Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 13/2020

11.09.2019 | Original Article

Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm

verfasst von: Weiguo Zhao, Liying Wang, Zhenxing Zhang

Erschienen in: Neural Computing and Applications | Ausgabe 13/2020

Einloggen

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

search-config
loading …

Abstract

A novel nature-inspired meta-heuristic optimization algorithm, named artificial ecosystem-based optimization (AEO), is presented in this paper. AEO is a population-based optimizer motivated from the flow of energy in an ecosystem on the earth, and this algorithm mimics three unique behaviors of living organisms, including production, consumption, and decomposition. AEO is tested on thirty-one mathematical benchmark functions and eight real-world engineering design problems. The overall comparisons suggest that the optimization performance of AEO outperforms that of other state-of-the-art counterparts. Especially for real-world engineering problems, AEO is more competitive than other reported methods in terms of both convergence rate and computational efforts. The applications of AEO to the field of identification of hydrogeological parameters are also considered in this study to further evaluate its effectiveness in practice, demonstrating its potential in tackling challenging problems with difficulty and unknown search space. The codes are available at https://​www.​mathworks.​com/​matlabcentral/​fileexchange/​72685-artificial-ecosystem-based-optimization-aeo.
Vorheriger Artikel Neural network-supported patient-adaptive fall prevention system
Nächster Artikel A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection

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
Anhänge
Nur mit Berechtigung zugänglich
Literatur
1.
Adeli H, Cheng NT (1993) Integrated genetic algorithm for optimization of space structures. J Aerosp Eng ASCE 6(4):315–328CrossRef
2.
Wang L, Zhao W, Tian Y, Pan G (2018) A bare bones bacterial foraging optimization algorithm. Cognit Syst Res 52:301–311CrossRef
3.
Talbi EG (2009) Metaheuristics: from design to implementation, vol 74. Wiley, New YorkMATHCrossRef
4.
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
5.
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of sixth international symposium on micro machine and human science (SMMHS-1995), pp 39–43
6.
Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B 26(1):29–41CrossRef
7.
8.
Storn R, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359MathSciNetMATHCrossRef
9.
Milner S, Davis C, Zhang H, Llorca J (2012) Nature-inspired self-organization, control, and optimization in heterogeneous wireless networks. IEEE Trans Mob Comput 11(7):1207–1222CrossRef
10.
Ayman AA (2011) Pid parameters optimization using genetic algorithm technique for electrohydraulic servo control system. Intell Control Autom 2:888–896
11.
Hamidreza RK, Karim F (2008) An improved feature selection method based on ant colony optimization (ACO) evaluated on face recognition system. Appl Math Comput 205:716–725MATH
12.
Zhang H, Cao X, Ho JK, Chow TW (2016) Object-level video advertising: an optimization framework. IEEE Trans Ind Inf 13(2):520–531CrossRef
13.
Lai C, Shao Q, Chen X, Wang Z (2016) Flood risk zoning using a rule mining based on ant colony algorithm. J Hydrol 542:268–280CrossRef
14.
Tarek H, Mohamed S, Moustafa K (2011) Incorporating rework into construction schedule analysis. Autom Constr 20:1051–1059CrossRef
15.
Nayak B, Misra B, Choudhury TR (2018) Meta-heuristic optimization algorithms for design of gain constrained state variable filter. Int J Electron Commun (AEÜ) 93:7–18CrossRef
16.
Hare W, Nutini J, Tesfamariam S (2013) A survey of non-gradient optimization methods in structural engineering. Adv Eng Softw 59:19–28CrossRef
17.
Mühlenbein H, Gorges-Schleuter M, Krämer O (1988) Evolution algorithms in combinatorial optimization. Parallel Comput 7(1):65–85MATHCrossRef
18.
Geem ZW, Kim J, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Trans Simul 76(2):60–68CrossRef
19.
Krause J, Cordeiro J, Parpinelli RS, Lopes HS (2013) A survey of swarm algorithms applied to discrete optimization problems. In: Yang X-S, Cui Z, Xiao R, Gandomi AH, Karamanoglu M (eds) Swarm intelligence and bio-inspired computation: theory and applications. Elsevier Science & Technology Books, Elsevier, London, pp 169–191CrossRef
20.
Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst 22(3):52–67MathSciNetCrossRef
21.
De Falco I, Della Cioppa A, Maisto D, Scafuri U, Tarantino E (2012) Biological invasion-inspired migration in distributed evolutionary algorithms. Inf Sci 207:50–65CrossRef
22.
23.
Vikhar P (2016) Evolutionary algorithm: a classical search and optimization technique. Int J Pure Appl Res Eng Technol 4(9):758–766
24.
Corno F, Reorda MS, Squillero G (1998) A new evolutionary algorithm inspired by the selfish gene theory. In: IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence, vol 1976. IEEE, pp 575–580
25.
Eusuff MM, Lansey KE (2003) Optimizing of water distribution network design using the shuffled frog leaping algorithm. J Water Resour Plan Manag 129(3):210–225CrossRef
26.
Simon D (2009) Biogeography-based optimization. IEEE Trans Evolut Comput 12(6):702–713CrossRef
27.
Uymaz SA, Tezel G, Yel E (2015) Artificial algae algorithm (AAA) for nonlinear global optimization. Appl Soft Comput 31:153–171CrossRef
28.
Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144MathSciNetMATH
29.
Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26:69–74CrossRef
30.
Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci 192:120–142CrossRef
31.
Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132MathSciNetMATH
32.
Oftadeh R, Mahjoob MJ, Shariatpanahi M (2010) A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput Math Appl 60(7):2087–2098MATHCrossRef
33.
Kiran MS (2015) TSA: tree-seed algorithm for continuous optimization. Expert Syst Appl 42(19):6686–6698CrossRef
34.
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67CrossRef
35.
Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47CrossRef
36.
Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Nature and biologically inspired computing, NaBIC 2009, world congress on IEEE, pp 210–214
37.
Fathollahi-Fard AM, Hajiaghaei-Keshteli M, Tavakkoli-Moghaddam R (2018) The social engineering optimizer (SEO). Eng Appl Artif Intell 72:267–293CrossRef
38.
Kaveh A, Farhoudi N (2013) A new optimization method: dolphin echolocation. Adv Eng Softw 59:53–70CrossRef
39.
Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio Inspired Comput 2(2):78–84CrossRef
40.
Li MD, Zhao H, Weng XW, Han T (2016) A novel nature-inspired algorithm for optimization: virus colony search. Adv Eng Softw 92:65–88CrossRef
41.
Zhao W, Wang L, Zhang Z (2019) Supply-demand-based optimization: a novel economics-inspired algorithm for global optimization. IEEE Access 7:73182–73206CrossRef
42.
Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191CrossRef
43.
Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845MathSciNetMATHCrossRef
44.
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):6374–6384CrossRef
45.
Askarzadeh A (2014) Bird mating optimizer: an optimization algorithm inspired by bird mating strategies. Commun Nonlinear Sci Numer Simul 19(4):1213–1228MathSciNetMATHCrossRef
