Skip to main content

Advertisement

Springer Nature Link
Log in

Flower pollination algorithm: a comprehensive review

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Flower pollination algorithm (FPA) is a computational intelligence metaheuristic that takes its metaphor from flowers proliferation role in plants. This paper provides a comprehensive review of all issues related to FPA: biological inspiration, fundamentals, previous studies and comparisons, implementation, variants, hybrids, and applications. Besides, it makes a comparison between FPA and six different metaheuristics such as genetic algorithm, cuckoo search, grasshopper optimization algorithm, and others on solving a constrained engineering optimization problem . The experimental results are statistically analyzed with non-parametric Friedman test which indicates that FPA is superior more than other competitors in solving the given problem.

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

Access this article

Log in via an institution

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

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

References

  • Abdelaziz AY, Ali ES (2015) Static VAR compensator damping controller design based on flower pollination algorithm for a multi-machine power system. Electric Power Compon Syst 43(11):1268–1277

    Article  Google Scholar 

  • Abdelaziz AY, Ali ES, Elazim SA (2016a) Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems. Energy 101:506–518

    Article  Google Scholar 

  • Abdelaziz AY, Ali ES, Elazim SA (2016b) Flower pollination algorithm and loss sensitivity factors for optimal sizing and placement of capacitors in radial distribution systems. Int J Electr Power Energy Syst 78:207–214

    Article  Google Scholar 

  • Abdelaziz AY, Ali ES, Elazim SA (2016c) Optimal sizing and locations of capacitors in radial distribution systems via flower pollination optimization algorithm and power loss index. Eng Sci Technol Int J 19(1):610–618

    Article  Google Scholar 

  • Abdel-Baset M, Hezam IM (2015a) An improved flower pollination algorithm for ratios optimization problems. Appl Math Inf Sci Lett Int J 3(2):83–91

    Google Scholar 

  • Abdel-Baset M, Hezam IM (2015b) An effective hybrid flower pollination and genetic algorithm for constrained optimization problems. Adv Eng Technol Appl Int J 4:27

    Google Scholar 

  • Abdel-Baset M, Hezam I (2016) A hybrid flower pollination algorithm for engineering optimization problems. Int J Comput Appl 140(12):10–23

    Google Scholar 

  • Abdel-Raouf O, Abdel-Baset M (2014) A new hybrid flower pollination algorithm for solving constrained global optimization problems. Int J Appl Oper Res Open Access J 4(2):1–13

    Google Scholar 

  • Abdel-Raouf O, Abdel-Baset M, El-Henawy I (2014a) An improved flower pollination algorithm with chaos. Int J Educ Managt Eng 4(2):1–8

    Article  Google Scholar 

  • Abdel-Raouf O, El-Henawy I, Abdel-Baset M (2014b) A novel hybrid flower pollination algorithm with chaotic harmony search for solving sudoku puzzles. Int J Mod Educ Comput Sci 6(3):38

    Article  Google Scholar 

  • Agarwal P, Mehta S (2016) Enhanced flower pollination algorithm on data clustering. Int J Comput Appl 38(2–3):144–155

    Google Scholar 

  • Alam DF, Yousri DA, Eteiba MB (2015) Flower pollination algorithm based solar PV parameter estimation. Energy Convers Manag 101:410–422

    Article  Google Scholar 

  • Alshamlan HM, Badr GH, Alohali YA (2015) Genetic bee colony (GBC) algorithm: a new gene selection method for microarray cancer classification. Comput Biol Chem 56:49–60

    Article  Google Scholar 

  • Arora JS (1989) Introduction to optimum design. McGraw-Hill, New York

    Google Scholar 

  • Azis MF, Ryanta A, Putra DFU, Fenno O (2015) Dynamic economic dispatch considering emission using multi-objective flower pollination algorithm. In: ASEAN/Asian academic society international conference proceeding series

  • Bairwa SK, Kumar P, Baranwal AK (2016) Enhancement of radiation pattern for linear antenna array using flower pollination algorithm. In: Electrical power and energy systems (ICEPES), international conference. IEEE, pp 1–4

  • Banerjee S, Chattopadhyay S (2016) Equalizer optimization using flower pollination algorithm. In: Power electronics, intelligent control and energy systems (ICPEICES), IEEE international conference. IEEE, pp 1–5‏

  • Bazant MZ (2005) 18.366 Random walks and diffusion. Springer, New York

    Google Scholar 

  • Bekdaş G, Nigdeli SM, Sayin B (2016) Constraint factor in optimization of truss structures via flower pollination algorithm. In: 14th international conference ofnumerical analysis and applied mathematics, Rhodes, Greece, pp 19–25

  • Bekdaş G, Nigdeli SM, Yang XS (2015a) Sizing optimization of truss structures using flower pollination algorithm. Appl Soft Comput 37:322–331

    Article  Google Scholar 

  • Bekdaş G, Nigdeli SM, Yang XS (2015b) Truss structure optimization using flower pollination algorithm. In: 9th European solid mechanics conference (ESMC 2015), Madrid, Spain

  • Bekdaş G, Nigdeli SM, Yang XS (2017a) Size optimization of truss structures employing flower pollination algorithm without grouping structural members. Int J Theor Appl Mech 1:269–273

