Ritthipakdee Amarita, Thammano Arit, Premasathian Nol, Jitkongchuen Duangjai
Computational Intelligence Laboratory, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
Comput Intell Neurosci. 2017;2017:8034573. doi: 10.1155/2017/8034573. Epub 2017 Jul 20.
This paper proposes a swarm intelligence algorithm, called firefly mating algorithm (FMA), for solving continuous optimization problems. FMA uses genetic algorithm as the core of the algorithm. The main feature of the algorithm is a novel mating pair selection method which is inspired by the following 2 mating behaviors of fireflies in nature: (i) the mutual attraction between males and females causes them to mate and (ii) fireflies of both sexes are of the multiple-mating type, mating with multiple opposite sex partners. A female continues mating until her spermatheca becomes full, and, in the same vein, a male can provide sperms for several females until his sperm reservoir is depleted. This new feature enhances the global convergence capability of the algorithm. The performance of FMA was tested with 20 benchmark functions (sixteen 30-dimensional functions and four 2-dimensional ones) against FA, ALC-PSO, COA, MCPSO, LWGSODE, MPSODDS, DFOA, SHPSOS, LSA, MPDPGA, DE, and GABC algorithms. The experimental results showed that the success rates of our proposed algorithm with these functions were higher than those of other algorithms and the proposed algorithm also required fewer numbers of iterations to reach the global optima.
本文提出了一种用于解决连续优化问题的群体智能算法，称为萤火虫交配算法（FMA）。FMA以遗传算法作为算法核心。该算法的主要特点是一种新颖的交配配对选择方法，它受自然界中萤火虫的以下两种交配行为启发：（i）雄性和雌性之间的相互吸引导致它们交配；（ii）两性萤火虫均为多配偶类型，与多个异性伴侣交配。雌性持续交配直至受精囊充满，同样地，雄性可以为多个雌性提供精子直至其精子库耗尽。这一新特性增强了算法的全局收敛能力。使用20个基准函数（16个30维函数和4个2维函数）对FMA的性能进行了测试，并与FA、ALC - PSO、COA、MCPSO、LWGSODE、MPSODDS、DFOA、SHPSOS、LSA、MPDPGA、DE和GABC算法进行比较。实验结果表明，我们提出的算法在这些函数上的成功率高于其他算法，并且该算法达到全局最优所需的迭代次数也更少。
