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
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 23/2020

19.06.2020 | Review

A comprehensive review on water cycle algorithm and its applications

verfasst von: Mohammad Nasir, Ali Sadollah, Young Hwan Choi, Joong Hoon Kim

Erschienen in: Neural Computing and Applications | Ausgabe 23/2020

Einloggen

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

search-config
loading …

Abstract

In recent years, significant attentions have been devoted to design of metaheuristic optimization algorithms in order to solve optimization problems. Metaheuristic optimizers are methods which are inspired by observing the phenomena occurring in nature. In this paper, a comprehensive and exhaustive review has been carried out on water cycle algorithm (WCA) and its applications in a wide variety of study fields. The WCA is one of the novel metaheuristic optimization algorithms which is inspired by water cycle process in nature and how streams and rivers flow into the sea. Good exploitation and exploration capabilities have made the WCA a good alternative for solving large-scale optimization problems. Due to its capabilities and strengths, the WCA has been utilized in many and various majors including mechanical engineering, electrical and electronic engineering, civil engineering, industrial engineering, water resources and hydropower engineering, computer engineering, mathematics, and so forth. A variety of articles based on WCA have been published in different international journals such as Elsevier, Springer, IEEE Transactions, Wiley, Taylor & Francis, and in the proceedings of international conferences as well, since 2012 to the present. Thus, it is highly believed that this paper can be appropriate, beneficial and practical for students, academic researchers, professionals, and engineers. Also, it can be an innovative and comprehensive reference for subsequent academic papers and books relevant to the WCA, optimization methods, and metaheuristic optimization algorithms.
Vorheriger Artikel A systematic mapping study on solving university timetabling problems using meta-heuristic algorithms
Nächster Artikel Multi-scale attention vehicle re-identification

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
Literatur
1.
Glover F (1989) Tabu search—part I. ORSA J Comput 1:190–206MATH
2.
Glover F (1990) Tabu search—part II. ORSA J Comput 2:4–32MATH
3.
Hoos H, Stützle T (2004) Stochastic local search. Foundations and applications. Elsevier, AmsterdamMATH
4.
Merrikh-Bayat F (2015) Metaheuristiv optimization algorithms (with applications in electrical engineering). Jahad Daneshgahi Publication, Tehran
5.
Yaghini M et al (2017) Metaheuristiv optimization algorithms. Jahad Daneshgahi Amirkabir Publication, Tehran
6.
Eshghi K et al (2013) Hybridization optimization and Metaheuristiv Algorithms. Azin Mehr Publication, Tehran
7.
Radosavljević J (2018) Metaheuristic optimization in power engineering. The Institution of Engineering and Technology Press, London
8.
Fister I Jr, Yang X-S, Fister I, Brest J, Fister D (2013) A brief review of nature-inspired algorithms for optimisation. Elektroteh Vestn 80(3):1–7MATH
9.
Abdel-Basset M, Abdel-Fatah L, Kumar Sangaiah A (2018) Chapter 10 Metaheuristic algorithms: a comprehensive review. In: Intelligent data-centric systems, computational intelligence for multimedia big data on the cloud with engineering applications, Academic Press, Cambridge, pp 185–231
10.
Fausto F, Reyna-Orta A, Cuevas E et al (2019) From ants to whales: metaheuristics for all tastes. Artif Intell Rev 53:1–58
11.
Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133
12.
Price KV, Storn RM, Lampinen JA (2005) Different evolution, a practical approach to global optimization. Springer, BerlinMATH
13.
Price K, Storn RM, Lampinen JA (2005) Differential evolution. A practical approach to global optimization. Springer, BerlinMATH
14.
Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713
15.
Rechenberg I (1978) Evolutionsstrategien. Springer, Berlin, pp 83–114
16.
Dasgupta D, Zbigniew M (eds) (2013) Evolutionary algorithms in engineering applications. Springer, Berlin
17.
Koza JR (1992) Genetic programming. MIT Press, CambridgeMATH
18.
Rahmati SHA, Zandieh M (2012) A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem. Int J Adv Manuf Technol 58:1115–1129
19.
Li MD, Zhao H, Weng XW, Han T (2016) A novel nature-inspired algorithm for optimization: virus colony search. Adv Eng Softw 92:65–88
20.