  • Bekdaş G, Nigdeli SM, Yang XS (2017b) Metaheuristic based optimization for tuned mass dampers using frequency domain responses. In: Del Ser J (ed) Harmony search algorithm. ICHSA 2017. Advances in intelligent systems and computing, vol 514. Springer, Singapore

  • Belegundu AD, Arora JS (1985) A study of mathematical programming methods for structural optimization. Part I: theory. Int J Numer Methods Eng 21(9):1583–1599

    Article  MATH  Google Scholar 

  • Benkercha R, Moulahoum S, Colak I, Taghezouit B (2016) PV module parameters extraction with maximum power point estimation based on flower pollination algorithm. In: Power electronics and motion control conference (PEMC), 2016 IEEE international. IEEE, pp 442–449

  • Bensouyad M, Saidouni DE (2015) A hybrid discrete flower pollination algorithm for graph coloring problem. In Proceedings of the the international conference on engineering & MIS 2015. ACM, p 22

  • Bhatia NK, Kumar V, Rana KPS, Gupta P, Mishra P (2016) Development of a flower pollination algorithm toolkit in LabVIEW™. In: Computing for sustainable global development (INDIACom), 2016 3rd international conference. IEEE, pp 309–314

  • Bibiks K, Li JP, Hu F (2015) Discrete flower pollination algorithm for resource constrained project scheduling problem. Int J Comput Sci Inf Secur 13(7):8

    Google Scholar 

  • Binh HTT, Hanh NT, Dey N (2016) Improved cuckoo search and chaotic flower pollination optimization algorithm for maximizing area coverage in wireless sensor networks. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2823-5

    Article  Google Scholar 

  • Biswas S, Kundu S, Das S, Vasilakos AV (2013) Teaching and learning best differential evoltuion with self adaptation for real parameter optimization. In: Evolutionary computation (CEC), 2013 IEEE congress. IEEE, pp 1115–1122

  • Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 35(3):268–308

    Article  Google Scholar 

  • Blum C, Roli A (2008) Hybrid metaheuristics: an introduction. In: Hybrid metaheuristics. Springer, Berlin, pp 1–30

  • Blum C, Puchinger J, Raidl GR, Roli A (2010) A brief survey on hybrid metaheuristics. In: Proceedings of BIOMA, pp 3–18

  • Caraffini F, Neri F, Cheng J, Zhang G, Picinali L, Iacca G, Mininno E (2013) Super-fit multicriteria adaptive differential evolution. In: Evolutionary computation (CEC), 2013 IEEE congress. IEEE, pp 1678–1685

  • Caraffini F, Iacca G, Neri F, Picinali L, Mininno E (2013) A CMA-ES super-fit scheme for the re-sampled inheritance search. In: Evolutionary computation (CEC), 2013 IEEE congress. IEEE, pp 1123–1130

  • Chakraborty D, Saha S, Dutta O (2014) DE-FPA: a hybrid differential evolution-flower pollination algorithm for function minimization. In: High performance computing and applications (ICHPCA), 2014 international conference. IEEE, pp 1–6

  • Chakraborty D, Saha S, Maity S (2015) Training feedforward neural networks using hybrid flower pollination-gravitational search algorithm. In: Futuristic trends on computational analysis and knowledge management (ABLAZE), 2015 international conference. IEEE, pp 261–266

  • Chakraborty D, Saha S, Maity S (2015) Training feedforward neural networks using hybrid flower pollination-gravitational search algorithm. In Futuristic trends on computational analysis and knowledge management (ABLAZE), 2015 international conference. IEEE, pp 261–266

  • Chattopadhyay S, Banerjee S (2016) Optimum power allocation of parallel concatenated convolution turbo code using flower pollination algorithm. In: Control, instrumentation, energy & communication (CIEC), 2016 2nd international conference. IEEE, pp 516–520

  • Chen KH, Wang KJ, Wang KM, Angelia MA (2014) Applying particle swarm optimization-based decision tree classifier for cancer classification on gene expression data. Appl Soft Comput 24:773–780

    Article  Google Scholar 

  • Chiroma H, Khan A, Abubakar AI, Saadi Y, Hamza MF, Shuib L, Herawan T (2016) A new approach for forecasting OPEC petroleum consumption based on neural network train by using flower pollination algorithm. Appl Soft Comput 48:50–58

    Article  Google Scholar 

  • Črepinšek M, Liu SH, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv 45(3):35

    Article  MATH  Google Scholar 

  • Cuevas E, Cienfuegos M, Zaldívar D, Pérez-Cisneros M (2013) A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst Appl 40(16):6374–6384

    Article  Google Scholar 

  • Cuevas E, Osuna V, Oliva D (2017) Filter design. In: Evolutionary computation techniques: a comparative perspective. Springer, pp 205–222

  • Dahi ZAEM, Mezioud C, Draa A (2016) On the efficiency of the binary flower pollination algorithm: application on the antenna positioning problem. Appl Soft Comput 47:395–414

    Article  Google Scholar 

  • Das S, Suganthan PN (2010) Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Jadavpur University, Nanyang Technological University, Kolkata

    Google Scholar 

  • Dash P, Saikia LC, Sinha N (2016) Flower pollination algorithm optimized PI-PD cascade controller in automatic generation control of a multi-area power system. Int J Electr Power Energy Syst 82:19–28

    Article  Google Scholar 

  • De Castro LN, Von Zuben FJ (2000) The clonal selection algorithm with engineering applications. In: Proceedings of GECCO, vol 2000, pp 36–39

