Abstract
Flower pollination algorithm (FPA) is a nature-inspired meta-heuristics to handle a large scale optimization process. This paper reviews the previous studies on the application of FPA, modified FPA and hybrid FPA for solving optimization problems. The effectiveness of FPA for solving the optimization problems are highlighted and discussed. The improvement aspects include local and global search strategies and the quality of the solutions. The measured enhancements in FPA are based on various research domains. The results of review indicate the capability of the enhanced and hybrid FPA for solving optimization problems in variety of applications and outperformed the results of other established optimization techniques.
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References
Abdel-Baset M, Hezam IM (2015) An effective hybrid flower pollination and genetic algorithm for constrained optimization problems. Adv Eng Technol Appl Int J 4:27–27
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
Abdel-Raouf O, Abdel-Baset M, El-henawy I (2014a) An improved flower pollination algorithm with chaos. Int J Educ Manag Eng (IJEME) 4(2):1
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):38–44
Abedinia O, Amjady N, Ghasemi A (2016) A new metaheuristic algorithm based on shark smell optimization. Complexity 21(5):97–116
Abedinpourshotorban H, Shamsuddin SM, Beheshti Z, Jawawi DN (2016) Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evol Comput 26:8–22
Asrari A, Wu TX, Ramos B (2017) A hybrid algorithm for short-term solar power prediction-sunshine state case study. IEEE Trans Sustain Energy 8(2):582–591
Banerjee S, Chattopadhyay S (2017) Power optimization of three-dimensional turbo code using a novel modified symbiotic organism search (MSOS) algorithm. Wirel Pers Commun 92(3):941–968
Bao Z, Zhou Y, Li L, Ma M (2015) A hybrid global optimization algorithm based on wind driven optimization and differential evolution. Math Probl Eng. doi:10.1155/2015/389630
Bensouyad M, Saidouni D (2015) A discrete flower pollination algorithm for graph coloring problem. In: 2015 IEEE 2nd international conference on cybernetics (CYBCONF). IEEE, pp 151–155
Bouchekara HREH, Chaib AE, Abido MA, El-Sehiemy RA (2016) Optimal power flow using an Improved Colliding Bodies Optimization algorithm. Appl Soft Comput 42:119–131
Chakraborty D, Saha S, Dutta O (2014) DE-FPA: a hybrid differential evolution-flower pollination algorithm for function minimization. In: 2014 International conference on high performance computing and applications (ICHPCA). IEEE, pp 1–6
Chakraborty D, Saha S, Maity S (2015) Training feedforward neural networks using hybrid flower pollination-gravitational search algorithm. In: 2015 International conference on futuristic trends on computational analysis and knowledge management (ABLAZE). IEEE, pp 261–266
Chen B, Zeng W, Lin Y, Zhang D (2015) A new local search-based multiobjective optimization algorithm. IEEE Trans Evol Comput 19(1):50–73
Cheng MY, Prayogo D (2017) A novel fuzzy adaptive teaching-learning-based optimization (FATLBO) for solving structural optimization problems. Eng Comput 33(1):55–69
Cuevas E, Gálvez J, Hinojosa S, Avalos O, Zaldívar D, Pérez-Cisneros M (2014) A comparison of evolutionary computation techniques for IIR model identification. J Appl Math 2014:1–9
De Vincenzo I, Giannoccaro I, Carbone G (2016) The human group optimizer (HGO): mimicking the collective intelligence of human groups as an optimization tool for combinatorial problems. arXiv preprint arXiv:1608.01495
Doğan B, Ölmez T (2015) A new metaheuristic for numerical function optimization: Vortex Search algorithm. Inf Sci 293:125–145
Dubey HM, Pandit M, Panigrahi BK (2015a) A biologically inspired modified flower pollination algorithm for solving economic dispatch problems in modern power systems. Cogn Comput 7(5):594–608
Dubey HM, Pandit M, Panigrahi BK (2015b) Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch. Renew Energy 83:188–202
Ebrahimi A, Khamehchi E (2016) Sperm whale algorithm: an effective metaheuristic algorithm for production optimization problems. J Nat Gas Sci Eng 29:211–222
El Hassani H, Benkachcha S, Benhra J (2017) New genetic operator (jump crossover) for the traveling salesman problem. In: Nature-inspired computing: concepts, methodologies, tools, and applications. IGI Global, pp 1739–1752
El-Abd M (2017) Global-best brain storm optimization algorithm. Swarm Evol Comput
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, pp 93–100
Geem ZW (2007) Harmony search algorithm for solving sudoku. In: International conference on knowledge-based intelligent information and engineering systems. Springer, Berlin, pp 371–378
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68
Gonçalves MS, Lopez RH, Miguel LFF (2015) Search group algorithm: a new metaheuristic method for the optimization of truss structures. Comput Struct 153:165–184
