显著的性能优势.



Abstract:It is necessary to develop some novel multi-objective optimization algorithms to cope with the complicated multi-objective optimization problems which are emerging and increasingly hard in reality. The basic firefly algorithm is extended to the realm of multi-objective optimization, and a hybrid multi-objective firefly algorithm (HMOFA) is proposed in this paper. Firstly, an initialization approach of mix-level orthogonal experimental design with the quantification of the continuous search space is used to generate an even-distributed initial population in the decision space. Secondly, the elites in the external archive are randomly selected to guide the movement of the fireflies in the evolutionary process. Finally, the archive pruning strategy based on three-point shortest path is used to maintain the diversity of the external archive. The proposed HMOFA is compared with other five peer algorithms in the performance of hypervolume based on seventeen benchmark multi-objective test instances, and the experimental results show that the HMOFA employs the overall performance advantages in convergence, diversity and robustness over other peer algorithms.



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