  • de Lima Júnior PCR (2008) Integration of geographic information systems, meta-heuristics and multi-criteria analysis for territories alignment. Doctoral dissertation, Universidade do Porto Portugal

  • Deb S, Goswami AK (2016) Congestion management by generator real power rescheduling using flower pollination algorithm. In: Control, instrumentation, energy & communication (CIEC), 2016 2nd international conference. IEEE, pp 437–441

  • Dorigo M (1992) Optimization, learning and natural algorithms. Ph.D. Thesis, Politecnico di Milano, Italy

  • Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66

    Article  Google Scholar 

  • Draa A (2015) On the performances of the flower pollination algorithm–Qualitative and quantitative analyses. Appl Soft Comput 34:349–371

    Article  Google Scholar 

  • Draa A, Bouzoubia S, Boukhalfa I (2015) A sinusoidal differential evolution algorithm for numerical optimisation. Appl Soft Comput 27:99–126

    Article  Google Scholar 

  • Dubey HM, Panigrahi BK, Pandit M (2014) Improved flower pollination algorithm for short term hydrothermal scheduling. In: International conference on swarm, evolutionary, and memetic computing. Springer, Cham, pp 721–737

  • Dubey HM, Pandit M, Panigrahi BK (2015a) Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch. Renew Energy 83:188–202

    Article  Google Scholar 

  • Dubey HM, Pandit M, Panigrahi BK (2015b) A biologically inspired modified flower pollination algorithm for solving economic dispatch problems in modern power systems. Cognit Comput 7(5):594–608

    Article  Google Scholar 

  • Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, vol 1, pp 39–43

  • El-henawy I, Ismail M (2014) An improved chaotic flower pollination algorithm for solving large integer programming problems. Int J Digit Content Technol Appl 8(3):72

    Google Scholar 

  • Emary E, Zawbaa HM, Hassanien AE, Tolba MF, Snášel V (2014) Retinal vessel segmentation based on flower pollination search algorithm. In: Proceedings of the fifth international conference on innovations in bio-inspired computing and applications IBICA 2014. Springer, New York, pp 93–100

  • Emary E, Zawbaa HM, Hassanien AE, Parv B (2017) Multi-objective retinal vessel localization using flower pollination search algorithm with pattern search. Adv Data Anal Classif 11(3):611–627

    Article  MathSciNet  MATH  Google Scholar 

  • Eriksson O, Friis EM, Löfgren P (2000) Seed size, fruit size, and dispersal systems in angiosperms from the early cretaceous to the late tertiary. Am Nat 156(1):47–58

    Article  Google Scholar 

  • Frankel R, Galun E (2012) Pollination mechanisms, reproduction and plant breeding, vol 2. Springer, New York

    Google Scholar 

  • Fredriksson L (2010) A brief survey of Lévy walks: with applications to probe diffusion. (Bachelor dissertation). http://www.divaportal.org/smash/get/diva2:288755/FULLTEXT02.pdf

  • Frohlich MW (2003) Opinion: an evolutionary scenario for the origin of flowers. Nat Rev Genet 4(7):559

    Article  Google Scholar 

  • Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845

    Article  MathSciNet  MATH  Google Scholar 

  • Gandomi AH, Yang XS, Talatahari S, Alavi AH (eds) (2013) Metaheuristic applications in structures and infrastructures. Newnes

  • Gao Y, Guan H, Qi Z, Hou Y, Liu L (2013) A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J Comput Syst Sci 79(8):1230–1242

    Article  MathSciNet  MATH  Google Scholar 

  • Gao ML, Zang YR, Shen J, Zhang YC, Yu DS (2016) Visual tracking based on flower pollination algorithm. In: Control conference (CCC), 2016 35th Chinese. IEEE, pp 3866–3868

  • Gautam U, Malmathanraj R, Srivastav C (2015) Simulation for path planning of autonomous underwater vehicle using flower pollination algorithm, genetic algorithm and Q-learning. In: Cognitive computing and information processing (CCIP), 2015 international conference. IEEE, pp 1–5

  • Gibbons JD, Chakraborti S (2011) Nonparametric statistical inference. Springer, Berlin, pp 977–979

    MATH  Google Scholar 

  • Glover F, McMillan C (1986) The general employee scheduling problem. An integration of MS and AI. Comput Oper Res 13(5):563–573

    Article  Google Scholar 

  • Gonidakis D (2016) Application of flower pollination algorithm to multi-objective environmental/economic dispatch. Int J Manag Sci Eng Manag 11(4):213–221

    Google Scholar 

  • Goyal S, Patterh MS (2015) Flower pollination algorithm based localization of wireless sensor network. In: Recent advances in engineering & computational sciences (RAECS), 2015 2nd international conference. IEEE, pp 1–5

  • Guo SM, Yang CC, Hsu PH, Tsai JSH (2015) Improving differential evolution with a successful-parent-selecting framework. IEEE Trans Evol Comput 19(5):717–730

    Article  Google Scholar 

  • Harikrishnan R, Jawahar Senthil Kumar V, Sridevi Ponmalar P (2015) Nature inspired flower pollen algorithm for WSN localization problem. ARPN J Eng Appl Sci 10(5):2122–2125

