Skip to main content
SpringerLink
Log in

A review of artificial fish swarm algorithms: recent advances and applications

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming and following behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the continuous AFSA, encompassing the original ASFA, its improvements and hybrid models, as well as their associated applications. We focus on articles published in high-quality journals since 2013. Our review provides insights into AFSA parameters modifications, procedure and sub-functions. The main reasons for these enhancements and the comparison results with other hybrid methods are discussed. In addition, hybrid, multi-objective and dynamic AFSA models that have been proposed to solve continuous optimization problems are elucidated. We also analyse possible AFSA enhancements and highlight future research directions for advancing AFSA-based models.

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

Access this article

Log in via an institution

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
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Al-Rifaie MM, Aber A, Hemanth DJ (2015) Deploying swarm intelligence in medical imaging identifying metastasis, micro-calcifications and brain image segmentation. IET Syst Biol 9(6):234–244

    Article  Google Scholar 

  • Alkeshuosh AH, Moghadam MZ, Mansoori IA, Abdar M (2017) Using PSO algorithm for producing best rules in diagnosis of heart disease. In: International conference on computer and applications (ICCA), pp 306–311

  • Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12

    Article  Google Scholar 

  • Babaee Tirkolaee E, Goli A, Weber GW (2020) Fuzzy mathematical programming and self-adaptive artificial fish swarm algorithm for just-in-time energy-aware flow shop scheduling problem with outsourcing option. IEEE Trans Fuzzy Syst 28(11):2772–2783

    Article  Google Scholar 

  • Bastos Filho CJ, de Lima Neto FB, Lins AJ, Nascimento AI, Lima MP (2008) A novel search algorithm based on fish school behavior. In: IEEE International conference on systems, man and cybernetics, pp 2646–2651

  • Binghui Y, Xiaohui Y, Jinwen W, Xianzhang Q (2006) A random perturbation particle swarm optimization algorithm. Comput Eng 32(12):189–190

    Google Scholar 

  • Blackwell T, Branke J (2006) Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Trans Evolut Comput 10(4):459–472

    Article  Google Scholar 

  • Blum C, Li X (2008) Swarm intelligence in optimization. In: Swarm intelligence, pp. 43–85. Springer

  • Cai Y (2010) Artificial fish school algorithm applied in a combinatorial optimization problem. Int J Intell Syst Appl 2(1):37

    Google Scholar 

  • Cao J, Zhao X, Li Z, Liu W, Gu H (2017) Modified artificial fish school algorithm for free space optical communication with sensor-less adaptive optics system. J Korean Phys Soc 71(10):636–646

    Article  Google Scholar 

  • Chen L, Zhao X (2016) An improved power control AFSA for minimum interference to primary users in cognitive radio networks. Wirel Personal Commun 87(1):293–311

    Article  Google Scholar 

  • Chen W, Feng YZ, Jia GF, Zhao HT (2018) Application of artificial fish swarm algorithm for synchronous selection of wavelengths and spectral pretreatment methods in spectrometric analysis of beef adulteration. Food Anal Methods 11(8):2229–2236

    Article  Google Scholar 

  • Cheng M, Xiang M (2017) Parameter estimation of a composite production function model based on improved artificial fish swarm algorithm and model application. Commun Stat-Simul Comput 46(10):8218–8232

    Article  MathSciNet  MATH  Google Scholar 

  • Cheng Y, Jiang M, Yuan D (2009) Novel clustering algorithms based on improved artificial fish swarm algorithm. In: IEEE international conference on fuzzy systems and knowledge discovery, vol 3, pp 141–145

  • Cheng Z, Lu Z (2018) Research on the PID control of the ESP system of tractor based on improved AFSA and improved SA. Comput Electron Agric 148:142–147

    Article  Google Scholar 

  • Crepinsek M, Mernik M, Liu SH (2011) Analysis of exploration and exploitation in evolutionary algorithms by ancestry trees. Int J Innovat Comput Appl 3(1):11–19

    Article  MATH  Google Scholar 

  • DaWei W, Changliang W (2015) Wireless sensor networks coverage optimization based on improved AFSA algorithm. Int J Future Generat Commun Network 8(1):99–108

