Firefly algorithm (FA) is a classical and efficient swarm intelligence optimization method and has a natural capability to address multimodal optimization. However, it suffers from premature convergence and low stability in the solution quality. In this paper, a Yin-Yang firefly algorithm (YYFA) based on dimensionally Cauchy mutation is proposed for performance improvement of FA. An initial position of fireflies is specified by the good nodes set (GNS) strategy to ensure the spatial representativeness of the firefly population. A designed random attraction model is then used in the proposed work to reduce the time complexity of the algorithm. Besides, a key self-learning procedure on the brightest firefly is undertaken to strike a balance between exploration and exploitation. The performance of the proposed algorithm is verified by a set of CEC 2013 benchmark functions used for the single objective real parameter algorithm competition. Experimental results are compared with those of other the state-of-the-art variants of FA. Nonparametric statistical tests on the results demonstrate that YYFA provides highly competitive performance in terms of the tested algorithms. In addition, the application in constrained engineering optimization problems shows the practicability of YYFA algorithm.

Firefly算法（FA）是一种经典且有效的群体智能优化方法，具有解决多模式优化的天然能力。但是，它存在过早收敛和解决方案质量低稳定性的问题。提出了一种基于柯西突变的阴阳萤火虫算法（YYFA），以提高FA的性能。萤火虫的初始位置由良好节点集（GNS）策略指定，以确保萤火虫种群的空间代表性。然后在拟议的工作中使用设计的随机吸引模型来减少算法的时间复杂度。此外，还针对萤火虫最亮处进行了关键的自学程序，以在探索与开发之间取得平衡。该算法的性能通过一组用于单目标实参算法竞赛的CEC 2013基准函数验证。实验结果与FA的其他最新变体进行了比较。结果的非参数统计测试表明，YYFA在测试算法方面提供了极具竞争力的性能。另外，在约束工程优化问题中的应用表明了YYFA算法的实用性。