    Google Scholar 

  • Haupt RL, Haupt SE (2004) Practical genetic algorithms. Wiley, New York

    MATH  Google Scholar 

  • He X, Yang XS, Karamanoglu M, Zhao Y (2017) Global convergence analysis of the flower pollination algorithm: a discrete-time Markov chain approach. Proc Comput Sci 108:1354–1363

    Article  Google Scholar 

  • Hegazy O, Soliman OS, Salam MA (2015) Comparative study between FPA, BA, MCS, ABC, and PSO algorithms in training and optimizing of LS-SVM for stock market prediction. Int J Adv Comput Res 5(18):35

    Google Scholar 

  • Heng J, Wang C, Zhao X, Xiao L (2016) Research and application based on adaptive boosting strategy and modified CGFPA algorithm: a case study for wind speed forecasting. Sustainability 8(3):235

    Article  Google Scholar 

  • Hezam IM, Abdel-Baset M, Hassan BM (2016) A hybrid flower pollination algorithm with tabu search for unconstrained optimization problems. Inf Sci Lett 5(1):29–34. https://doi.org/10.18576/isl/050104

    Article  Google Scholar 

  • Hoang ND, Bui DT, Liao KW (2016) Groutability estimation of grouting processes with cement grouts using differential flower pollination optimized support vector machine. Appl Soft Comput 45:173–186

    Article  Google Scholar 

  • Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65–70

    MathSciNet  MATH  Google Scholar 

  • Huang SJ, Gu PH, Su WF, Liu XZ, Tai TY (2015) Application of flower pollination algorithm for placement of distribution transformers in a low-voltage grid. In: Industrial technology (ICIT), 2015 IEEE international conference. IEEE, pp 1280–1285

  • Huson IJ (2017) Mrmr Ba: a hybrid gene selection algorithm for cancer classification. J Theor Appl Inf Technol 95(12):2610–2618

    Google Scholar 

  • Jagatheesan K, Anand B, Samanta S, Dey N, Santhi V, Ashour AS, Balas VE (2017) Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity. Neural Comput Appl 28(1):475–488

    Article  Google Scholar 

  • Jain P, Bansal S, Singh AK, Gupta N (2015) Golomb ruler sequences optimization for FWM crosstalk reduction: multi-population hybrid flower pollination algorithm. In: Progress in electromagnetics research symposium (PIERS), Prague, Czech Republic, pp 2463–2467

  • Jamil M, Zepernick HJ (2013) Lévy flights and global optimization. In: Swarm intelligence and bio-inspired computation: theory and applications, pp 49–72

  • Jensi R, Jiji GW (2015) Hybrid data clustering approach using K-means and flower pollination algorithm. arXiv preprint arXiv:1505.03236‏

  • Jiang M, Luo YP, Yang SY (2007) Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm. Inf Proc Lett 102(1):8–16

    Article  MathSciNet  MATH  Google Scholar 

  • Kalra S, Arora S (2016) Firefly algorithm hybridized with flower pollination algorithm for multimodal functions. In: Proceedings of the international congress on information and communication technology. Springer, Singapore, pp 207–219

  • Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, vol 200, Technical report-tr06. Erciyes University, Engineering Faculty, Computer Engineering Department

  • Kaur G, Singh D, Kaur M (2013) Robust and efficient ‘RGB’ based fractal image compression: flower pollination based optimization. Int J Comput Appl 78(10):11–15

    Google Scholar 

  • Kaveh A (2017) Applications of metaheuristic optimization algorithms in civil engineering. Springer, New York

    Book  MATH  Google Scholar 

  • Kazemian M, Ramezani Y, Lucas C, Moshiri B (2006) Swarm clustering based on flowers pollination by artificial bees. In: Swarm intelligence in data mining. Springer, Berlin, pp 191–202

  • Kessaci Y (2013) Multi-criteria scheduling on clouds. Doctoral dissertation, Université des Sciences et Technologie de Lille-Lille I

  • Khalil AW (2015) An improved flower pollination algorithm for solving integer programming problems. Int J Appl Math Inf Sci 3(1):31–37

    Google Scholar 

  • Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MathSciNet  MATH  Google Scholar 

  • Ku-Mahamud KR (2015) Hybrid ant colony system and flower pollination algorithms for global optimization. In: IT in Asia (CITA), 2015 9th international conference. IEEE, pp 1–9

  • Kumar BS, Suryakalavathi M, Kumar GN (2015) Optimal power flow with static VAR compensator based on flower pollination algorithm to minimize real power losses. In Power, control, communication and computational technologies for sustainable growth (PCCCTSG), 2015 conference. IEEE, pp 112–116

  • Kusuma I, Ma’sum MA, Sanabila HS, Wisesa HA, Jatmiko W, Arymurthy AM, Wiweko B (2016) Fetal head segmentation based on Gaussian elliptical path optimize by flower pollination algorithm and cuckoo search. In: Advanced computer science and information systems (ICACSIS), 2016 international conference. IEEE, pp 564–571

  • Lakshmi D, Fathima AP, Muthu R (2016) A novel flower pollination algorithm to solve load frequency control for a hydro-thermal deregulated power system. Circuits Syst 7(04):166

    Article  Google Scholar 

  • Lenin K, Ravindhranath RB, Surya KM (2014) Shrinkage of active power loss by hybridization of flower pollination algorithm with chaotic harmony search algorithm. Control Theory Inform 4:31–38