Haghbayan P, Nezamabadi-pour H, Kamyab S (2017) A niche GSA method with nearest neighbor scheme for multimodal optimization. Swarm Evol Comput 3:78–92
Hasançebi O, Azad SK (2015) Adaptive dimensional search: a new metaheuristic algorithm for discrete truss sizing optimization. Comput Struct 154:1–16
Hasanipanah M, Shahnazar A, Amnieh HB, Armaghani DJ (2017) Prediction of air-overpressure caused by mine blasting using a new hybrid PSO-SVR model. Eng Comput 33(1):23–31
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
Huang F, Wang L, Yang C (2016) A new improved artificial bee colony algorithm for ship hull form optimization. Eng Optim 48(4):672–686
James JQ, Li VO (2015) A social spider algorithm for global optimization. Appl Soft Comput 30:614–627
Jayaprakasam S, Rahim SKA, Leow CY (2015) PSOGSA-explore: a new hybrid metaheuristic approach for beampattern optimization in collaborative beamforming. Appl Soft Comput 30:229–237
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459–471
Karami M, Moosavinia A, Ehsanian M, Teshnelab M (2015) A new evolutionary optimization algorithm inspired by plant life cycle. In: 2015 23rd Iranian conference on electrical engineering (ICEE). IEEE, pp 573–577
Kaur G, Singh D, Kaur M (2013) Robust and efficient ‘RGB’ based fractal image compression: flower pollination based optimization. Proc Int J Comput Appl 78(10):11–15
Kaveh A, Bakhshpoori T (2016) A new metaheuristic for continuous structural optimization: water evaporation optimization. Struct Multidiscipl Optim 54(1):23–43
Kaveh A, Ghobadi M (2017) A multistage algorithm for blood banking supply chain allocation problem. Int J Civ Eng 15(1):103–112
Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 113:1–130
Khalil AW (2015) An improved flower pollination algorithm for solving integer programming problems. Int J Appl Math Inf Sci 3(1):31–37
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680
Koupaei JA, Hosseini SMM, Ghaini FM (2016) A new optimization algorithm based on chaotic maps and golden section search method. Eng Appl Artif Intell 50:201–214
Ku-Mahamud KR (2015) Hybrid ant colony system and flower pollination algorithms for global optimization. In: 2015 9th International conference on IT in Asia (CITA). IEEE, pp 1–9
Kumar AS, Giridhar AV (2014, October) A new meta heuristic algorithm based shunt capacitive compensation for power loss reduction on radial distribution system. Int J Eng Res Technol 3(10)
Lenin K (2014) Shrinkage of active power loss by hybridization of flower pollination algorithm with chaotic harmony search algorithm. Control Theory Inform 4(8):31–38
Lenin K, Reddy BR, Kalavathi DMS (2014) A chaotic particle swarm optimization (CPSO) algorithm for solving optimal reactive power dispatch problem. Indust Eng Lett 4(31):11–17
Li Q, Chen H, Huang H, Zhao X, Cai Z, Liu Tong C, Liu W, Tian X (2017a) An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis. Comput Math Methods Med 2017:1–15
Li L, Yevseyeva I, Basto-Fernandes V, Trautmann H, Jing N, Emmerich M (2017b) Building and using an ontology of preference-based multiobjective evolutionary algorithms. International conference on evolutionary multi-criterion optimization. Springer, Cham, pp 406–421
Liang YC, Cuevas Juarez JR (2016) A novel metaheuristic for continuous optimization problems: virus optimization algorithm. Eng Optim 48(1):73–93
Lu C, Gao L, Li X, Xiao S (2017) A hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industry. Eng Appl Artif Intell 57:61–79
Marinakis Y, Migdalas A, Sifaleras A (2017) A hybrid particle swarm optimization-variable neighborhood search algorithm for constrained shortest path problems. Eur J Oper Res 261(3):819–834
Medjahed SA, Saadi TA, Benyettou A, Ouali M (2016) Gray Wolf Optimizer for hyperspectral band selection. Appl Soft Comput 40:178–186
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513
Mousavirad SJ, Ebrahimpour-Komleh H (2017) Human mental search: a new population-based metaheuristic optimization algorithm. Appl Intell 47(3):850–887
Muthiah-Nakarajan V, Noel MM (2016) Galactic swarm optimization: a new global optimization metaheuristic inspired by galactic motion. Appl Soft Comput 38:771–787
Ochoa A, Gonzalez S, Margain L, Padilla T, Castillo O, Melin P (2014) Implementing flower multi-objective algorithm for selection of university academic credits. In: 2014 Sixth world congress on nature and biologically inspired computing (NaBIC). IEEE, pp 7–11
Pambudy M, Musofa M, Hadi SP, Ali HR (2014) Flower pollination algorithm for optimal control in multi-machine system with GUPFC. In: 2014 6th International conference on information technology and electrical engineering (ICITEE). IEEE, pp 1–6
Patel VK, Savsani VJ (2015) Heat transfer search (HTS): a novel optimization algorithm. Inf Sci 324:217–246
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 (pp. 151–183). Springer International Publishing
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
Rao RV, Waghmare GG (2017) A new optimization algorithm for solving complex constrained design optimization problems. Eng Optim 49(1):60–83
Razmjooy N, Khalilpour M, Ramezani M (2016) A new meta-heuristic optimization algorithm inspired by FIFA world cup competitions: theory and its application in PID designing for AVR system. J Control Autom Electr Syst 27(4):419–440
Sadollah A, Eskandar H, Bahreininejad A, Kim JH (2015) Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems. Appl Soft Comput 30:58–71
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 Soft Comput Eng 4(3):13–19
Salmani MH, Eshghi K (2017) A metaheuristic algorithm based on chemotherapy science: CSA. J Optim 2017:1–13
Sarakhsi MK, Ghomi SF, Karimi B (2016) A new hybrid algorithm of scatter search and Nelder-Mead algorithms to optimize joint economic lot sizing problem. J Comput Appl Math 292:387–401
Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47
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
Sun Y, Huang Z, Chen, Y (2014) ELA: a new swarm intelligence algorithm. In: 2014 International conference on progress in informatics and computing (PIC). IEEE, pp 109–113
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
Tilahun SL, Ong HC (2015) Prey-predator algorithm: a new metaheuristic algorithm for optimization problems. Int J Inf Technol Decis Mak 14(06):1331–1352
Valipour K, Ghasemi A (2017) Using a new modified harmony search algorithm to solve multi-objective reactive power dispatch in deterministic and stochastic models. J AI Data Min 5(1):89–100
Wang GG (2016) Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memet Comput 1–14
Wang R, Zhou Y (2014) Flower pollination algorithm with dimension by dimension improvement. Math Probl Eng 2014:1–9
Wang GG, Deb S, Coelho LDS (2015a) Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Int J Bio-Inspired Comput 1–14
Wang R, Zhou Y, Zhao C, Wu H (2015b) A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation. Bio-Med Mater Eng 26(s1):S1345–S1351
Wang GG, Deb S, Gao XZ, Coelho LDS (2016a) A new metaheuristic optimisation algorithm motivated by elephant herding behaviour. Int J Bio-Inspired Comput 8(6):394–409
Wang GG, Gandomi AH, Yang XS, Alavi AH (2016b) A new hybrid method based on krill herd and cuckoo search for global optimisation tasks. Int J Bio-Inspired Comput 8(5):286–299
Wang GG, Gandomi AH, Zhao X, Chu HCE (2016c) Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft Comput 20(1):273–285
Wang R, Zhou Y, Qiao S, Huang K (2016d) Flower pollination algorithm with bee pollinator for cluster analysis. Inf Process Lett 116(1):1–14
Yang XS (2008) Firefly algorithm (chapter 8). In: Nature-inspired metaheuristic algorithms. Luniver Press
Yang XS, Deb S (2009, December) Cuckoo search via Lévy flights. In: Nature & biologically inspired computing, 2009. NaBIC 2009. World Congress on. IEEE, pp 210–214
Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver Press, Beckington
Yang XS (2012) Flower pollination algorithm for global optimization. Unconventional computation and natural computation. Springer, Berlin, pp 240–249
Yang XS, Deb S, He X (2013a) Eagle strategy with flower algorithm. In: 2013 International conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 1213–1217
Yang XS, Karamanoglu M, He X (2013b) Multi-objective flower algorithm for optimization. Procedia Comput Sci 18:861–868
Yang XS, Karamanoglu M, He X (2014) Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim 46(9):1222–1237
Yazdani M, Jolai F (2016) Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J Comput Des Eng 3(1):24–36
Zawbaa HM, Hassanien AE, Emary E, Yamany W, Parv B (2015) Hybrid flower pollination algorithm with rough sets for feature selection. In: 2015 11th International on computer engineering conference (ICENCO). IEEE, pp 278–283
Zhang B, Zheng YJ, Zhang MX, Chen SY (2017) Fireworks algorithm with enhanced fireworks interaction. IEEE/ACM Trans Comput Biol Bioinform 14(1):42–55
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
Zheng YJ (2015) Water wave optimization: a new nature-inspired metaheuristic. Comput Oper Res 55:1–11
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
Zhou Y, Wang R, Luo Q (2016) Elite opposition-based flower pollination algorithm. Neurocomputing 188:294–310
Acknowledgements
Special appreciation to reviewer(s) for useful advices and comments. The authors greatly acknowledge the Soft Computing Research Group (SCRG), Research Management Centre (RMC) UTM and Ministry of Higher Education Malaysia (MOHE) for financial support through the Fundamental Research Grant Scheme (FRGS) Vot No. Q.J130000.2528.11H72
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Lazim, D., Zain, A.M., Bahari, M. et al. Review of modified and hybrid flower pollination algorithms for solving optimization problems. Artif Intell Rev 52, 1547–1577 (2019). https://doi.org/10.1007/s10462-017-9580-4
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DOI: https://doi.org/10.1007/s10462-017-9580-4