nach oben

Neural Computing and Applications

Erschienen in:

05.12.2019 | Original Article

Electric fish optimization: a new heuristic algorithm inspired by electrolocation

verfasst von: Selim Yilmaz, Sevil Sen

Erschienen in: Neural Computing and Applications | Ausgabe 15/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Swarm behaviors in nature have inspired the emergence of many heuristic optimization algorithms. They have attracted much attention, particularly for complex problems, owing to their characteristics of high dimensionality, nondifferentiability, and the like. A new heuristic algorithm is proposed in this study inspired by the prey location and communication behaviors of electric fish. Nocturnal electric fish have very poor eyesight and live in muddy, murky water, where visual senses are very limited. Therefore, they rely on their species-specific ability called electrolocation to perceive their environment. The active and passive electrolocation capability of such fish is believed to be a good candidate for balancing local and global search, and hence it is modeled in this study. A new heuristic called electric fish optimization (EFO) is introduced and compared with six well-known heuristics (simulated annealing, SA; vortex search, VS; genetic algorithm, GA; differential evolution, DE; particle swarm optimization, PSO; and artificial bee colony, ABC). In the experiments, 50 basic and 30 complex mathematical functions, 13 clustering problems, and five real-world design problems are used as the benchmark sets. The simulation results indicate that EFO is better than or very competitive with its competitors.

Vorheriger Artikel Slicing point cloud incrementally for Additive Manufacturing via online learning

Nächster Artikel Hierarchical stochastic graphlet embedding for graph-based pattern recognition

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

https://wise.cs.hacettepe.edu.tr/projects/efo/.

Akay B, Karaboga D (2012) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23(4):1001–1014. https://doi.org/10.1007/s10845-010-0393-4 CrossRef

Aragon V, ES C, Coello CCA (2010) A modified version of a t cell algorithm for constrained optimization problems. Int J Numer Meth Eng 84(3):351–378. https://doi.org/10.1002/nme.2904 CrossRefMATH

Arora JS (1967) Introduction to optimum design, 1989. McGraw-Mill Book Company, New Yrok

Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12. https://doi.org/10.1016/j.compstruc.2016.03.001 CrossRef

Awad N, Ali M, Liang J, Qu B, Suganthan P (2016) Problem definitions and evaluation criteria for the cec 2017 special session and competition on single objective real-parameter numerical optimization. Tech Rep

Bandyopadhyay S, Maulik U (2002) Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recogn 35(6):1197–1208CrossRef

Barr RS, Golden BL, Kelly JP, Resende MGC, Stewart WR (1995) Designing and reporting on computational experiments with heuristic methods. J Heuristics 1(1):9–32. https://doi.org/10.1007/BF02430363 CrossRefMATH

Bernardino HS, Barbosa HJC, Lemonge ACC (2007) A hybrid genetic algorithm for constrained optimization problems in mechanical engineering. In: 2007 IEEE congress on evolutionary computation, pp 646–653. https://doi.org/10.1109/CEC.2007.4424532

Bernardino HS, Barbosa HJC, Lemonge ACC, Fonseca LG (2008) A new hybrid ais-ga for constrained optimization problems in mechanical engineering. In: 2008 IEEE congress on evolutionary computation (IEEE world congress on computational intelligence), pp 1455–1462. https://doi.org/10.1109/CEC.2008.4630985

10.

Blake C, Merz C (1998) University of california at irvine repository of machine learning databases. Department of Information and Computer Science, Irvine, CA

11.

Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press Inc, New York, NYMATH

12.

Boussaïd I, Lepagnot J, Siarry P (2013) A survey on optimization metaheuristics. Inf Sci 237:82–117. https://doi.org/10.1016/j.ins.2013.02.041 prediction, Control and Diagnosis using Advanced Neural ComputationsMathSciNetCrossRefMATH

13.

Civicioglu P (2013a) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144. https://doi.org/10.1016/j.amc.2013.02.017 MathSciNetCrossRefMATH

14.

Civicioglu P (2013b) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144. https://doi.org/10.1016/j.amc.2013.02.017 MathSciNetCrossRefMATH

15.

Corne D, Dorigo M, Glover F, Dasgupta D, Moscato P, Poli R, Price KV (eds) (1999) New ideas in optimization. McGraw-Hill Ltd., Maidenhead

16.

Datta D, Figueira JR (2011) A real-integer-discrete-coded particle swarm optimization for design problems. Appl Soft Comput 11(4):3625–3633. https://doi.org/10.1016/j.asoc.2011.01.034 CrossRef

17.

Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1(1):3–18. https://doi.org/10.1016/j.swevo.2011.02.002 CrossRef

18.