    Google Scholar 

  • Liang JJ, Qu BY, Suganthan PN, Hernández-Díaz AG (2013) Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical Report, 201212

  • Łukasik S, Kowalski PA (2015) Study of flower pollination algorithm for continuous optimization. In: Intelligent systems’ 2014. Springer, Cham, pp 451–459

  • Mahata S, Saha SK, Kar R, Mandal D (2017) Optimal design of wideband digital integrators and differentiators using hybrid flower pollination algorithm. Soft Comput. https://doi.org/10.1007/s00500-017-2595-6

    Article  Google Scholar 

  • Mahdad B, Srairi K (2016) Security constrained optimal power flow solution using new adaptive partitioning flower pollination algorithm. Appl Soft Comput 46:501–522

    Article  Google Scholar 

  • Mantegna RN (1994) Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Phys Rev E 49(5):4677

    Article  Google Scholar 

  • Merzougui A, Labed N, Hasseine A, Bonilla-Petriciolet A, Laiadi D, Bacha O (2016) Parameter Identification in liquid–liquid equilibrium modeling of food-related thermodynamic systems using flower pollination algorithms. Open Chem Eng J 10(1):59–73

    Article  Google Scholar 

  • Metwalli MAB, Hezam I (2015) A modified flower pollination algorithm for fractional programming problems. Int J Intell Syst Appl Eng 3(3):116–123

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  • Mishra A, Deb S (2016) Assembly sequence optimization using a flower pollination algorithm-based approach. J Intell Manuf. https://doi.org/10.1007/s10845-016-1261-7

    Article  Google Scholar 

  • Mucherino A, Seref O (2007) Monkey search: a novel metaheuristic search for global optimization. In: Data mining, systems analysis and optimization in biomedicine, vol 953(1). AIP Publishing, pp 162–173

  • Nabil E (2016) A modified flower pollination algorithm for global optimization. Expert Syst Appl 57:192–203

    Article  Google Scholar 

  • Namachivayam G, Sankaralingam C, Perumal SK, Devanathan ST (2016) Reconfiguration and capacitor placement of radial distribution systems by modified flower pollination algorithm. Electric Power Compon Syst 44(13):1492–1502

    Article  Google Scholar 

  • Nasser AB, Hujainah F, Alsewari AA, Zamli KZ (2015) Sequence and sequence-less t-way test suite generation strategy based on flower pollination algorithm. In: Research and development (SCOReD), 2015 IEEE student conference. IEEE pp 676–680

  • Nasser AB, Alsewari AA, Mu’azu AA, Zamli KZ (2016) Comparative performance analysis of flower pollination algorithm and harmony search based strategies: a case study of applying interaction testing in the real world. In: Proceeding book: 2nd international conference on new directions in multidisciplinary research and practice, 12–13 May 2016, Istanbul, Turkey, p 1–5

  • Nesmachnow S, Iturriaga S, Dorronsoro B, Talbi EG, Bouvry P (2015) Metaheuristics for the virtual machine mapping problem in clouds. Informatica 26(1):111–134

    Article  Google Scholar 

  • Nigdeli SM, Bekdaş G, Yang XS (2015) Optimum structural design of pin-jointed plane frames using the flower pollination algorithm. In: 28th conference of the european chapter on combinatorial optimization (ECCO XXVIII-2015), Catania, Italy, pp 28–30

  • Nigdeli SM, Bekdaş G, Yang XS (2016) Application of the flower pollination algorithm in structural engineering. In: Metaheuristics and optimization in civil engineering. Springer, pp 25–42

  • Nigdeli SM, Bekdas G, Yang XS (2017a) Optimum tuning of mass dampers for seismic structures using flower pollination algorithm. Int J Theor Appl Mech 264–268

  • Nigdeli SM, Bekdas G, Yang XS (2017b) Optimum tuning of mass dampers by using a hybrid method using harmony search and flower pollination algorithm. In: Harmony search algorithm. Advances in intelligent systems and computing, vol 514. Springer, pp 222–231

  • Ochoa A, González S, Margain L, Padilla T, Castillo O, Melín P (2014) Implementing flower multi-objective algorithm for selection of university academic credits. In: Nature and biologically inspired COMPUTING (NaBIC), 2014 sixth world congress. IEEE, pp 7–11

  • Oda ES, Abdelsalam AA, Abdel-Wahab MN, El-Saadawi MM (2015) Distributed generations planning using flower pollination algorithm for enhancing distribution system voltage stability. Ain Shams Eng J 8(4):593–603. https://doi.org/10.1016/j.asej.2015.12.001

    Article  Google Scholar 

  • Ouadfel S, Taleb-Ahmed A (2016) Social spiders optimization and flower pollination algorithm for multilevel image thresholding: a performance study. Expert Syst Appl 55:566–584

    Article  Google Scholar 

  • Pambudy MMM, Hadi SP, Ali HR (2014) Flower pollination algorithm for optimal control in multi-machine system with GUPFC. In: Information technology and electrical engineering (ICITEE), 2014 6th international conference. IEEE, pp 1–6

  • Pandya KS, Dabhi DA, Joshi SK (2015) Comparative study of bat & flower pollination optimization algorithms in highly stressed large power system. In: Power systems conference (PSC), 2015 Clemson University. IEEE, pp 1–5