46.
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248MATHCrossRef
47.
Formato RA (2007) Central force optimization: a new metaheuristic with applications in applied electromagnetics. Prog Electromagn Res 77:425–491CrossRef
48.
Bayraktar Z, Komurcu M, Bossard JA, Werner DH (2013) The wind driven optimization technique and its application in electromagnetics. IEEE Trans Antennas Propag 61(5):2745–2757MathSciNetMATHCrossRef
49.
Xie L, Zeng J (2010) The performance analysis of artificial physics optimization algorithm driven by different virtual forces. ICIC Express Lett 4(1):239–244
50.
Abedinpourshotorban H, Shamsuddin SM, Beheshti Z, Jawawi DNA (2016) Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evolut Comput 26:8–22CrossRef
51.
Zhao W, Wang L, Zhang Z (2019) A novel atom search optimization for dispersion coefficient estimation in groundwater. Future Gener Comput Syst 91:601–610CrossRef
52.
Zhao W, Wang L, Zhang Z (2019) Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowl Based Syst 163:283–304CrossRef
53.
Kaveh A, Bakhshpoori T (2016) Water evaporation optimization: a novel physically inspired optimization algorithm. Comput Struct 167:69–85CrossRef
54.
Shah-Hosseini H (2011) Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. Int J Comput Sci Eng 6(1–2):132–140
55.
Chuang CL, Jiang JA (2007) Integrated radiation optimization: inspired by the gravitational radiation in the curvature of space–time. In: IEEE congress on evolutionary computation, CEC 2007. IEEE, pp 3157–3164
56.
Shah-Hosseini H (2009) The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. Int J Bio Inspired Comput 1(1–2):71–79CrossRef
57.
Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–84CrossRef
58.
Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213(3):267–289MATHCrossRef
59.
Zheng M, Liu G, Zhou C, Liang Y, Wang Y (2010) Gravitation field algorithm and its application in gene cluster. Algorithms Mol Biol 5(1):32CrossRef
60.
Javidy B, Hatamlou A, Mirjalili S (2015) Ions motion algorithm for solving optimization problems. Appl Soft Comput 32:72–79CrossRef
61.
Mirjalili SA, Hashim SZM (2012) BMOA: binary magnetic optimization algorithm. Int J Mach Learn Comput 2(3):204CrossRef
62.
63.
Flores JJ, López R, Barrera J (2011) Gravitational interactions optimization. In: International conference on learning and intelligent optimization. Springer, Berlin, pp 226–237
64.
Tamura K, Yasuda K (2011) Primary study of spiral dynamics inspired optimization. IEEE Trans Electr Electron Eng 6(S1):S98–S100CrossRef
65.
Rao RV, Savsani VJ, Vakharia DP (2012) Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci 183(1):1–15MathSciNetCrossRef
66.
Zarand G, Pazmandi F, Pál KF, Zimányi GT (2002) Using hysteresis for optimization. Phys Rev Lett 89(15):150201CrossRef
67.
Shen J, Li Y (2009) Light ray optimization and its parameter analysis. In: International joint conference on computational sciences and optimization, CSO 2009, vol 2. IEEE, pp 918–922
68.
Kripka M, Kripka RML (2008) Big crunch optimization method. In: International conference on engineering optimization, Brazil, pp 1–5
69.
Patel VK, Savsani VJ (2015) Heat transfer search (HTS): a novel optimization algorithm. Inf Sci 324:217–246CrossRef
70.
Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm: a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110:151–166CrossRef
71.
Moghaddam FF, Moghaddam RF, Cheriet M (2012) Curved space optimization: a random search based on general relativity theory. arXiv preprint arXiv:​1208.​2214
72.
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evolut Comput 1(1):67–82CrossRef
73.
O’Neill RV, Deangelis DL, Waide JB, Allen TF, Allen GE (1986) A hierarchical concept of ecosystems, vol 23. Princeton University Press, Princeton
74.
Giannakos MN, Krogstie J, Aalberg T (2016) Video-based learning ecosystem to support active learning: application to an introductory computer science course. Smart Learn Environ 3(1):11CrossRef
75.
76.
Viswanathan GM, Afanasyev V, Buldyrev SV, Murphy EJ, Prince PA, Stanley HE (1996) Lévy flight search patterns of wandering albatrosses. Nature 381:413–415CrossRef
77.
Brown C, Liebovitch LS, Glendon R (2007) Lévy flights in Dobe Ju/’hoansi foraging patterns. Hum Ecol 35:129–138CrossRef
78.
Ning AP, Zhang XY (2013) Convergence analysis of artificial bee colony algorithm. Control Decis 28(10):1554–1558
79.
80.
Luo J, Li X, Chen M (2010) The Markov model of shuffled frog leaping algorithm and its convergence analysis. Dianzi Xuebao (Acta Electronica Sinica) 38(12):2875–2880
81.
Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical report, Nanyang Technological University, Singapore
82.
Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evolut Comput 44:148–175CrossRef
83.
Zhao W, Wang L (2016) An effective bacterial foraging optimizer for global optimization. Inf Sci 329:719–735CrossRef
84.
Nowcki H (1974) Optimization in pre-contract ship design. In: Fujita Y, Lind K, Williams TJ (eds) Computer applications in the automation of shipyard operation and ship design, vol 2. Elsevier, New York, pp 327–338
85.
Ray T, Liew KM (2003) Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans Evolut Comput 7(4):386–396CrossRef
86.
Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10:629–640CrossRef
87.
Zhang M, Luo W, Wang X (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inf Sci 178:3043–3074CrossRef
88.
Wang Y, Cai Z, Zhou Y, Fan Z (2009) Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint handling technique. Struct Multidisc Optim 37:395–413CrossRef