Di Caro G, Ducatelle F, Gambardella LM (2005) Anthocnet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks. Eur Trans Telecommun 16(5):443–455. https://doi.org/10.1002/ett.1062 CrossRef

19.

Du TS, Ke XT, Liao JG, Shen YJ (2018) Dslc-foa : improved fruit fly optimization algorithm for application to structural engineering design optimization problems. Appl Math Model 55:314–339. https://doi.org/10.1016/j.apm.2017.08.013 MathSciNetCrossRefMATH

20.

Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, Berlin. https://doi.org/10.1007/978-3-662-05094-1

21.

Falco ID, Cioppa AD, Tarantino E (2007) Facing classification problems with particle swarm optimization. Appl Soft Comput 7(3):652–658CrossRef

22.

Falco ID, Cioppa AD, Maisto D, Tarantino E (2008) Differential evolution as a viable tool for satellite image registration. Appl Soft Comput 8(4):1453–1462. https://doi.org/10.1016/j.asoc.2007.10.013 soft Computing for Dynamic Data MiningCrossRef

23.

Fan S, Ding S, Xue Y (2018) Self-adaptive kernel k-means algorithm based on the shuffled frog leaping algorithm. Soft Comput 22(3):861–872. https://doi.org/10.1007/s00500-016-2389-2 CrossRef

24.

Gandomi AH (2014) Interior search algorithm (isa): a novel approach for global optimization. ISA Trans 53(4):1168–1183. https://doi.org/10.1016/j.isatra.2014.03.018 disturbance Estimation and MitigationCrossRef

25.

Gandomi AH, Yang XS (2011) Benchmark problems in structural optimization. Springer, Berlin, pp 259–281MATH

26.

Golinski J (1970) Optimal synthesis problems solved by means of nonlinear programming and random methods. J Mech 5(3):287–309. https://doi.org/10.1016/0022-2569(70)90064-9. http://www.sciencedirect.com/science/article/pii/0022256970900649

27.

Haldar V, Chakraborty N (2016) A novel evolutionary technique based on electrolocation principle of elephant nose fish and shark: fish electrolocation optimization. Soft Comput pp 1–22. https://doi.org/10.1007/s00500-016-2033-1

28.

Han J, Yang C, Zhou X, Gui W (2018) A two-stage state transition algorithm for constrained engineering optimization problems. Int J Control Autom Syst 16(2):522–534. https://doi.org/10.1007/s12555-016-0338-6 CrossRef

29.

Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222(Supple c):175–184. https://doi.org/10.1016/j.ins.2012.08.023 MathSciNetCrossRef

30.

Holland JH (1992) Adaptation in natural and artificial systems. MIT Press, Cambridge, MACrossRef

31.

Hwang CL, Yoon K (2012) Multiple attribute decision making: methods and applications a state-of-the-art survey, vol 186. Springer Science & Business Media, BerlinMATH

32.

Kamilaris A, Prenafeta-Boldú FX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70–90. https://doi.org/10.1016/j.compag.2018.02.016. http://www.sciencedirect.com/science/article/pii/S0168169917308803

33.

Kanagaraj G, Ponnambalam S, Jawahar N, Nilakantan JM (2014) An effective hybrid cuckoo search and genetic algorithm for constrained engineering design optimization. Eng Optim 46(10):1331–1351. https://doi.org/10.1080/0305215X.2013.836640 MathSciNetCrossRef

34.

Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Tech. rep., Technical report-tr06, Erciyes university, engineering faculty, computer engineering department

35.

Karaboga D, Akay B (2009a) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132. https://doi.org/10.1016/j.amc.2009.03.090 MathSciNetCrossRefMATH

36.

Karaboga D, Akay B (2009b) A survey: algorithms simulating bee swarm intelligence. Artif Intell Rev 31(1):61. https://doi.org/10.1007/s10462-009-9127-4 CrossRef

37.

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. https://doi.org/10.1007/s10898-007-9149-x MathSciNetCrossRefMATH

38.

Karaboga D, Ozturk C (2011) A novel clustering approach: artificial bee colony (abc) algorithm. Appl Soft Comput 11(1):652–657CrossRef

39.

Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, 1995, vol 4, pp 1942–1948

40.

Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680. https://doi.org/10.1126/science.220.4598.671. http://science.sciencemag.org/content/220/4598/671.full.pdf

41.

Kramer B (1996) Electroreception and communication in fishes, vol 42. Gustav Fischer, Berlin

42.

Lebastard V, Chevallereau C, Amrouche A, Jawad B, Girin A, Boyer F, Gossiaux PB (2010) Underwater robot navigation around a sphere using electrolocation sense and kalman filter. In: 2010 IEEE/RSJ international conference on intelligent robots and systems, IEEE. https://doi.org/10.1109/iros.2010.5648929

43.

Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JA, van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88. https://doi.org/10.1016/j.media.2017.07.005. http://www.sciencedirect.com/science/article/pii/S1361841517301135

44.

Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10(2):629–640. https://doi.org/10.1016/j.asoc.2009.08.031. http://www.sciencedirect.com/science/article/pii/S1568494609001550

45.

MacIver M, Fontaine E, Burdick J (2004) Designing future underwater vehicles: Principles and mechanisms of the weakly electric fish. IEEE J Oceanic Eng 29(3):651–659. https://doi.org/10.1109/joe.2004.833210 CrossRef

46.

Maciver MA, Nelson ME (2001) Towards a biorobotic electrosensory systemtowards a biorobotic electrosensory system. Auton Robot 11(3):263–266. https://doi.org/10.1023/a:1012443124333 CrossRefMATH

47.

Mahdavifar S, Ghorbani AA (2019) Application of deep learning to cybersecurity: a survey. Neurocomputing 347:149–176. https://doi.org/10.1016/j.neucom.2019.02.056. http://www.sciencedirect.com/science/article/pii/S0925231219302954

48.

Mezura-Montes E, Hernandez-Ocana B (2008) Bacterial foraging for engineering design problems: preliminary results. In: Memorias del 4o Congreso Nacional de Computacion Evolutiva (COMCEV 2008)

49.

Mezura-Montes E, Coello CC, Landa-Becerra R (2003) Engineering optimization using simple evolutionary algorithm. In: Proceedings of the 15th IEEE international conference on tools with artificial intelligence, IEEE, pp 149–156

50.

Mezura-Montes E, Coello CC, Velázquez-Reyes J (2006) Increasing successful offspring and diversity in differential evolution for engineering design. In: Proceedings of the seventh international conference on adaptive computing in design and manufacture (ACDM 2006), pp 131–139

51.

Mohamed AW (2018) A novel differential evolution algorithm for solving constrained engineering optimization problems. J Intell Manuf 29(3):659–692. https://doi.org/10.1007/s10845-017-1294-6 CrossRef

52.

Moller P (1995) Electric fishes: history and behavior. Chapman and Hall fish and fisheries series. Chapman & Hall, London

53.

Neveln ID, Bai Y, Snyder JB, Solberg JR, Curet OM, Lynch KM, MacIver MA (2013) Biomimetic and bio-inspired robotics in electric fish research. J Exp Biol 216(13):2501–2514. https://doi.org/10.1242/jeb.082743 CrossRef

54.

Nezamabadi-pour H, Saryazdi S, Rashedi E (2006) Edge detection using ant algorithms. Soft Comput 10(7):623–628. https://doi.org/10.1007/s00500-005-0511-y CrossRef

55.

Opricovic S (1998) Multicriteria optimization of civil engineering systems. Fac Civil Eng Belgrade 2(1):5–21MathSciNet

56.

Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl-Based Syst 26:69–74. https://doi.org/10.1016/j.knosys.2011.07.001 CrossRef

57.

Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52–67. https://doi.org/10.1109/MCS.2002.1004010 CrossRef

58.

Rao R, Savsani V, Vakharia D (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315. https://doi.org/10.1016/j.cad.2010.12.015. http://www.sciencedirect.com/science/article/pii/S0010448510002484

59.

Rao SS (1996) Engineering optimization: theory and practice, 3rd edn. Wiley-Interscience, New Yok

60.

Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248. https://doi.org/10.1016/j.ins.2009.03.004 CrossRefMATH

61.

Ridge E, Kudenko D (2007) Screening the parameters affecting heuristic performance. In: Proceedings of the genetic and evolutionary computation conference, ACM

62.

Sandgren E (1990) Nonlinear integer and discrete programming in mechanical design optimization. J Mech Des 112(2):223. https://doi.org/10.1115/1.2912596 CrossRef

63.

dos Santos CL (2010) Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Syst Appl 37(2):1676–1683. https://doi.org/10.1016/j.eswa.2009.06.044 CrossRef

64.

Schwefel H (1965) Kybernetische evolution als strategie der experimentellen forschung in der stromungstechnik. Master’s thesis, Technical University of Berlin, Germany

65.

Sen S (2010) Evolutionary computation techniques for intrusion detection in mobile ad hoc networks. PhD thesis, University of York

66.

Solberg JR, Lynch KM, MacIver MA (2007) Robotic electrolocation: active underwater target localization with electric fields. In: Proceedings 2007 IEEE international conference on robotics and automation, pp 4879–4886. https://doi.org/10.1109/ROBOT.2007.364231

67.