  • Pathak P, Mahajan K (2015) A pollination based optimization for load balancing task scheduling in cloud computing. Int J Adv Res Comput Sci 6(7):7–12

    Google Scholar 

  • Platt GM (2014) Application of the flower pollination algorithm in nonlinear algebraic systems with multiple solutions. Eng Optim 2014:117

    Google Scholar 

  • Poikolainen I, Neri F (2013) Differential evolution with concurrent fitness based local search. In: Evolutionary computation (CEC), 2013 IEEE congress. IEEE, pp 384–391

  • Pop CB, Chifu VR, Salomie I, Racz DS, Bonta RM (2017) Hybridization of the flower pollination algorithm—a case study in the problem of generating healthy nutritional meals for older adults. In: Nature-inspired computing and optimization. Springer, New York, pp 151–183

  • Prathiba R, Moses MB, Sakthivel S (2014) Flower pollination algorithm applied for different economic load dispatch problems. Int J Eng Technol 6(2):1009–1016

    Google Scholar 

  • Pravallika DL, Rao BV (2016) Flower pollination algorithm based optimal setting of TCSC to minimize the transmission line losses in the power system. Proc Comput Sci 92:30–35

    Article  Google Scholar 

  • Putra AP, Anggorowati MA (2016) MetaheuristicFPA: an implementation of flower pollination algorithm in R. Retrieved 25 July 2017. https://rdrr.io/cran/MetaheuristicFPA/

  • Putra PH, Saputra TA (2016) Modified flower pollination algorithm for nonsmooth and multiple fuel options economic dispatch. In: Information technology and electrical engineering (ICITEE), 2016 8th international conference. IEEE, pp 1–5

  • Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417

    Article  Google Scholar 

  • Raidl GR (2006) A unified view on hybrid metaheuristics. In: International workshop on hybrid metaheuristics. Springer, pp 1–12

  • Ram JP, Rajasekar N (2017a) A new global maximum power point tracking technique for solar photovoltaic (PV) system under partial shading conditions (PSC). Energy 118:512–525

    Article  Google Scholar 

  • Ram JP, Rajasekar N (2017b) A novel flower pollination based global maximum power point method for solar maximum power point tracking. IEEE Trans Power Electron 32(11):8486–8499

    Article  Google Scholar 

  • Ram JP, Babu TS, Dragicevic T, Rajasekar N (2017) A new hybrid bee pollinator flower pollination algorithm for solar PV parameter estimation. Energy Convers Manag 135:463–476

    Article  Google Scholar 

  • Ramadas M, Kumar S (2016) An efficient hybrid approach using differential evolution and flower pollination algorithm. In: Cloud system and big data engineering (confluence), 2016 6th international conference. IEEE, pp 59–64

  • Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  MATH  Google Scholar 

  • Rathasamuth W, Nootyaskool S (2016) Comparison solving discrete space on flower pollination algorithm, PSO and GA. In: Knowledge and smart technology (KST), 2016 8th international conference. IEEE, pp 18–21

  • Reddy PDP, Reddy VV, Manohar TG (2016) Application of flower pollination algorithm for optimal placement and sizing of distributed generation in distribution systems. J Electr Syst Inf Technol 3(1):14–22

    Google Scholar 

  • Regalado JA, Emilio BE, Cuevas E (2015) Optimal power flow solution using modified flower pollination algorithm. In: Power, electronics and computing (ROPEC), 2015 IEEE international autumn meeting. IEEE, pp 1–6‏

  • Ripley BD (2001) The R project in statistical computing. MSOR Connect Newslett LTSN Maths Stats OR Netw 1(1):23–25

    Google Scholar 

  • Rodrigues D, Yang XS, De Souza AN, Papa JP (2015) Binary flower pollination algorithm and its application to feature selection. In: Recent advances in swarm intelligence and evolutionary computation. Springer, pp 85–100

  • Rodrigues D, Silva GF, Papa JP, Marana AN, Yang XS (2016) EEG-based person identification through binary flower pollination algorithm. Expert Syst Appl 62:81–90

    Article  Google Scholar 

  • Sakib N, Kabir MWU, Subbir M, Alam S (2014) A comparative study of flower pollination algorithm and bat algorithm on continuous optimization problems. Int J Appl Inf Syst 7(9):13–19

    Google Scholar 

  • Sakthivel S, Manopriya P, Venus S, Ranjitha S, Subhashini R (2016) Optimal reactive power dispatch problem solved by using flower pollination algorithm. Int J Appl Eng Res 11(6):4387–4391

    Google Scholar 

  • Saleh H (2002) Metaheuristics for optimising the use of geographic information systems. In: Proceedings of the EuIo—conference of the science for water policy (SWAP): the implications of the water framework directive, East Anglia. UK

  • Saleh H. (2003). Metaheuristics for optimizing the water framework directive based geographic information systems. In: Science for water policy (SWAP). The EC, Research Directorate-General, pp 195–215

  • Salgotra R, Singh U (2016) A novel bat flower pollination algorithm for synthesis of linear antenna arrays. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2833-3

    Article  Google Scholar 

  • Sampson JR (1976) Adaptation in natural and artificial systems (John H. Holland). SIAM Rev 18(3):529–530

    Article  MathSciNet  Google Scholar 

  • Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Article  Google Scholar 

  • Saxena P, Kothari A (2016) Linear antenna array optimization using flower pollination algorithm. SpringerPlus 5(1):306

