Skip to main content

Advertisement

Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Firefly algorithm (FA) is a new swarm intelligence optimization algorithm, which has shown an effective performance on many optimization problems. However, it may suffer from premature convergence when solving complex optimization problems. In this paper, we propose a new FA variant, called NSRaFA, which employs a random attraction model and three neighborhood search strategies to obtain a trade-off between exploration and exploitation abilities. Moreover, a dynamic parameter adjustment mechanism is used to automatically adjust the control parameters. Experiments are conducted on a set of well-known benchmark functions. Results show that our approach achieves much better solutions than the standard FA and five other recently proposed FA variants.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Amiri B, Hossain L, Crawford JW, Wigand RT (2013) Community detection in complex networks: multi-objective enhanced firefly algorithm. Knowl Based Syst 46:1–11

    Article  Google Scholar 

  • Brest J, Greiner S, Bo\(\check{s}\)kovi\(\acute{c}\) B, Mernik M, \(\check{Z}\)umer V, (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evolut Comput 10(6):646–657

  • Chandrasekaran K, Simon SP, Padhy NP (2013) Binary real coded firefly algorithm for solving unit commitment problem. Inf Sci 249:67–84

    Article  Google Scholar 

  • Chen BJ, Shu HZ, Coatrieux G, Chen G, Sun XM, Coatrieux JL (2015) Color image analysis by quaternion-type moments. J Math Imaging Vis 51(1):124–144

    Article  MathSciNet  MATH  Google Scholar 

  • 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), pp 58–63

  • Coelho LS, Mariani VC (2013) Improved firefly algorithm approach applied to chiller loading for energy conservation. Energy Build 59:273–278

    Article  Google Scholar 

  • Das S, Abraham A, Chakraborty U, Konar A (2009) Differential evolution using a neighborhood-based mutation operator. IEEE Trans Evol Comput 13(3):526–553

    Article  Google Scholar 

  • Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26:29–41

    Article  Google Scholar 

  • Duang H, Luo Q (2015) New progresses in swarm intelligence-based computation. Int J Bio-Inspir Comput 7(1):26–35

    Article  Google Scholar 

  • Farahani SM, Abshouri AA, Nasiri B, Meybodi MR (2011) A Gaussian firefly algorithm. Int J Mach Learn Comput 1(5):448–453

    Article  Google Scholar 

  • Fister Jr I, Yang XS, Fister I, Brest J (2012) Memetic firefly algorithm for combinatorial optimization. In: Bioinspired optimization methods and their applications (BIOMA 2012), pp 1–14

  • Fister I Jr, Fister I, Yang XS, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evolut Comput 13:34–46

    Article  Google Scholar 

  • Fister I, Yang XS, Brest J, Fister I Jr (2013) Modified firefly algorithm using quaternion representation. Exp Syst Appl 40(18):7220–7230

    Article  Google Scholar 

  • Fister I Jr, Yang XS, Fister I, Brest J, Fister D (2013) A brief review of nature-inspired algorithms for optimization. Elektrotehniǎki Vestnik 80(3):1–7

    MATH  Google Scholar 

  • Fister I Jr, Perc M, Kamal SM, Fister I (2015) A review of chaos-based firefly algorithms: perspectives and research challenges. Appl Math Comput 252:155–165

    MathSciNet  MATH  Google Scholar 

  • Fister I Jr, Yang XS, Brest J, Fister D, Fister I (2015) Analysis of randomisation methods in swarm intelligence. Int J Bio-Inspir Comput 7(1):36–49

    Article  Google Scholar 

  • Florence AP, Shanthi V (2014) A load balancing model using firefly algorithm in cloud computing. J Comput Sci 10(7):1156–1165

    Article  Google Scholar 

  • Fu ZJ, Sun XM, Liu Q, Zhou L, Shu JG (2015) Achieving efficient cloud search services: multi-keyword ranked search over encrypted cloud data supporting parallel computing. IEICE Trans Commun E98–B(1):190–200

    Article  Google Scholar 

  • Gandomi AH, Yang XS, Alavi AH (2013) Mixed variable structural optimization using firefly algorithm. Comput Struct 89(23–24):2325–2336

    Google Scholar 

  • Gandomi AH, Yang XS, Talatahari S, Alavi AR (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18(1):89–98

    Article  MathSciNet  MATH  Google Scholar 

  • García S, Fern\(\acute{a}\)ndez A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental an alysis of power. Inf Sci 180(20):2044–2064

  • Gopinadh V, Singh A (2015) Swarm intelligence approaches for cover scheduling problem in wireless sensor networks. Int J Bio-Inspir Comput 7(1):50–61

    Article  Google Scholar 

  • Gu B, Sheng VS, Tay KY, Romano W, Li S (2015) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416

    Article  MathSciNet  Google Scholar 

  • Gu B, Sheng VS, Wang ZJ, Ho D, Osman S, Li S (2015) Incremental learning for \(\nu \)-support vector regression. Neural Netw 67:140–150

    Article  Google Scholar 

  • Hassanzadeh T, Vojodi H, Moghadam AME (2011) An image segmentation approach based on maximum variance intra-cluster method and firefly algorithm. In: The 7th international conference on natural computation (ICNC), pp 1817–1821

  • Horng MH (2012) Vector quantization using the firefly algorithm for image compression. Exp Syst Appl 39(1):1078–1091

    Article  Google Scholar 

  • Kazem A, Sharifi E, Hussain F, Saberi M, Hussain OK (2013) Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Appl Soft Comput 13(2):947–958

    Article  Google Scholar 

  • Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, pp 1942–1948

  • Kougianos E, Mohanty SP (2015) A nature-inspired firefly algorithm based approach for nanoscale leakage optimal RTL structure. Integr VLSI J 51:46–60

    Article  Google Scholar 

  • Li J, Li XL, Yang B, Sun XM (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518

    Article  Google Scholar 

  • Liang RH, Wang JC, Chen YT, Tseng WT (2015) An enhanced firefly algorithm to multi-objective optimal active/reactive power dispatch with uncertainties consideration. Int J Electr Power Energy Syst 64:1088–1097

    Article  Google Scholar 

  • Long NC, Meesad P, Unger H (2015) A highly accurate firefly based algorithm for heart disease prediction. Exp Syst Appl 42(21):8221–8231

    Article  Google Scholar 

  • Ma TH, Zhou JJ, Tang ML, Tian Y, Al-dhelaan A, Al-rodhann M, Lee S (2015) Social network and tag sources based augmenting collaborative recommender system. IEICE Trans Inf Syst E98–D(4):902–910

    Article  Google Scholar 

  • Mahapatra S, Panda S, Swain SC (2014) A hybrid firefly algorithm and pattern search technique for SSSC based power oscillation damping controller design. Ain Shams Eng J 5:1177–1188

    Article  Google Scholar 

  • Marichelvam MK, Prabaharan T, Yang XS (2014) A discrete firefly algorithm for the multi-objective hybrid flowshop scheduling problems. IEEE Trans Evol Comput 18(2):301–305

    Article  Google Scholar 

  • Miguel LFF, Lopez RH, Miguel LFF (2013) Multimodal size, shape, and topology optimisation of truss structures using the firefly algorithm. Adv Eng Softw 56:23–37

    Article  Google Scholar 

  • Poursalehi N, Zolfaghari A, Minuchehr A (2013) Multi-objective loading pattern enhancement of PWR based on the discrete firefly algorithm. Ann Nucl Energy 57:151–163

    Article  Google Scholar 

  • Rahmani A, MirHassani SA (2014) A hybrid firefly-genetic algorithm for the capacitated facility location problem. Inf Sci 283:70–78

    Article  MathSciNet  MATH  Google Scholar 

  • Ren YJ, Shen J, Wang J, Han J, Lee S (2015) Mutual verifiable provable data auditing in public cloud storage. J Int Technol 16(2):317–323

    Google Scholar 

  • Sahu RK, Panda S, Padhan S (2015) A hybrid firefly algorithm and pattern search technique for automatic generation control of multi area power systems. Int J Electr Power Energy Syst 64:9–23

    Article  Google Scholar 

  • Saraç E, Özel SA (2013) Web page classification using firefly optimization. In: IEEE international symposiumon innovations in intelligent systems and applications (INISTA), pp 1–5

  • Sayadi MK, Hafezalkotob A, Naini S (2013) Firefly-inspired algorithm for discrete optimization problems: an application to manufacturing cell formation. J Manuf Syst 32(1):78–84

    Article  Google Scholar 

  • Senthilnath J, Omkar SN, Mani V (2011) Clustering using firefly algorithm: performance study. Swarm Evolut Comput 1(3):164–171

    Article  Google Scholar 

  • Shen J, Tan HW, Wang J, Wang JW, Lee S (2015) A novel routing protocol providing good transmission reliability in underwater sensor networks. J Int Technol 16(1):171–178

    Google Scholar 

  • Shomalnasab F, Sadeghzadeh M, Esmaeilpour M (2014) An optimal similarity measure for collaborative filtering using firefly algorithm. J Adv Comput Res 5(3):101–111

    Google Scholar 

  • Srivatsava PR, Mallikarjun B, Yang XS (2013) Optimal test sequence generation using firefly algorithm. Swarm Evolut Comput 8:44–53

    Article  Google Scholar 

  • Tilahun SL, Ong HC (2012) Modified firefly algorithm. J Appl Math 2012:1–12. doi:10.1155/2012/467631

    Article  MathSciNet  MATH  Google Scholar 

  • Wang H, Wu ZJ, Rahnamayan S, Li CH, Zeng SY, Jiang DZ (2011) Particle swarm optimization with simple and efficient neighbourhood search strategies. Int J Innov Comput Appl 3(2):7–104

    Google Scholar 

  • Wang H, Rahnamayan S, Sun H, Omran MGH (2013) Gaussian bare-bones differential evolution. IEEE Trans Cybern 43(2):634–647

    Article  Google Scholar 

  • Wang H, Sun H, Li CH, Rahnamayan S, Pan JS (2013) Diversity enhanced particle swarm optimization with neighborhood search. Inf Sci 223:119–135

    Article  MathSciNet  Google Scholar 

  • Wang B, Li DX, Jiang JP, Liao YH (2014) A modified firefly algorithm based on light intensity difference. J Comb Optim 31(3):1045–1060

    Article  MathSciNet  MATH  Google Scholar 

  • Wang H, Wang WJ, Sun H, Rahnamayan S (2016) Firefly algorithm with random attraction. Int J Bio-Inspir Comput 8(1):33–41

    Article  Google Scholar 

  • Wen XZ, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295:395–406

  • Xia ZH, Wang XH, Sun XM, Liu QS, Xiong NX (2014) Steganalysis of LSB matching using differences between nonadjacent pixels. Multimed Tools Appl. doi:10.1007/s11042-014-2381-8

  • Xia ZH, Wang XH, Sun XM, Wang Q (2015) A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. IEEE Trans Parallel Distrib Syst. doi:10.1109/TPDS.2015.2401003

  • Xia ZH, Wang XH, Sun XM, Wang BW (2014) Steganalysis of least significant bit matching using multi-order differences. Secur Commun Netw 7(8):1283–1291

    Article  Google Scholar 

  • Xie SD, Wang YX (2014) Construction of tree network with limited delivery latency in homogeneous wireless sensor networks. Wirel Pers Commun 78(1):231–246

    Article  Google Scholar 

  • Xu M, Liu GZ (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 XS, Deb S (2009) Cuckoo search via Lvy flights. In: World congress on nature and biologically inspired computing (NaBIC 2009), pp 210–214

  • Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), Springer, Berlin, pp 65–74

  • Yang XS (2008) Nature-inspired metaheuristic algorithms. Luniver Press, London

    Google Scholar 

  • Yang XS (2010) Engineering optimization: an introduction with metaheuristic applications. Wiley, New York

    Book  Google Scholar 

  • Yang XS, Hosseini SSS, Gandomi AH (2012) Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl Soft Comput 12(3):1180–1186

    Article  Google Scholar 

  • Yu SH, Su SB, Lu QP, Huang L (2014) A novel wise step strategy for firefly algorithm. Int J Comput Math 91(12):2507–2513

    Article  MathSciNet  MATH  Google Scholar 

  • Yu SH, Zhu SL, Ma Y, Mao DM (2015) A variable step size firefly algorithm for numerical optimization. Appl Math Comput 263:214–220

    MathSciNet  Google Scholar 

  • Zheng YH, Jeon B, Xu DH, Wu QMJ, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst 28(2):961–973

    Google Scholar 

Download references

Acknowledgments

This work is supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, the Humanity and Social Science Foundation of Ministry of Education of China (No. 13YJCZH174), the National Natural Science Foundation of China (Nos. 61305150, 61261039, and 61461032), and the Natural Science Foundation of Jiangxi Province (No. 20142BAB217020).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hui Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Cui, Z., Sun, H. et al. Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism. Soft Comput 21, 5325–5339 (2017). https://doi.org/10.1007/s00500-016-2116-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-016-2116-z

Keywords