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Du C, Sun X, Zhou J, Dai Z, Yin D (2018) Precision distribution method of navigation system based on improved artificial fish swarm algorithm. In: 2018 10th international conference on intelligent human-machine systems and cybernetics (IHMSC), vol 02, pp 329–334

  • Duan Q, Mao M, Duan P, Hu B (2016) An improved artificial fish swarm algorithm optimized by particle swarm optimization algorithm with extended memory. Kybernetes 45(2):210–222

    Article  Google Scholar 

  • Duan QC (2011) Simulation analysis of particle swarm optimization algorithm with extended memory. Control Dec 26:25

    Google Scholar 

  • El-Said SA (2015) Image quantization using improved artificial fish swarm algorithm. Soft Comput 19(9):2667–2679

    Article  Google Scholar 

  • Fang N, Zhou J, Zhang R, Liu Y, Zhang Y (2014) A hybrid of real coded genetic algorithm and artificial fish swarm algorithm for short-term optimal hydrothermal scheduling. Int J Electr Power Energy Syst 62:617–629

    Article  Google Scholar 

  • Fang Z, Hu L, Qin L, Mao K, Chen W, Fu X (2017) Estimation of ultrasonic signal onset for flow measurement. Flow Measure Instrum 55:1–12

    Article  Google Scholar 

  • Farzi S (2009) Efficient job scheduling in grid computing with modified artificial fish swarm algorithm. Int J Comput Theory Eng 1(1):13

    Article  Google Scholar 

  • Fei C, Zhang P, Li J (2014) Motion estimation based on artificial fish-swarm in H. 264/AVC coding. WSEAS Trans Signal Process 10:221–229

    Google Scholar 

  • Fei T, Zhang L (2017) Application of BFO-AFSA to location of distribution centre. Clust Comput 20(4):3459–3474

    Article  Google Scholar 

  • Fei T, Zhang L, Zhang X, Chen Q, Liang J (2021) Location selection strategy of distribution centers based on artificial fish swarm algorithm improved by bacterial colony chemotaxis. J Internet Technol 22:685–695

    Google Scholar 

  • Feng Y, Zhao S, Liu H (2020) Analysis of network coverage optimization based on feedback k-means clustering and artificial fish swarm algorithm. IEEE Access 8:42864–42876

    Article  Google Scholar 

  • Fernandes EMGP, Martins TFMC, Rocha AMAC (2009) Fish swarm intelligent algorithm for bound constrained global optimization. In: International conference on computational and mathematical methods in science and engineering, pp 1–12

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

    Article  Google Scholar 

  • Gao Y, Guan L, Wang T (2014) Optimal artificial fish swarm algorithm for the field calibration on marine navigation. Measurement 50:297–304

    Article  Google Scholar 

  • Gao Y, Guan L, Wang T (2015) Triaxial accelerometer error coefficients identification with a novel artificial fish swarm algorithm. J Sens 5:58–59

    Google Scholar 

  • Gao Y, Guan L, Wang T, Sun Y (2015) A novel artificial fish swarm algorithm for recalibration of fiber optic gyroscope error parameters. Sensors 15(5):10547–10568

    Article  Google Scholar 

  • Gao Y, Xie L, Zhang Z, Fan Q (2020) Twin support vector machine based on improved artificial fish swarm algorithm with application to flame recognition. Applied Intelligence

  • Gholami J, Pourpanah F, Wang X (2020) Feature selection based on improved binary global harmony search for data classification. Appl Soft Comput 93:106402

    Article  Google Scholar 

  • Goluguri NRR, Devi KS, Srinivasan P (2021) Rice-net: an efficient artificial fish swarm optimization applied deep convolutional neural network model for identifying the oryza sativa diseases. Neural Comput Appl 33(11):5869–5884

    Article  Google Scholar 

  • Gorgich S, Tabatabaei S (2021) Proposing an energy-aware routing protocol by using fish swarm optimization algorithm in wsn (wireless sensor networks). Wirel Personal Commun. 119:1–21