89.
Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35CrossRef
90.
Chickermane H, Gea HC (1996) Structural optimization using a new local approximation method. Int J Numer Methods Eng 39(5):829–846MathSciNetMATHCrossRef
91.
Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112CrossRef
92.
Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249CrossRef
93.
Belegundu AD (1982) A study of mathematical programming methods for structural optimization. Department of Civil and Environmental Engineering, University of Iowa, Iowa City
94.
Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113–127CrossRef
95.
Coello CAC, Montes EM (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inf 16:193–203CrossRef
96.
Coello CAC, Becerra RL (2004) Efficient evolutionary optimization through the use of a cultural algorithm. Eng Optim 36:219–236CrossRef
97.
He Q, Wang L (2006) An effective co-evolutionary particle swarm optimization for engineering optimization problems. Eng Appl Artif Intell 20:89–99CrossRef
98.
He Q, Wang L (2007) A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Appl Math Comput 186:1407–1722MathSciNetMATH
99.
Dos Santos Coelho L (2010) Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Syst Appl 37(2):1676–1683CrossRef
100.
Parsopoulos KE, Vrahatis MN (2005) Unified particle swarm optimization for solving constrained engineering optimization problems. In: International conference on natural computation. Springer, Berlin, pp 582–591
101.
Huang FZ, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186(1):340–356MathSciNetMATH
102.
Mezura-Montes E, Coello CAC (2005) Useful infeasible solutions in engineering optimization with evolutionary algorithms. In: MICAI 2005. Lecture notes in artificial intelligence, vol 3789, pp 652–662
103.
Kannan BK, Kramer SN (1994) An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. J Mech Des 116:405–411CrossRef
104.
Akay B, Karaboga D (2012) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23(4):1001–1014CrossRef
105.
Askarzadeh A (2016) A novel metaheuristic method for solving constrained ngineering optimization problems: crow search algorithm. Comput Struct 169:1–12CrossRef
106.
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67CrossRef
107.
Ngo TT, Sadollah A, Kim JH (2016) A cooperative particle swarm optimizer with stochastic movements for computationally expensive numerical optimization problems. J Comput Sci 13:68–82MathSciNetCrossRef
108.
Wang L, Li LP (2010) An effective differential evolution with level comparison for constrained engineering design. Struct Multidisc Optim 41:947–963CrossRef
109.
Montes E, Coello CAC, Reyes JV (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, pp 131–139
110.
Rao BR, Tiwari R (2007) Optimum design of rolling element bearings using genetic algorithms. Mech Mach Theory 42(2):233–250MATHCrossRef
111.
Gupta S, Tiwari R, Shivashankar BN (2017) Multi-objective design optimization of rolling bearings using genetic algorithm. Mech Mach Theory 42:1418–1443MATHCrossRef
112.
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315CrossRef
113.
Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13(5):2592–2612CrossRef
114.
Osyczka A (2002) Evolutionary algorithms for single and multicriteria design optimization: studies in fuzzyness and soft computing. PhysicaVerlag, HeidelbergMATH
115.
Deb K, Srinivasan A (2005) Innovization: innovative design principles through optimization. Kanpur Genetic Algorithms Laboratory (KanGAL), Indian Institute of Technology Kanpur, KanGAL report number 2005007
116.
Mcelwee CD (1980) Theis parameter evaluation from pumping tests by sensitivity analysis. Ground Water 18(1):56–60CrossRef
117.
Hui J, Bo C, Hongyu P (2009) Groundwater dynamics. Geological Publishing House, Beijing
118.
Srivastava R, Guzman-Guzman A (1998) Practical approximations of the well function. Groundwater 36(5):844–848CrossRef
119.
Hantush MS, Jacob CE (1955) Non-steady radial flow in an infinite leaky aquifer. Trans Am Geophys Union 36(1):95–100MathSciNetCrossRef
120.
Samuel MP, Jha MK (2003) Estimation of aquifer parameters from pumping test data by genetic algorithm optimization technique. J Irrig Drain Div 129(5):348–359CrossRef
121.
Yeh HD, Lin YC, Huang YC (2007) Parameter identification for leaky aquifers using global optimization methods. Hydrol Process 21(7):862–872CrossRef
Metadaten
Titel
Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm
verfasst von
Weiguo Zhao
Liying Wang
Zhenxing Zhang
Publikationsdatum
11.09.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 13/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04452-x

Weitere Artikel der Ausgabe 13/2020

Neural Computing and Applications 13/2020 Zur Ausgabe

Original Article

Stock closing price prediction based on sentiment analysis and LSTM

Original Article

Automated detection of epileptic seizures using successive decomposition index and support vector machine classifier in long-term EEG

Original Article

Multi-scale kernel Fisher discriminant analysis with adaptive neuro-fuzzy inference system (ANFIS) in fault detection and diagnosis framework for chemical process systems

Original Article

Spatial context-based optimal multilevel energy curve thresholding for image segmentation using soft computing techniques

Original Article

Deep convolutional neural network designed for age assessment based on orthopantomography data

Original Article

Virtual reality in the context of Internet of Things

Premium Partner

    Bildnachweise