Firefly algorithm with adaptive control parameters
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Firefly algorithm (FA) is a new swarm intelligence optimization method, which has shown good search abilities on many optimization problems. However, the performance of FA highly depends on its control parameters. In this paper, we investigate the control parameters of FA, and propose a modified FA called FA with adaptive control parameters (ApFA). To verify the performance of ApFA, experiments are conducted on a set of well-known benchmark problems. Results show that the ApFA outperforms the standard FA and five other recently proposed FA variants.
KeywordsFirefly algorithm (FA) Swarm intelligence Adaptive control parameters Self-adaptive FA Global optimization
This work is supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions, the Humanity and Social Science Foundation of Ministry of Education of China (No. 13YJCZH174), the National Natural Science Foundation of China (Nos. 61305150, 61261039, 61402294, and 61572328), Major Fundamental Research Project in the Science and Technology Plan of Shenzhen under Grants (Nos. JCYJ20140 828163633977, JCYJ20140418181958501, and JCYJ201 50630105452814), Open Research Fund of China-UK Visual Information Processing Lab, National Social Science Foundation of China (No. 15CGL040), the Foundation of State Key Laboratory of Software Engineering (No. SKLSE2014-10-04), and the Natural Science Foundation of Jiangxi Province (Nos. 20142BAB217020 and 20151BAB217007).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Chhikara RR, Singh L (2015) An improved discrete firefly and t-test based algorithm for blind image steganalysis. In: The 6th international conference on intelligent systems, modelling and simulation (ISMS). IEEE, pp 58–63Google Scholar
- Fister Jr I, Yang X-S, Fister I, Brest J (2012) Memetic firefly algorithm for combinatorial optimization. arXiv preprint arXiv:1204.5165
- Fister I, Perc M, Kamal SM (2015a) A review of chaos-based firefly algorithms. Appl Math Comput 252:155–165Google Scholar
- Fister I Jr, Yang X-S, Brest J, Fister D, Fister I (2015b) Analysis of randomisation methods in swarm intelligence. Int J Bio-Inspired Comput 7(1):36–49Google Scholar
- Gu B, Sheng VS, Tay KY, Romano W, Li S (2015a) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416Google Scholar
- Gu B, Sheng VS, Wang Z, Ho D, Osman S, and Li S (2015b) Incremental learning for \(\nu \)-support vector regression. Neural Netw 67:140–150Google Scholar
- Hassanzadeh T, Vojodi H, Moghadam AME (2011) An image segmentation approach based on maximum variance intra-cluster method and firefly algorithm. In: The seventh international conference on natural computation (ICNC). IEEE, pp 1817–1821Google Scholar
- Kennedy J, Eberhart R (1995) Particle swarm optimization. IEEE Int Conf Neural Netw 4:1942–1948Google Scholar
- Palit S, Sinha SN, Molla MA, Khanra A, Kule M (2011) A cryptanalytic attack on the knapsack cryptosystem using binary firefly algorithm. In: 2011 2nd international conference on computer and communication technology (ICCCT). IEEE, pp 428–432Google Scholar
- Ren Y, Shen J, Wang J, Han J, Lee S (2015) Mutual verifiable provable data auditing in public cloud storage. J Internet Technol 16(2):317–323Google Scholar
- Roy AG, Rakshit P, Konar A, Bhattacharya S, Kim E, Nagar AK (2013) Adaptive firefly algorithm for nonholonomic motion planning of car-like system. In: IEEE congress on evolutionary computation (CEC 2013). IEEE, pp 2162–2169Google Scholar
- Saraç E, Özel SA (2013) Web page classification using firefly optimization. In: IEEE international symposium on innovations in intelligent systems and applications (INISTA). IEEE, pp 1–5Google Scholar
- Shen J, Tan H, Wang J, Wang J, Lee S (2015) A novel routing protocol providing good transmission reliability in underwater sensor networks. J Internet Technol 16(1):171–178Google Scholar
- Shomalnasab F, Sadeghzadeh M, Esmaeilpour M (2014) An optimal similarity measure for collaborative filtering using firefly algorithm. J Adv Comput Res 5(3):101–111Google Scholar
- Wang B, Li D-X, Jiang J-P, Liao Y-H (2014) A modified firefly algorithm based on light intensity difference. J Combin Optim 1–16. doi: 10.1007/s10878-014-9809-y
- Xia Z, Wang X, Sun X, Liu Q, Xiong N (2014a) Steganalysis of LSB matching using differences between nonadjacent pixels. Multimedia Tools and Applications. doi: 10.1007/s11042-014-2381-8
- Xia Z, Wang X, Sun X, Wang B (2014b) Steganalysis of least significant bit matching using multi-order differences. Secur Commun Netw 7(8):1283–1291Google Scholar
- Xia Z, Wang X, Sun X, Wang Q (2015) A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. IEEE Trans Parallel Distributed Syst. doi: 10.1109/TPDS.2015.2401003
- Xu M, Liu G (2013) A multipopulation firefly algorithm for correlated data routing in underwater wireless sensor networks. Int J Distrib Sens Netw. doi: 10.1155/2013/865154
- Yang X-S (2008) Nature-inspired metaheuristic algorithms. Luniver Press, BeckingtonGoogle Scholar
- Zheng Y, Jeon B, Xu D, Wu QM, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst 28(2):961–973Google Scholar