    Google Scholar 

  • Guo Q, Xu R, Yang T, He L, Cheng X, Li Z, Yang J (2016) Application of GRAM and AFSACA-BPN to thermal error optimization modeling of CNC machine tools. Int J Adv Manuf Technol 83(5–8):995–1002

    Article  Google Scholar 

  • Hajisalem V, Babaie S (2018) A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection. Comput Netw 136:37–50

    Article  Google Scholar 

  • He J, Jin X, Xie S, Cao L, Lin Y, Wang N (2019) Multi-body dynamics modeling and TMD optimization based on the improved AFSA for floating wind turbines. Renew Energy 141:305–321

    Article  Google Scholar 

  • He S, Belacel N, Chan A, Hamam H, Bouslimani Y (2016) A hybrid artificial fish swarm simulated annealing optimization algorithm for automatic identification of clusters. Int J Inform Technol Decis Mak 15(05):949–974

    Article  Google Scholar 

  • He Y, Zhao X, Guo R, Gan X (2021) Multi-resolution wavelet neural network learning algorithm based on artificial fish swarm algorithm. In: The 2nd international conference on computing and data science, pp 1–5

  • Hua Z, Xiao Y, Cao J (2021) Misalignment fault prediction of wind turbines based on improved artificial fish swarm algorithm. Entropy 23(6):692

    Article  Google Scholar 

  • Huang J, Zeng J, Bai Y, Cheng Z, Feng Z, Qi L, Liang D (2021) Layout optimization of fiber bragg grating strain sensor network based on modified artificial fish swarm algorithm. Optical Fiber Technol 65:102583

    Article  Google Scholar 

  • Huang X, Xu G, Xiao F (2021) Optimization of a novel urban growth simulation model integrating an artificial fish swarm algorithm and cellular automata for a smart city. Sustainability 13:2338

    Article  Google Scholar 

  • Huang Z, Chen Y (2015) Log-linear model based behavior selection method for artificial fish swarm algorithm. Comput Intell Neurosci 2015:10

    Article  Google Scholar 

  • Jia B, Hao L, Zhang C, Huang B (2020) A privacy-sensitive service selection method based on artificial fish swarm algorithm in the internet of things. Mobile Netw Appl 26:1–9

    Google Scholar 

  • Jia D, Li Z, Zhang C (2020) A parametric optimization oriented, AFSA based random forest algorithm: application to the detection of cervical epithelial cells. IEEE Access 8:64891–64905

    Article  Google Scholar 

  • Jia X, Lu G (2019) An improved random Taguchi’s method based on swarm intelligence and dynamic reduced rate for electromagnetic optimization. IEEE Antennas Wirel Propag Lett 18(9):1878–1881

    Article  Google Scholar 

  • Jiang C, Wan L, Sun Y, Li Y (2017) The application of PSO-AFSA method in parameter optimization for underactuated autonomous underwater vehicle control. Math Probl Eng

  • Jiang M, Luo Y, Yang S (2007) Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm. Inform Process Lett 102(1):8–16

    Article  MathSciNet  MATH  Google Scholar 

  • Kang C, Wang S, Ren W, Lu Y, Wang B (2019) Optimization design and application of active disturbance rejection controller based on intelligent algorithm. IEEE Access 7:59862–59870

    Article  Google Scholar 

  • Kanimozhi N, Singaravel G (2021) Hybrid artificial fish particle swarm optimizer and kernel extreme learning machine for type-ii diabetes predictive model. Med Biol Eng Comput 59(4):841–867

    Article  Google Scholar 

  • Kennedy J (2010) Particle swarm optimization. Encycl Mach Learn 88:760–766

    Google Scholar 

  • Koohestani A, Abdar M, Khosravi A, Nahavandi S, Koohestani M (2019) Integration of ensemble and evolutionary machine learning algorithms for monitoring diver behavior using physiological signals. IEEE Access 7:98971–98992

    Article  Google Scholar 

  • Krishnaraj N, Jayasankar T, Kousik NV, Daniel A (2021) 2 Artificial Fish swarm optimization algorithm with hill climbing based clustering technique for throughput maximization in wireless multimedia sensor network, pp 23–42. River Publishers