    Article  Google Scholar 

  • Sayed SAF, Nabil E, Badr A (2016) A binary clonal flower pollination algorithm for feature selection. Pattern Recogn Lett 77:21–27

    Article  Google Scholar 

  • Sharawi M, Emary E, Saroit IA, El-Mahdy H (2014) Flower pollination optimization algorithm for wireless sensor network lifetime global optimization. Int J Soft Comput Eng 4(3):54–59

    Google Scholar 

  • Shilaja C, Ravi K (2016) Optimal line flow in conventional power system using euclidean affine flower pollination algorithm. Int J Renew Energy Res 6(1)

  • Shilaja C, Ravi K (2017) Optimization of emission/economic dispatch using euclidean affine flower pollination algorithm (eFPA) and binary FPA (BFPA) in solar photo voltaic generation. Renew Energy 107:550–566

    Article  Google Scholar 

  • Shlesinger MF, Zaslavsky GM, Frisch U (1995) Lévy flights and related topics in physics. Lect Notes Phys 450:52

    MATH  Google Scholar 

  • Singh U, Salgotra R (2018) Synthesis of linear antenna array using flower pollination algorithm. Neural Comput Appl 29(2):435–445

    Article  Google Scholar 

  • Soto R, Crawford B, Olivares R, De Conti M, Rubio R, Almonacid B, Niklander S (2016) Resolving the manufacturing cell design problem using the flower pollination algorithm. In: International workshop on multi-disciplinary trends in artificial intelligence. Springer, pp 184–195

  • Sudabattula S, Kowsalya M (2016) Distributed energy resources allocation using flower pollination algorithm in radial distribution systems. Energy Proc 103:76–81

    Article  Google Scholar 

  • Sutton AM, Lunacek M, Whitley LD (2007) Differential evolution and non-separability: using selective pressure to focus search. In: Proceedings of the 9th annual conference on Genetic and evolutionary computation. ACM, pp 1428–1435

  • Tahani M, Babayan N, Pouyaei A (2015) Optimization of PV/wind/battery stand-alone system, using hybrid FPA/SA algorithm and CFD simulation, case study: Tehran. Energy Convers Manag 106:644–659

    Article  Google Scholar 

  • Takhtajan A (2009) Flowering plants. Springer, New York

    Book  Google Scholar 

  • Tamilselvan V, Jayabarathi T (2016) Multi objective flower pollination algorithm for solving capacitor placement in radial distribution system using data structure load flow analysis. Arch Electric Eng 65(2):203–220

    Article  Google Scholar 

  • Tanabe R, Fukunaga A (2013) Success-history based parameter adaptation for differential evolution. In: Evolutionary computation (CEC), 2013 IEEE congress. IEEE, pp 71–78

  • Teodorović D, Dell’Orco M (2005) Bee colony optimization—a cooperative learning approach to complex transportation problems. In: Advanced OR and AI methods in transportation: proceedings of 16th mini–EURO conference and 10th meeting of EWGT (13–16 September 2005). Publishing House of the Polish Operational and System Research, Poznan, pp 51–60

  • Trivedi IN, Purani SV, Jangir PK (2015) Optimized over-current relay coordination using flower pollination algorithm. In: Advance computing conference (IACC), 2015 IEEE international conference. IEEE, pp 72–77

  • Tsai PW, Nguyen TT, Pan JS, Dao TK, Zheng WM (2017) A parallel optimization algorithm based on communication strategy of pollens and agents. In: Advances in intelligent information hiding and multimedia signal processing: proceeding of the twelfth international conference on intelligent information hiding and multimedia signal processing, Nov., 21–23, 2016, Kaohsiung, Taiwan, vol 2. Springer, pp 315–324

  • Tvrdík J, Poláková R (2013) Competitive differential evolution applied to CEC 2013 problems. In: Evolutionary computation (CEC), 2013 IEEE congress. IEEE, pp 1651–1657

  • Valenzuela L, Valdez F, Melin P (2017) Flower pollination algorithm with fuzzy approach for solving optimization problems. In: Nature-inspired design of hybrid intelligent systems. Springer, New York, pp 357–369

  • Velamuri S, Sreejith S, Ponnambalam P (2016) Static economic dispatch incorporating wind farm using flower pollination algorithm. Perspect Sci 8:260–262

    Article  Google Scholar 

  • Verma S, Mukherjee V (2016) A novel flower pollination algorithm for congestion management in electricity market. In: Recent advances in information technology (RAIT), 2016 3rd international conference. IEEE, pp 203–208

  • Vijayaraj S, Santhi RK (2016) Multi-area economic dispatch using flower pollination algorithm. In: Electrical, electronics, and optimization techniques (ICEEOT), international conference. IEEE, pp 4355–4360

  • Walton S, Hassan O, Morgan K, Brown MR (2011) Modified cuckoo search: a new gradient free optimisation algorithm. Chaos Solitons Fractals 44(9):710–718

    Article  Google Scholar 

  • Wang R, Zhou Y (2014) Flower pollination algorithmwith dimension by dimension improvement. Math Probl Eng 2014(2014):481791. https://doi.org/10.1155/2014/481791

    Article  Google Scholar 

  • Wang F, He XS, Wang Y, Yang SM (2012) Markov model and convergence analysis of cuckoo search algorithm. Comput Eng 38(11):180–185