Sousa T, Silva A, Neves A (2004) Particle swarm based data mining algorithms for classification tasks. Parallel Comput 30(5–6):767–783. https://doi.org/10.1016/j.parco.2003.12.015 parallel and nature-inspired computational paradigms and applicationsCrossRef

68.

Sun F, Hu G (1998) Speech recognition based on genetic algorithm for training HMM. Electron Lett 34(16):1563. https://doi.org/10.1049/el:19980096 CrossRef

69.

Talbi EG (2009) Metaheuristics: from design to implementation, vol 74. Wiley, New YokCrossRef

70.

Tan K, Chiam S, Mamun A, Goh C (2009) Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization. Eur J Oper Res 197(2):701–713. https://doi.org/10.1016/j.ejor.2008.07.025 CrossRefMATH

71.

Wang H, Hu Z, Sun Y, Su Q, Xia X (2018) A novel modified bsa inspired by species evolution rule and simulated annealing principle for constrained engineering optimization problems. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3329-5

72.

Weise T (2008a) Global optimization algorithms - theory and application. 2008th edn. Thomas Weise

73.

Weise T (2008b) Global optimization algorithms – theory and application. 2008th edn. Thomas Weise. http://www.it-weise.de/

74.

Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82CrossRef

75.

Yang X, Suash D (2009) Cuckoo search via lévy flights. In: 2009 world congress on nature biologically inspired computing (NaBIC), pp 210–214. https://doi.org/10.1109/NABIC.2009.5393690

76.

Yang X, Gandomi A, Talatahari S, Alavi A (2012) Metaheuristics in water, geotechnical and transport engineering. Elsevier insights, Elsevier Science, Amsterdam

77.

Yang XS (2010a) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspir Comput 2(2):78–84CrossRef

78.

Yang XS (2010b) Nature-inspired metaheuristic algorithms, 2nd edn. Luniver Press, Beckington

79.

Yang XS (2013) 1 - optimization and metaheuristic algorithms in engineering. In: Yang XS, Gandomi AH, Talatahari S, Alavi AH (eds) Metaheuristics in water, geotechnical and transport engineering. Elsevier, Oxford, pp 1–23

80.

Yang XS, Cui Z, Xiao R, Gandomi AH, Karamanoglu M (2013) Swarm intelligence and bio-inspired computation: theory and applications, 1st edn. Elsevier Science Publishers B. V., Amsterdam

81.

Zahadat P, Schmickl T (2014) Wolfpack-inspired evolutionary algorithm and a reaction-diffusion-based controller are used for pattern formation. In: Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO 14, ACM Press. https://doi.org/10.1145/2576768.2598262

82.

Zhang J, Liang C, Huang Y, Wu J, Yang S (2009) An effective multiagent evolutionary algorithm integrating a novel roulette inversion operator for engineering optimization. Appl Math Comput 211(2):392–416. https://doi.org/10.1016/j.amc.2009.01.048 MathSciNetCrossRefMATH

83.

Zhang M, Luo W, Wang X (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inf Sci 178(15):3043–3074. https://doi.org/10.1016/j.ins.2008.02.014 nature Inspired Problem-SolvingCrossRef

84.

Zhang N, Ding S (2017) Unsupervised and semi-supervised extreme learning machine with wavelet kernel for high dimensional data. Memet Comput 9(2):129–139. https://doi.org/10.1007/s12293-016-0198-x CrossRef

85.

Zhang N, Ding S, Zhang J, Xue Y (2017) Research on point-wise gated deep networks. Appl Soft Comput 52:1210–1221. https://doi.org/10.1016/j.asoc.2016.08.056 CrossRef

86.

Zhang N, Ding S, Zhang J, Xue Y (2018a) An overview on restricted boltzmann machines. Neurocomputing 275:1186–1199. https://doi.org/10.1016/j.neucom.2017.09.065 CrossRef

87.

Zhang Q, Yang LT, Chen Z, Li P (2018b) A survey on deep learning for big data. Inf Fus 42:146–157. https://doi.org/10.1016/j.inffus.2017.10.006. http://www.sciencedirect.com/science/article/pii/S1566253517305328

Titel: Electric fish optimization: a new heuristic algorithm inspired by electrolocation
verfasst von: Selim Yilmaz
Sevil Sen
Publikationsdatum: 05.12.2019
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 15/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-019-04641-8

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 15/2020

A personalized recommendation algorithm based on large-scale real micro-blog data

A deep learning approach for Parkinson’s disease diagnosis from EEG signals

Concrete compressive strength using artificial neural networks

Evaluation on diabetic plantar pressure data-set employing auto-segmentation technologies

Parameters identification and discharge capacity prediction of Nickel–Metal Hydride battery based on modified fuzzy c-regression models

Drug recommendation with minimal side effects based on direct and temporal symptoms

Premium Partner