  • Kusakci AO, Can M (2014) An adaptive evolution strategy for constrained optimisation problems in engineering design. Int J Bio-Inspir Comput 6(3):175–191

    Article  Google Scholar 

  • Lei X, Ouyang H, Xu L (2018) Image segmentation based on equivalent three-dimensional entropy method and artificial fish swarm optimization algorithm. Opt Eng 57(10):103106

    Article  Google Scholar 

  • Li C, Sun J, Palade V, Li LW (2021) Diversity collaboratively guided random drift particle swarm optimization. Int J Mach Learn Cybernet 58:1–22

    Article  Google Scholar 

  • Li H, Huang Y, Tian S (2019) Risk probability predictions for coal enterprise infrastructure projects in countries along the belt and road initiative. Int J Ind Ergon 69:110–117

    Article  Google Scholar 

  • Li J, Zhao S, Xu Y (2015) Quantum-inspired artificial fish swarm algorithm based on the bloch sphere searching. Quantum 4(4):06–18

    Google Scholar 

  • Li S, Li W, Sun H (2013) Artificial fish swarm parallel algorithm based on multi-core cluster. J Comput Appl 33(12):3380–3384

    Google Scholar 

  • Li T, Yang F, Zhang D, Zhai L (2021) Computation scheduling of multi-access edge networks based on the artificial fish swarm algorithm. IEEE Access 9:74674–74683

    Article  Google Scholar 

  • Li TH, Xie SS, Liu SP, Xiao L, Jia WZ, He DW (2018) A fault detection optimization method based on chaos adaptive artificial fish swarm algorithm on distributed control system. J Syst Control Eng 232(9):1182–1193

    Google Scholar 

  • Li W, Bi Y, Zhu X, Yuan CA, Zhang XB (2016) Hybrid swarm intelligent parallel algorithm research based on multi-core clusters. Microprocess Microsyst 47:151–160

    Article  Google Scholar 

  • Li XL, Shao ZJ, Qian JX (2002) Optimizing method based on autonomous animats: fish-swarm algorithm. Syst Eng Theory Pract 22(11):32–38 (in Chinese)

    Google Scholar 

  • Liang JJ, Suganthan PN, Deb K (2005) Novel composition test functions for numerical global optimization. In: IEEE swarm intelligence symposium, pp 68–75. IEEE

  • Lin M, Yuan X, Lei H, Ji Z (2021) Kinematic analysis of tensegrity mechanisms based on improved artificial fish swarm algorithm with variable step size. In: Journal of Physics: Conference Series, vol 1903, p 012071

  • Liu D, Zhao D, Fu Q, Wu Q, Zhang Y, Li T, Imran KM, Abrar FM (2016) Complexity measurement of regional groundwater resources system using improved lempel-ziv complexity algorithm. Arab J Geosc 9(20):746

    Article  Google Scholar 

  • Liu Y, Feng X, Yang Y, Ruan Z, Zhang L, Li K (2020) Solving urban electric transit network problem by integrating pareto artificial fish swarm algorithm and genetic algorithm. J Intell Transp Syst 26:1–28

    Google Scholar 

  • Liu Y, Tao Z, Yang J, Mao F (2019) The modified artificial fish swarm algorithm for least-cost planning of a regional water supply network problem. Sustainability 11(15):4121

    Article  Google Scholar 

  • Liu Y, Wang J, Shahbazzade S (2019) The improved AFSA algorithm for the berth allocation and quay crane assignment problem. Clust Comput 22(2):3665–3672

    Article  Google Scholar 

  • Liu Y, Wang R (2016) Study on network traffic forecast model of SVR optimized by GAFSA. Chaos Solitons Fract 89:153–159

    Article  MATH  Google Scholar 

  • Lung RI, Dumitrescu D (2010) Evolutionary swarm cooperative optimization in dynamic environments. Nat Comput 9(1):83–94

    Article  MathSciNet  MATH  Google Scholar 

  • Ma C, He R (2019) Green wave traffic control system optimization based on adaptive genetic-artificial fish swarm algorithm. Neural Comput Appl 31(7):2073–2083

    Article  Google Scholar 

  • Ma L, Li Y, Fan S, Fan R (2015) A hybrid method for image segmentation based on artificial fish swarm algorithm and fuzzy-means clustering. Comput Math Methods Med

  • Maji KB, Kar R, Mandal D, Ghoshal S (2018) Optimal design of low power high gain and high speed CMOS circuits using fish swarm optimization algorithm. Int J Mach Learn Cybernet 9(5):771–786

    Article  Google Scholar 

  • Mao M, Duan Q, Duan P, Hu B (2018) Comprehensive improvement of artificial fish swarm algorithm for global MPPT in PV system under partial shading conditions. Trans Inst Measur Control 40(7):2178–2199

    Article  Google Scholar 

  • Mavrovouniotis M, Li C, Yang S (2017) A survey of swarm intelligence for dynamic optimization: algorithms and applications. Swarm Evolut Comput 33:1–17

    Article  Google Scholar 

  • Mechta D, Harous S (2017) Prolonging WSN lifetime using a new scheme for sink moving based on artificial fish swarm algorithm. In: Proceedings of the second international conference on advanced wireless information, data, and communication technologies, pp 1–5

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

    Article  Google Scholar 

  • Nand R, Sharma BN, Chaudhary K (2021) Stepping ahead firefly algorithm and hybridization with evolution strategy for global optimization problems. Appl Soft Comput 109:107517

    Article  Google Scholar 

  • Neshat M, Sepidnam G, Sargolzaei M, Toosi AN (2014) Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif Intell Rev 42(4):965–997

    Article  Google Scholar 

  • Omidvar MN, Li X, Mei Y, Yao X (2014) Cooperative co-evolution with differential grouping for large scale optimization. IEEE Trans Evolut Comput 18(3):378–393

    Article  Google Scholar 

  • Pajouhi Z, Roy K (2018) Image edge detection based on swarm intelligence using memristive networks. IEEE Trans Comput-Aided Des Integr Circuits Syst 37(9):1774–1787

    Article  Google Scholar 

  • Pavlyukevich I (2007) Lévy flights, non-local search and simulated annealing. J Comput Phys 226(2):1830–1844

    Article  MathSciNet  MATH  Google Scholar 

  • Peng Z, Dong K, Yin H, Bai Y (2018) Modification of fish swarm algorithm based on levy flight and firefly behavior. Comput Intell Neurosci

  • Pourpanah F, Lim CP, Saleh JM (2016) A hybrid model of fuzzy artmap and genetic algorithm for data classification and rule extraction. Expert Syst Appl 49:74–85

    Article  Google Scholar 

  • Pourpanah F, Lim CP, Wang X, Tan CJ, Seera M, Shi Y (2019) A hybrid model of fuzzy min-max and brain storm optimization for feature selection and data classification. Neurocomputing 333:440–451

    Article  Google Scholar 

  • Pourpanah F, Shi Y, Lim CP, Hao Q, Tan CJ (2019) Feature selection based on brain storm optimization for data classification. Appl Soft Comput 80:761–775

    Article  Google Scholar 

  • Pourpanah F, Tan CJ, Lim CP, Mohamad-Saleh J (2017) A q-learning-based multi-agent system for data classification. Appl Soft Comput 52:519–531

    Article  Google Scholar 

  • Pourpanah F, Wang R, Wang X (2019) Feature selection for data classification based on binary brain storm optimization. In: IEEE international conference on cloud computing and intelligence systems (CCIS), pp 108–113

  • Pourpanah F, Wang R, Wang X, Shi Y, Yazdani D (2019) MBSO: a multi-population brain storm optimization for multimodal dynamic optimization problems. In: 2019 IEEE symposium series on computational intelligence (SSCI), pp 673–679

  • Pourpanah F, Zhang B, . 1–4

  • Pourpanah F, Zhang B, Ma R, Hao Q (2018) Non-intrusive human motion recognition using distributed sparse sensors and the genetic algorithm based neural network. In: 2018 IEEE SENSORS, pp 1–4

  • Qin N, Xu J (2018) An adaptive fish swarm-based mobile coverage in WSNs. Wirel Commun Mobile Comput

  • Reynolds RG, Peng B (2004) Cultural algorithms: modeling of how cultures learn to solve problems. In: IEEE international conference on tools with artificial intelligence, pp 166–172

  • Sathya DJ, Geetha K (2017) Hybrid ANN optimized artificial fish swarm algorithm based classifier for classification of suspicious lesions in breast DCE-MRI. Polish J Med Phys Eng 23(4):81–88

    Article  Google Scholar 

  • Serapião AB, Corrêa GS, Gonçalves FB, Carvalho VO (2016) Combining K-means and K-harmonic with fish school search algorithm for data clustering task on graphics processing units. Appl Soft Comput 41:290–304

    Article  Google Scholar 

  • Shao H, Jiang H, Zhao H, Wang F (2017) A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mech Syst Signal Process 95:187–204

    Article  Google Scholar 

  • Shi Y (2011) Brain storm optimization algorithm. In: International conference in swarm intelligence, pp 303–309

  • Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: IEEE international conference on evolutionary computation proceedings. pp 69–73

  • Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  • Sun T, Zhang H, Liu S, Cao Y (2017) Application of an artificial fish swarm algorithm in solving multiobjective trajectory optimization problems. Chem Technol Fuels Oils 53(4):541–547

    Article  Google Scholar 

  • Talha M, Saeed MS, Mohiuddin G, Ahmad M, Nazar MJ, Javaid N (2018) Energy optimization in home energy management system using artificial fish swarm algorithm and genetic algorithm. In: International conference on intelligent networking and collaborative systems, pp 203–213

  • Tan WH, Mohamad-Saleh J (2019) Normative fish swarm algorithm (NFSA) for optimization. Soft Comput 9:1–17

    Google Scholar 

  • Upadhyay P, Pandey MK, Kohli N (2021) Periodic pattern mining from spatio-temporal database using novel global pollination artificial fish swarm optimizer-based clustering and modified fp tree. Soft Comput 25(6):4327–4344

    Article  Google Scholar 

  • Wang H, Guo Y (2015) A blind equalization algorithm based on global artificial fish swarm and genetic optimization DNA encoding sequences. In: industrial informatics and computer engineering conference, pp 131–134

  • Wang HB, Fan CC, Tu XY (2016) AFSAOCP: a novel artificial fish swarm optimization algorithm aided by ocean current power. Appl Intell 45(4):992–1007

    Article  Google Scholar 

  • Wang X, Li H, Li Z (2018) Estimation of interfacial heat transfer coefficient in inverse heat conduction problems based on artificial fish swarm algorithm. Heat Mass Transf 54(10):3151–3162

    Article  Google Scholar 

  • Wei P, Li Y, Zhang Z, Hu T, Li Z, Liu D (2019) An optimization method for intrusion detection classification model based on deep belief network. IEEE Access 7:87593–87605

    Article  Google Scholar 

  • Xi L, Zhang F (2019) An adaptive artificial-fish-swarm-inspired fuzzy c-means algorithm. Neural Comput Appl 28:1–9

    Google Scholar 

  • Xian S, Zhang J, Xiao Y, Pang J (2018) A novel fuzzy time series forecasting method based on the improved artificial fish swarm optimization algorithm. Soft Comput 22(12):3907–3917

    Article  Google Scholar 

  • Xian Z, Yang H (2021) An early warning model for the stuck-in medical drilling process based on the artificial fish swarm algorithm and SVM. Distribut Parall Databases pp 1–18

  • Xu H, Zhao Y, Ye C, Lin F (2019) Integrated optimization for mechanical elastic wheel and suspension based on an improved artificial fish swarm algorithm. Adv Eng Softw 137:102722

    Article  Google Scholar 

  • Yan L, He Y, Huangfu Z (2020) A fish swarm inspired holes recovery algorithm for wireless sensor networks. Int J Wirel Inform Netw 27(1):89–101