    Google Scholar 

  • Wang R, Zhou Y, Zhao C, Wu H (2015) A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation. BioMed Mater Eng 26(s1):S1345–S1351

    Google Scholar 

  • Wang R, Zhou Y, Qiao S, Huang K (2016) Flower pollination algorithm with bee pollinator for cluster analysis. Inf Process Lett 116(1):1–14

    Article  Google Scholar 

  • Widihananta M (2014). FPA C++ code. Retrieved 25 July 2017. https://github.com/atnanahidiw/fpa

  • Wie BC, Chai WY (2004) An intelligent GIS-based spatial zoning system with multiobjective hybrid metaheuristic method. In: Orchard B, Yang C, Ali M (eds) Innovations in applied artificial intelligence. IEA/AIE 2004. Lecture notes in computer science, vol 3029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24677-0_79

  • Xu S, Wang Y (2017) Parameter estimation of photovoltaic modules using a hybrid flower pollination algorithm. Energy Convers Manag 144:53–68

    Article  Google Scholar 

  • Xu S, Wang Y, Huang F (2017a) Optimization of multi-pass turning parameters through an improved flower pollination algorithm. Int J Adv Manuf Technol 89(1–4):503–514

    Article  Google Scholar 

  • Xu S, Wang Y, Liu X (2017b) Parameter estimation for chaotic systems via a hybrid flower pollination algorithm. Neural Comput Appl. https://doi.org/10.1007/s00521-017-2890-2

    Article  Google Scholar 

  • Yamany W, Zawbaa HM, Emary E, Hassanien AE (2015) Attribute reduction approach based on modified flower pollination algorithm. In: Fuzzy systems (FUZZ-IEEE), 2015 IEEE international conference. IEEE, pp 1–7

  • Yang XS (2008) Firefly algorithm (chapter 8). Nature-inspired metaheuristic algorithms. Luniver Press, Bristol

    Google Scholar 

  • 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 (2012) Flower pollination algorithm for global optimization. In: Unconventional computation and natural computation. Springer, Berlin, pp 240–249

  • Yang XE (2016) Flower pollination algorithm by Xin-She Yang in Java. Retrieved 25 July 2017. https://github.com/fum968/FPA?utm_source=itdadao&utm_medium=referral

  • Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Nature and biologically inspired computing, 2009. NaBIC 2009. World Congress. IEEE, pp 210–214

  • Yang X-S, Suash D (2010) Eagle strategy using Lévy walk and firefly algorithms for stochastic optimization. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, pp 101–111

  • Yang XS, Gandomi AH, Talatahari S, Alavi AH (Eds) (2012) Metaheuristics in water, geotechnical and transport engineering. Newnes

  • Yang XS, Karamanoglu M, He X (2013a) Multi-objective flower algorithm for optimization. Proc Comput Sci 18:861–868

    Article  Google Scholar 

  • Yang XS, Deb S, He X (2013) Eagle strategy with flower algorithm. In: Advances in computing, communications and informatics (ICACCI), 2013 international conference. IEEE, pp 1213–1217

  • Yang XS, Karamanoglu M, He X (2014) Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim 46(9):1222–1237

    Article  MathSciNet  Google Scholar 

  • Yusoh ZIM, Tang M (2012) Composite saas placement and resource optimization in cloud computing using evolutionary algorithms. In: Cloud computing (CLOUD), 2012 IEEE 5th international conference. IEEE, pp 590–597

  • Zainudin A, Sia CK, Ong P, Narong OLC, Nor NHM (2017) Taguchi design and flower pollination algorithm application to optimize the shrinkage of triaxial porcelain containing palm oil fuel ash. In: IOP conference series: materials science and engineering, vol 165(1). IOP Publishing, p 012036

  • Zawbaa HM, Hassanien AE, Emary E, Yamany W, Parv B (2015) Hybrid flower pollination algorithm with rough sets for feature selection. In: Computer engineering conference (ICENCO), 2015 11th international conference. IEEE, pp 278–283

  • Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958

    Article  Google Scholar 

  • Zhao C, Zhou Y (2016) A complex encoding flower pollination algorithm for global numerical optimization. In: International conference on intelligent computing. Springer, pp 667–678

  • Zhou Y, Wang R (2016) An improved flower pollination algorithm for optimal unmanned undersea vehicle path planning problem. Int J Pattern Recognit Artif Intell 30(04):1659010

    Article  Google Scholar 

  • Zhou Y, Zhang S, Luo Q, Wen C (2016a) Using flower pollination algorithm and atomic potential function for shape matching. Neural Comput Appl 29(6):21–40. https://doi.org/10.1007/s00521-016-2524-0

    Article  Google Scholar 

  • Zhou Y, Wang R, Luo Q (2016b) Elite opposition-based flower pollination algorithm. Neurocomputing 188:294–310

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Operations Research, Faculty of Computers and Informatics, Zagazig University, El-Zera Square, Zagazig, Sharqiyah, Egypt

    Mohamed Abdel-Basset & Laila A. Shawky

Authors
  1. Mohamed Abdel-Basset
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laila A. Shawky
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed Abdel-Basset.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdel-Basset, M., Shawky, L.A. Flower pollination algorithm: a comprehensive review. Artif Intell Rev 52, 2533–2557 (2019). https://doi.org/10.1007/s10462-018-9624-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-018-9624-4

Keywords

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy