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Computational Intelligence and Neuroscience
Volume 2017, Article ID 8034573, 10 pages
Research Article

Firefly Mating Algorithm for Continuous Optimization Problems

1Computational Intelligence Laboratory, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
2Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
3College of Innovative Technology and Engineering, Dhurakij Pundit University, Bangkok, Thailand

Correspondence should be addressed to Arit Thammano;

Received 15 January 2017; Revised 22 May 2017; Accepted 14 June 2017; Published 20 July 2017

Academic Editor: Leonardo Franco

Copyright © 2017 Amarita Ritthipakdee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.