    Article  Google Scholar 

  • Yan W, Li M, Pan X, Wu G, Liu L (2020) Application of support vector regression cooperated with modified artificial fish swarm algorithm for wind tunnel performance prediction of automotive radiators. Appl Thermal Eng 164:114543

    Article  Google Scholar 

  • Yan W, Li M, Zhong Y, Qu C, Li G (2020) A novel k-mpso clustering algorithm for the construction of typical driving cycles. IEEE Access 8:64028–64036

    Article  Google Scholar 

  • Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver Press, Bristol

    Google Scholar 

  • Yang XS (2010) A new metaheuristic bat-inspired algorithm, pp 65–74. Springer

  • Yang XS, Deb S (2009) Cuckoo search via lévy flights. In: World congress on nature & biologically inspired computing (NaBIC), pp 210–214

  • Yaseen ZM, Karami H, Ehteram M, Mohd NS, Mousavi SF, Hin LS, Kisi O, Farzin S, Kim S, El-Shafie A (2018) Optimization of reservoir operation using new hybrid algorithm. J Civil Eng 22(11):4668–4680

    Google Scholar 

  • Yazdani D, Akbarzadeh-Totonchi MR, Nasiri B, Meybodi MR (2012) A new artificial fish swarm algorithm for dynamic optimization problems. In: EEE Congress on evolutionary computation, pp 1–8. IEEE

  • Yazdani D, Golyari S, Meybodi MR (2010) A new hybrid algorithm for optimization based on artificial fish swarm algorithm and cellular learning automata. In: International symposium on telecommunications, pp 932–937. IEEE

  • Yazdani D, Golyari S, Meybodi MR (2010) A new hybrid approach for data clustering. In: International symposium on telecommunications, pp 914–919. IEEE

  • Yazdani D, Nabizadeh H, Kosari EM, Toosi AN (2011) Color quantization using modified artificial fish swarm algorithm. In: Australasian Joint Conference on Artificial Intelligence, pp 382–391. Springer

  • Yazdani D, Nasiri B, Sepas-Moghaddam A, Meybodi M, Akbarzadeh-Totonchi M (2014) mnafsa: a novel approach for optimization in dynamic environments with global changes. Swarm Evolut Comput 18:38–53

    Article  Google Scholar 

  • Yazdani D, Saman B, Sepas-Moghaddam A, Mohammad-Kazemi F, Meybodi MR (2013) A new algorithm based on improved artificial fish swarm algorithm for data clustering. Int J Artif Intell 11(13):1–29

    Google Scholar 

  • Yazdani D, Sepas-Moghaddam A, Dehban A, Horta N (2016) A novel approach for optimization in dynamic environments based on modified artificial fish swarm algorithm. Int J Comput Intell Appl 15(02):1650010

    Article  Google Scholar 

  • Yuan G, Yang W (2019) Study on optimization of economic dispatching of electric power system based on hybrid intelligent algorithms (PSO and AFSA). Energy 183:926–935

    Article  Google Scholar 

  • Yuan Y, Li Q, Yuan X, Luo X, Liu S (2020) A SAFSA- and metabolism-based nonlinear grey Bernoulli model for annual water consumption prediction. Iran J Sci Technol Trans Civil Eng 44(2):755–765

    Article  Google Scholar 

  • Zhang FS, Li SW, Hu ZG, Du Z (2017) Fish swarm window selection algorithm based on cell microscopic automatic focus. Clust Comput 20(1):485–495

    Article  Google Scholar 

  • Zhang L, Fu M, Fei T (2021) Research on location of cold chain logistics distribution center with low carbon in beijing-tianjin-hebei area on the basis of RNA-artificial fish swarm algorithm. J Phys 186:012005

    Google Scholar 

  • Zhang L, Fu M, Li H, Liu T (2021) Improved artificial bee colony algorithm based on damping motion and artificial fish swarm algorithm. J Phys 1903:012038

    Google Scholar 

  • Zhang S, Zhao X, Liang C, Ding X (2017) Adaptive power allocation schemes based on IAFS algorithm for OFDM-based cognitive radio systems. Int J Electron 104(1):1–15

    Article  Google Scholar 

  • Zhang X, Lian L, Zhu F (2021) Parameter fitting of variogram based on hybrid algorithm of particle swarm and artificial fish swarm. Fut Generat Comput Syst 116:265–274

    Article  Google Scholar 

  • Zhang X, Wang J, Yang A, Yan C, Zhu F, Zhao Z, Cao Z (2013) Identifying interacting genetic variations by fish-swarm logic regression. BioMed Res Int

  • Zhang Y, Guan G, Pu X (2016) The robot path planning based on improved artificial fish swarm algorithm. Math Probl Eng

  • Zhang Z, Ma J (2019) Adaptive parameter-tuning stochastic resonance based on SVD and its application in weak IF digital signal enhancement. J Adv Signal Process 2019(1):1–24

    Google Scholar 

  • Zhang Z, Wang K, Zhu L, Wang Y (2017) A pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem. Expert Syst Appl 86:165–176

    Article  Google Scholar 

  • Zheng R, Feng Z, Shi J, Jiang S, Tan L (2020) Hybrid bacterial forging optimization based on artificial fish swarm algorithm and Gaussian disturbance. In: Bio-inspired Comput Theor Appl, pp 124–134

  • Zhou G, Li Y, He YC, Wang X, Yu M (2018) Artificial fish swarm based power allocation algorithm for mimo-ofdm relay underwater acoustic communication. IET Commun 12(9):1079–1085

    Article  Google Scholar 

  • Zhou J, Qi G, Liu C (2021) A chaotic parallel artificial fish swarm algorithm for water quality monitoring sensor networks 3d coverage optimization. J Sens

  • Zhou X, Wang Z, Li D, Zhou H, Qin Y, Wang J (2019) Guidance systematic error separation for mobile launch vehicles using artificial fish swarm algorithm. IEEE Access 7:31422–31434

    Article  Google Scholar 

  • Zhu J, Wang C, Hu Z, Kong F, Liu X (2017) Adaptive variational mode decomposition based on artificial fish swarm algorithm for fault diagnosis of rolling bearings. Proc Inst Mech Eng Part C 231(4):635–654

    Article  Google Scholar 

  • Zhu Y, Xu W, Luo G, Wang H, Yang J, Lu W (2020) Random forest enhancement using improved artificial fish swarm for the medial knee contact force prediction. Artif Intell Med 103:101811

    Article  Google Scholar 

  • Zhuang D, Ma K, Tang C, Liang Z, Wang K, Wang Z (2019) Mechanical parameter inversion in tunnel engineering using support vector regression optimized by multi-strategy artificial fish swarm algorithm. Tunnell Underground Space Technol 83:425–436

    Article  Google Scholar 

  • Zomorodi-moghadam M, Abdar M, Davarzani Z, Zhou X, Pławiak P, Acharya UR (2019) Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease. Expert Syst p. e12485

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 62176160, 61976141 and 61732011, in part by the Natural Science Foundation of Shenzhen (University Stability Support Program) under Grant 20200804193857002, and in part by the Interdisciplinary Innovation Team of Shenzhen University.

Author information

Authors and Affiliations

  1. College of Mathematics and Statistics, Shenzhen University, Shenzhen, China

    Farhad Pourpanah & Ran Wang

  2. Department of Electrical and Computer Engineering, University of Windsor, Windsor, Canada

    Farhad Pourpanah

  3. Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University, Shenzhen, China

    Ran Wang

  4. Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Australia

    Chee Peng Lim

  5. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Xi-Zhao Wang

  6. School of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Danial Yazdani

  7. Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, China

    Ran Wang & Xi-Zhao Wang

Authors
  1. Farhad Pourpanah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ran Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chee Peng Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xi-Zhao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Danial Yazdani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ran Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pourpanah, F., Wang, R., Lim, C.P. et al. A review of artificial fish swarm algorithms: recent advances and applications. Artif Intell Rev 56, 1867–1903 (2023). https://doi.org/10.1007/s10462-022-10214-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-022-10214-4

Keywords

Search

Navigation