Fogel D (2009) Artificial intelligence through simulated evolution. Wiley-IEEE Press, New YorkMATH
21.
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE international conference on neural networks, pp 1942–1948
22.
Abbass HA ((2001) MBO: marriage in honey bees optimization—a haplometrosis polygynous swarming approach. In: Proceedings of the 2001 congress on evolutionary computation, pp 207–214
23.
Li X (2003) A new intelligent optimization-artificial fish swarm algorithm [Doctor thesis]. Zhejiang University of Zhejiang, China
24.
Colorni A, Dorigo M, Maniezzo V (1991) Distributed optimization by ant colonies. In: European conference on artificial life, Elsevier Publishing, Paris, France, pp 134–142
25.
Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell 1:28–39
26.
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471MathSciNetMATH
27.
Basturk B, Karaboga D (2006) An artificial bee colony (ABC) algorithm for numeric function optimization. In: Proceedings of the IEEE swarm intelligence symposium, pp 12–14
28.
Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inform 1:355–366
29.
Pinto PC, Runkler TA, Sousa JM (2007) Wasp swarm algorithm for dynamic MAX- SAT problems. In: Adaptive and natural computing algorithms, Springer, pp 350–357
30.
Mucherino A, Seref O (2007) Monkey search: a novel metaheuristic search for global optimization. In: AIP conference proceedings, p 162
31.
Yang C, Tu X, Chen J (2007) Algorithm of marriage in honey bees optimization based on the wolf pack search. In: Proceedings of the 2007 international conference on intelligent pervasive computing, IPC, pp 462–467
32.
Lu X, Zhou Y (2008) A novel global convergence algorithm: bee collecting pollen algorithm. In: Advanced intelligent computing theories and applications with aspects of artificial intelligence, Springer, pp 518–525
33.
Yang X-S, Deb S (2009) Cuckoo search via Lévy flights. In: Proceedings of the world congress on nature and biologically inspired computing, NaBIC, pp 210–214
34.
Shiqin Y, Jianjun J, Guangxing Y (2009) A dolphin partner optimization. In: Proceedings of the WRI global congress on intelligent systems, GCIS’09, pp 124–128
35.
Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: Proceedings of the workshop on nature inspired cooperative strategies for optimization (NICSO 2010), Springer, pp 65–74
36.
Yang X-S (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bioinspir Comput 2:78–84
37.
Oftadeh R, Mahjoob MJ, Shariatpanahi M (2010) A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput Math Appl 60:2087–2098MATH
38.
Hedayatzadeh R, AkhavanSalmassi F, Keshtgari M, Akbari R, Ziarati K (2010) Termite colony optimization: a novel approach for optimizing continuous problems. In: 2010 18th Iranian conference on electrical engineering, Isfahan, pp 553–558
39.
Bayraktar Z, Komurcu M, Werner DH (2010) Wind Driven Optimization (WDO): a novel nature-inspired optimization algorithm and its application to electromagnetics. In: 2010 IEEE antennas and propagation society international symposium, Toronto, ON, pp 1–4
40.
Askarzadeh A, Rezazadeh A (2012) A new heuristic optimization algorithm for modeling of proton exchange membrane fuel cell: bird mating optimizer. Int J Energy Res 86(11):3241–3249
41.
Gandomi AH, Alavi AH (2012) Krill Herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845MathSciNetMATH
42.
Pan W-T (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26:69–74
43.
Eskandar H, Sadollah A, Bahreininejad A, Mi H (2012) Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110–111:151–166
44.
Kaveh A, Farhoudi N (2013) A new optimization method: dolphin echolocation. Adv Eng Softw 59:53–70
45.
Li X, Zhang J, Yin M (2014) Animal migration optimization: on optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24:1867–1877
46.
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
47.
Wang R, Zhou Y (2014) Flower pollination algorithm with dimension by dimension improvement. In: Mathematical problems in engineering, p 9
48.
Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98
49.
Yu JQ, Li VOK (2015) A social spider algorithm for global optimization. Appl Soft Comput 30:614–627
50.
Merrikh-Bayat F (2015) The runner-root algorithm: a metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature. Appl Soft Comput 33:292–303
51.
Zheng Y-J (2015) Water wave optimization: a new nature-inspired metaheuristic. Comput Oper Res 55:1–11MathSciNetMATH
52.
Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12
53.
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
54.
Yazdani M, Jolai F (2016) Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J Comput Des Eng 3:24–36
55.
Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47
56.
Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evolut Comput 44:148–175
57.
Asghar Heidari A, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872
58.
Shadravan S, Naji HR, Bardsiri VK (2019) The Sailfish optimizer: a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Eng Appl Artif Intell 80:20–34
59.
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simmulated annealing. Science 220:671–680MathSciNetMATH
60.
Cerný V (1985) Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J Opt Theory Appl 45:41–51MathSciNetMATH
61.
Webster B, Bernhard PJ (2003) A local search optimization algorithm based on natural principles of gravitation. In: Proceedings of the 2003 international conference on information and knowledge engineering (IKE’03), pp 255–261
62.
Erol O, Eksin I (2006) A new optimization method: big bang–big crunch. Adv Eng Softw 37:106–111
63.
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248MATH
64.
Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213:267–289MATH
65.
Formato RA (2007) Central force optimization: a new metaheuristic with applications in applied electromagnetics. Prog Electromagn Res 77:425–491
66.
Alatas B (2011) ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst Appl 38:13170–13180
67.
Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184MathSciNet
68.
Kaveh A, Khayatzad M (2012) A novel meta-heuristic method: ray optimization. Comput Struct 112–113:283–294
69.
Du H, Wu X, Zhuang J (2006) Small-world optimization algorithm for function optimization. In: Advances in natural computation, Springer, pp 264–273
70.
Shah-Hosseini H (2011) Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimization. Int J Comput Sci Eng 6:132–140
71.
Moghaddam FF, Moghaddam RF, Cheriet M (2012) Curved space optimization: a random search based on general relativity theory. arXiv: 1208.2214
72.
Kashan AH (2014) League championship algorithm (LCA): an algorithm for global optimization inspired by sport championships. Appl Soft Comput 16:171–200
73.
Rao RV, Savsani VJ, Vakharia D (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43:303–315
74.
Geem ZW, Kim JH, Loganathan G (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–68
75.
Tan Y, Zhu Y (2010) Fireworks algorithm for optimization. In: Advances in swarm intelligence, Springer, pp 355–364
76.
He S, Wu Q, Saunders J (2006) A novel group search optimizer inspired by animal behavioural ecology. In: Proceedings of the 2006 IEEE congress on evolutionary computation, CEC, pp 1272–1278
77.
He S, Wu QH, Saunders J (2009) Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evol Comput 13:973–990
78.
Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Proceedings of the 2007 IEEE congress on evolutionary computation, CEC, pp 4661–4667
79.
Kaveh A, Mahdavi V (2014) Colliding bodies optimization method for optimum discrete design of truss structures. Comput Struct 139:43–53
80.
Kaveh A, Mahdavi VR (2014) Colling bodies optimization: a novel meta-heuristic method. Comput Struct 139:18–27
81.
Kaveh A (2014) Colliding bodies optimization. In: Advances in metaheuristic algorithms for optimal design of structures, Springer, pp 195–232
82.
Gandomi AH (2014) Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans 53(4):1168–1183
83.
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 13:2592–2612
84.
Moosavian N, Roodsari BK (2013) Soccer league competition algorithm: a new method for solving systems of nonlinear equations. Int J Intell Sci 4:7
85.
Moosavian N, Kasaee RB (2014) Soccer league competition algorithm: a novel meta-heuristic algorithm for optimal design of water distribution networks. Swarm Evol Comput 17:14–24
86.
Dai C, Zhu Y, Chen W (2007) Seeker optimization algorithm. In: Computational intelligence and security, Springer, pp 167–176
87.
Ramezani F, Lotfi S (2013) Social-based algorithm (SBA). Appl Soft Comput 13:2837–2856
88.
Ghorbani N, Babaei E (2014) Exchange market algorithm. Appl Soft Comput 19:177–187
89.
Eita MA, Fahmy MM (2014) Group counseling optimization. Appl Soft Comput 22:585–604
90.
Eita MA, Fahmy MM (2010) Group counseling optimization: a novel approach. In: Bramer M, Ellis R, Petridis M (eds) Research and development in intelligent systems, vol XXVI, Springer, London, pp 195–208
91.
Chen D, Zoe F, Lu R, Wang P (2017) Learning backtracking search optimisation algorithm and its application. Appl Soft Comput 376:71–94
92.
Mousavirad SJ, Ebrahimpour-Komleh H (2017) Human mental search: a new population-based metaheuristic optimization algorithm. Appl Intell 47:850–887
93.
Sadollah A, Sayyaadi H, Yadav A (2018) A dynamic metaheuristic optimization model inspired by biological nervous systems: neural network algorithm. Appl Soft Comput 71:747–782
94.
Rabanal P, Rodrıguez I, Rubio F (2007) Using river formation dynamics to design heuristic algorithms. In: International conference on unconventional computation, UC’07, Springer, pp 163–177
95.
Hosseini HS (2007) Problem solving by intelligent water drops. In: Proceedings of the 2007 IEEE congress on evolutionary computation, CEC’07, IEEE, pp 3226–3231
96.
Yang F-C, Wang Y-P (2007) Water flow-like algorithm for object grouping problems. J Chin Inst Ind Eng 24(6):475–488
97.
Ibrahim A, Rahnamayan M, Martin V (2014) Simulated raindrop algorithm for global optimization. In: 27th Canadian conference on electrical and computer engineering, CCECE’14, IEEE, pp 1–8
98.
Kaveh A, Bakhshpoori T (2016) Water evaporation optimization: a novel physically inspired optimization algorithm. Comput Struct 167:69–85
99.
Kaboli SHA, Selvaraj J, Rahim N (2017) Rain-fall optimization algorithm: a population based algorithm for solving constrained optimization problems. J Comput Sci 19:31–42
100.
101.
Yasrebi M, Eskandar-Baghban A, Parvin H, Mohammadpour M (2018) Optimisation inspiring from behaviour of raining in nature: droplet optimisation algorithm. Int J Bioinspir Comput 12(3):152–163
102.
103.
Camacho-Villalón CL, Dorigo M, Stützle T (2019) The intelligent water drops algorithm: why it cannot be considered a novel algorithm. Swarm Intell 13:1–20
104.
Feo TA, Resende MGC (1995) Greedy randomized adaptive search procedures. J Glob Optim 6:109–133MathSciNetMATH
105.
Hansen P, Mladenović N (1999) An introduction to variable neighborhood search. In: Voss S, Martello S, Osman IH, Roucairol C (eds) Meta-heuristics: advances and trends in local search paradigms for optimization. Kluwer, Boston, pp 433–458
106.
Voudouris C, Tsang EPK (1995) Guided local search. Technical report CSM-247, Department of Computer Science, University of Essex, August
107.
Katayama K, Narihisa H (1999) Iterated local search approach using genetic transformation to the traveling salesman problem. In: Proceedings of GECCO’99, vol 1, Morgan Kaufmann, pp 321–328
108.
Holland JH (1992) Genetic algorithms. Sci Am 267:66–72
109.
110.
Dai P, Liu K, Feng L, Zhang H, Lee VCS, Son SH, Wu X (2019) Temporal information services in large-scale vehicular networks through evolutionary multi-objective optimization. IEEE Trans Intell Transp Syst 20(1):218–231
111.
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):1207–1222
112.
Sadollah A, Nasir M, Geem ZW (2027) Sustainability and optimization: from conceptual fundamentals to applications. Sustainability 2020:12
113.
Sarvi M, Nasiri AI (2015) An optimized fuzzy logic controller by water cycle algorithm for power management of stand-alone hybrid green power generation. Energy Convers Manag 106:118–126
114.
Sadollah A, Eskandar H, Bahreininejad A, Kim JH (2015) Water cycle, mine blast and improved mine blast algorithms for discrete sizing optimization of truss structures. Comput Struct 149:1–16
115.
Kaushal M, Khehra BS, Sharma A (2017) Water cycle algorithm based multi-objective contrast enhancement approach. Optik 140:762–775
116.
Kler D, Sharma P, Banerjee A, Rana KPS, Kumar V (2017) PV cell and module efficient parameters estimation using evaporation rate based water cycle algorithm. Swarm Evolut Comput 35:93–110
117.
Rezk H, Fathy A (2017) A novel optimal parameters identification of triple-junction solar cell based on a recently meta-heuristic water cycle algorithm. Sol Energy 157:778–791
118.
Sadollah A, Eskandar H, Lee H, Yoo DG, Kim JH (2016) Water cycle algorithm: a detailed standard code. SoftwareX 5:37–43
119.
Yao J, Wan Z, Zhao Y, Yu J, Qian C, Fu Y (2019) Resonance suppression for hydraulic servo shaking table based on adaptive notch filter. Shock Vib 2019:1–12
120.
Sadollah A, Kim JH, Eskandar H, Yoo DG (2013) Sizing optimization of sandwich panels having prismatic core using water cycle algorithm. In: 2013 Fourth global congress on intelligent systems, Hong Kong, pp 325–328
121.
Jahan MV, Dashtaki M, Dashtaki M (2015) Water cycle algorithm improvement for solving job shop Scheduling problem. In: 2015 International congress on technology, communication and knowledge (ICTCK), Mashhad, pp 576–581
122.
Khalilpourazari S, Mohammadi M (2016) Optimization of closed-loop supply chain network design: a water cycle algorithm approach. In: 2016 12th international conference on industrial engineering (ICIE), Tehran, pp 41–45
123.
Barzegar A, Sadollah A, Rajabpour L, Su R (2016) Optimal power flow solution using water cycle algorithm. In: 2016 14th International conference on control, automation, robotics and vision (ICARCV), Phuket, pp 1–4
124.
El-Hameed MA, El-Fergany AA (2016) Water cycle algorithm-based load frequency controller for interconnected power systems comprising non-linearity. IET Gener Transm Distrib 10(15):3950–3961
125.
El-Ela RRA, Elkholy MM, Selem SI, Metwally HMB (2017) Parameter estimation of lithium-ion batteries dynamic model based on water cycle algorithm. In: 2017 Nineteenth international middle east power systems conference (MEPCON), Cairo, pp 127–133
126.
Dihem A, Salhi A, Naimi D, Bensalem A (2017) Solving smooth and non-smooth economic dispatch using water cycle algorithm. In: 2017 5th International conference on electrical engineering: Boumerdes (ICEE-B), Boumerdes, pp 1–6
127.
Hazra A, Das S, Sarkar P, Laddha A, Basu M (2017) Optimal allocation and sizing of multiple DG and capacitor banks considering load variations using water cycle algorithm. In: 2017 4th International conference on power, control and embedded systems (ICPCES), Allahabad, pp 1–6
128.
El-Ela AAA, El-Sehiemy RA, Abbas AS (2018) Optimal placement and sizing of distributed generation and capacitor banks in distribution systems using water cycle algorithm. IEEE Syst J 12(4):3629–3636
129.
El-Azab HI, Swief RA, El-Amary NH, Temraz HK (2018) Decarbonized unit commitment applying water cycle algorithm integrating plug-in electric vehicles. In: 2018 Twentieth international middle east power systems conference (MEPCON), Cairo, Egypt, pp 455–462
130.
Hato MM, Bouallègue S, Ayadi M (2018) Water cycle algorithm-tuned PI control of a doubly fed induction generator for wind energy conversion. In: 2018 9th International renewable energy congress (IREC), Hammamet, pp 1–6
131.
Tuba E, Strumberger I, Tuba I, Bacanin N, Tuba M (2018) Water cycle algorithm for solving continuous P-median problem. In: 2018 IEEE 12th international symposium on applied computational intelligence and informatics (SACI), Timisoara, pp 000351–000354
132.
Hasanien HM, Matar M (2018) Water cycle algorithm-based optimal control strategy for efficient operation of an autonomous microgrid. IET Gener Transm Distrib 12(21):5739–5746
133.
Hasanien HM (2019) Transient stability augmentation of a wave energy conversion system using a water cycle algorithm-based multiobjective optimal control strategy. IEEE Trans Ind Inform 15(6):3411–3419
134.
Korashy A, Kamel S, Youssef A, Jurado F (2018) Evaporation rate water cycle algorithm for optimal coordination of direction overcurrent relays. In: 2018 Twentieth international middle east power systems conference (MEPCON), Cairo, Egypt, pp 643–648
135.
Yang X, Yao K, Meng W, Yang L (2019) Optimal scheduling of CCHP with distributed energy resources based on water cycle algorithm. IEEE Access 7:105583–105592
136.
Ghaffarzadeh N (2015) Water cycle algorithm based power system stabilizer robust design for power systems. J Electr Eng 66(2):91–96MathSciNet
137.
Elkholy MM, Abd-Elkader F (2019) Optimal energy saving of doubly fed induction motor based on scalar rotor voltage control and water cycle algorithm. In: COMPEL: the international journal for computation and mathematics in electrical and electronic engineering
138.
Haroon SS, Malik TN (2016) Evaporation rate based water cycle algorithm for the environmental economic scheduling of hydrothermal energy systems. J Renew Sustain Energy 8:4
139.
Haroon SS, Malik TN (2017) Evaporation rate-based water cycle algorithm for short-term hydrothermal scheduling. Arab J Sci Eng 42(7):2615–2630
140.
Jafar RMS, Geng S, Ahmad W, Hussain S, Wang H (2018) A comprehensive evaluation: water cycle algorithm and its applications. In: Qiao J et al (eds) Bio-inspired computing: theories and applications. BIC-TA 2018. Communications in computer and information science, vol 952, Springer, Singapore
141.
Khalilpourazari S, Pasandideh SHR, Ghodratnama A (2018) Robust possibilistic programming for multi-item EOQ model with defective supply batches: whale optimization and water cycle algorithms. In: Neural computing and applications, pp 1–28
142.
Hadjaissa A, Ameur K, Boutoubat M (2019) AWCA-based optimization of a fuzzy sliding-mode controller for stand-alone hybrid renewable power system. Soft Comput 23(17):7831–7842
143.
Nayak SK, Padhy SK, Panda CS (2018) Efficient multiprocessor scheduling using water cycle algorithm. In: Pant M, Ray K (eds), Soft computing: theories and applications, vol 583, pp 559–568
144.
El-Fergany AA, Hasanien HM (2019) Water cycle algorithm for optimal overcurrent relays coordination in electric power systems. Soft Comput 23:1–18
145.
Praneeth P, Vasan A, Srinivasa Raju K (2019) Pipe size design optimization of water distribution networks using water cycle algorithm. In: Harmony search and nature inspired optimization algorithms, pp 1057–1067
146.
147.
148.
Mahdavi-Nasab N, Abouei Ardakan M, Mohammadi M (2019) Water cycle algorithm for solving the reliability-redundancy allocation problem with a choice of redundancy strategies. Commun Stat Theory Methods 49:2728–2748MathSciNet
149.
El-Hay EA, Elkholy M (2018) Optimal dynamic and steady-state performance of switched reluctance motor using water cycle algorithm. IEEJ Trans Electr Electron Eng 13(6):882–890
150.
151.
Bahl J, Muralidharan BJ (2019) Optimization of a hybrid phase-change memory cell using the water cycle algorithm. J Comput Electron 18(4):1192–1200
152.
153.
Latif A, Das DC, Ranjan S, Barik AK (2019) Comparative performance evaluation of WCA-optimised non-integer controller employed with WPG–DSPG–PHEV based isolated two-area interconnected microgrid system. IET Renew Power Gener 13(5):725–736
154.
Tuba E, Dolicanin E, Tuba M (2018) Water cycle algorithm for robot path planning. In: 2018 10th International conference on electronics, computers and artificial intelligence (ECAI), Iasi, Romania, pp 1–6
155.
156.
157.
158.
159.
160.
Muhammad MA, Mokhlis H, Naidu K, Amin A, Franco JF, Othman M (2020) Distribution network planning enhancement via network reconfiguration and DG integration using dataset approach and water cycle algorithm. J Mod Power Syst Clean Energy 8(1):86–93
161.
El-sayed M, El-Hameed M, El-Arini M (2019) Effective network reconfiguration with distributed generation allocation in radial distribution networks using water cycle algorithm. Egypt Int J Eng Sci Technol 28:9–21
162.
163.
164.
Mohamed TH, Elnoby AM, Hassan A, Abdelmoety AB, Abdelhameed S (2019) Load frequency control of single area power system using Water Cycle Algorithm. In: 2019 Proceedings of 5th international conference on energy engineering, Aswan, Egypt
165.
166.
167.
Barakat M, Donkol A, AlRahall H, Salama GM, Hesham FA (2019) Water cycle algorithm optimized a centralized PID controller for frequency stability of a real hybrid power system. In: 2019 21st International middle east power systems conference (MEPCON), Cairo, Egypt, pp 1112–1118
168.
Fodhil F, Hamidat A, Nadjemi O, Alliche Z, Berkani L (2020) Optimum design of a hybrid photovoltaic/diesel/battery/system using water cycle algorithm. In: Hatti M (eds) Smart energy empowerment in smart and resilient cities, ICAIRES 2019. Lecture notes in networks and systems, vol 102, Springer, Cham
169.
Guo J, Gao X, Tian M (2017) A gravitation-based chaos water cycle algorithm for numerical optimization. In: 2017 13th International conference on computational intelligence and security (CIS), Hong Kong, pp 224–228
170.
Xu Y, Mei Y (2018) A modified water cycle algorithm for long-term multi-reservoir optimization. Appl Soft Comput 71:317–332
171.
Yanjun K, Yadong M, Weinan L, Xianxun W, Yue B (2017) An enhanced water cycle algorithm for optimization of multi-reservoir systems. In: 2017 IEEE/ACIS 16th International conference on computer and information science (ICIS), Wuhan, pp 379–386
172.
Heidari AA, Ali Abbaspour R, Rezaee Jordehi A (2017) An efficient chaotic water cycle algorithm for optimization tasks. Neural Comput Appl 28:57–85
173.
Adam MMH, Hannoon NMS, Dhar S (2020) New modified water cycle optimized fuzzy PI controller for improved stability of photovoltaic-based distributed generation towards microgrid integration. In: Sharma R, Mishra M, Nayak J, Naik B, Pelusi D (eds) Innovation in electrical power engineering, communication, and computing technology. Lecture notes in electrical engineering, vol 630, Springer, Singapore
174.
Méndez E, Castillo O, Soria J, Sadollah A (2017) Fuzzy dynamic adaptation of parameters in the water cycle algorithm. Nat Inspir Des Hybrid Intell Syst 667:297–311
175.
Méndez E, Castillo O, Soria J, Melin P, Sadollah A (2016) Water cycle algorithm with fuzzy logic for dynamic adaptation of parameters. Adv Comput Intell 10061:250–260
176.
Wang J, Liu S (2018) Novel binary encoding water cycle algorithm for solving Bayesian network structures learning problem. Knowl Based Syst 150:95–110
177.
Gao K, Zhang Y, Sadollah A, Lentzakis A, Su R (2017) Jaya, harmony search and water cycle algorithms for solving large-scale real-life urban traffic light scheduling problem. Swarm Evolut Comput 37:58–72
178.
Osaba E, Del Ser J, Sadollah A, Bilbao MN, Camacho D (2018) A discrete water cycle algorithm for solving the symmetric and asymmetric traveling salesman problem. Appl Soft Comput 71:277–290
179.
Gao K, Duan P, Su R, Li J (2017) Bi-objective water cycle algorithm for solving remanufacturing rescheduling problem. In: Simulated evolution and learning, pp 671–683
180.
Bahreininejad A (2019) Improving the performance of water cycle algorithm using augmented Lagrangian method. Adv Eng Softw 132:55–64
181.
182.
Luo Q, Wen C, Qiao S, Zhou Y (2016) Dual-system water cycle algorithm for constrained engineering optimization problems. Intell Comput Theor Appl 9771:730–741
183.
Heidari AA, Abbaspour RA, RezaeeJordehi A (2017) Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems. Appl Soft Comput 57:657–671
184.
Abedi Pahnehkolaei SM, Alfi A, Sadollah A, Kim JH (2017) Gradient-based water cycle algorithm with evaporation rate applied to chaos suppression. Appl Soft Comput 53:420–440
185.
Korashy A, Kamel S, Youssef A-R, Jurado F (2019) Modified water cycle algorithm for optimal direction overcurrent relays coordination. Appl Soft Comput 74:10–25
186.
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
187.
Qiao S, Zhou Y, Wang R, Zhou Y (2015) Self-adaptive percolation behavior water cycle algorithm. Intell Comput Theor Methodol 9225:85–96
188.
Niu B, Liu H, Song X (2019) An inter-peer communication mechanism based water cycle algorithm. Adv Swarm Intell 11655:50–59
189.
Chen C, Wang P, Dong H, Wang X (2019) Enhanced water cycle algorithm with active learning and return strategy. In: 2019 IEEE congress on evolutionary computation (CEC), Wellington, New Zealand, pp 634–640
190.
Qiao S, Zhou Y, Zhou Y et al (2016) A simple water cycle algorithm with percolation operator for clustering analysis. Soft Comput 23(12):4081–4095
191.
192.
Mishra S, Lenka SR, Satapathy P, Nayak P (2020) Optimum design of PV-battery-based microgrid with mutation volatilization-dependent water cycle algorithm. In: Sharma R, Mishra M, Nayak J, Naik B, Pelusi D (eds) Innovation in electrical power engineering, communication, and computing technology. Lecture notes in electrical engineering, vol 630, Springer, Singapore
193.
Chen C, Wang P, Dong H, Wang X (2020) Hierarchical learning water cycle algorithm. Appl Soft Comput 86:105935
194.
Alatas B (2010) Chaotic harmony search algorithms. Appl Math Comput 216:2687–2699MATH
195.
Soleimanian GF, Gholizadeh H (2019) A comprehensive survey: whale optimization algorithm and its applications. Swarm Evolut Comput 48:1–24
196.
Schuster HG, Just W (2006) Deterministic chaos: an introduction. Wiley, HobokenMATH
197.
Sadollah A, Eskandar H, Kim JH (2015) Water cycle algorithm for solving constrained multi-objective optimization problems. Appl Soft Comput 27:279–298
198.
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–190
199.
Coello CAC (2000) An updated survey of GA-based multi-objective optimization techniques. ACM Comput Surv 32(2):109–143
200.
Wang L, Zhong X, Liu M (2012) A novel group search optimizer for multi-objective optimization. Expert Syst Appl 39(3):2939–2946
201.
Wang L, Zhong X, Liu M (2012) A novel group search optimizer for multi-objective optimization. Expert Syst Appl 39:2939–2946
202.
Lin Q, Chen J (2013) A novel micro-population immune multi-objective optimization algorithm. Expert Syst Appl 40:1590–1601MATH
203.
Saini N et al (2018) Extractive single document summarization using multi-objective optimization: exploring self-organized differential evolution, grey wolf optimizer and water cycle algorithm. Knowl Based Syst 164:45–67
204.
205.
Deihimi A, Keshavarz ZB, Iravani R (2016) An interactive operation management of a micro-grid with multiple distributed generations using multi-objective uniform water cycle algorithm. Energy 106:482–509
206.
Khodabakhshian A, Esmaili MR, Bornapour M (2016) Optimal Coordinated Design Of UPFC And PSS for improving power system performance by using multi-objective water cycle algorithm. Int J Electr Power Energy Syst 83:124–133
207.
Veeramani C, Sharanya S (2018) Analyzing the performance measures of multi-objective water cycle algorithm for multi-objective linear fractional programming problem. In: 2018 Second international conference on intelligent computing and control systems (ICICCS), Madurai, India, pp 297–306
208.
Moradi M, Sadollah A, Eskandar H, Eskandar H (2017) The application of water cycle algorithm to portfolio selection. Econ Res Ekonomska Istraživanja 30(1):1277–1299
209.
Sadollah A, Eskandar H, Bahreininejad A, Kim JH (2015) Water cycle algorithm for solving multi-objective optimization problems. Soft Comput 19(9):2587–2603
210.
Elhameed MA, El-Fergany AA (2017) Water cycle algorithm-based economic dispatcher for sequential and simultaneous objectives including practical constraints. Appl Soft Comput 58:145–154
211.
Wang XJ, Gao L, Zhang CY, Shao XY (2010) A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem. Int J Adv Manuf Technol 51(5–8):757–767
212.
Yang XS (ed) (2015) Recent advances in swarm intelligence and evolutionary computation. In: Studies in computational intelligence, Springer, Switzerland
213.
Malek M, Guruswamy M, Owens H, Pandya M (1989) A hybrid algorithm technique, University of Texas at Austin, Austin, TX
214.
Tao F et al (2015) Configurable intelligent optimization algorithm. Springer series in advanced manufacturing. Springer, BerlinMATH
215.
Wu TH, Chang CC, Yeh JY (2009) A hybrid heuristic algorithm adopting both boltzmann function and mutation operator for manufacturing cell formation problems. Int J Prod Econ 120(2):669–688
216.
Wang L, Pan QK, Suganthan PN, Wang WH, Wang YM (2010) A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems. Comput Oper Res 37(3):509–520MathSciNetMATH
217.
Li JQ, Pan QK, Liang YC (2010) An effective hybrid tabu search algorithm for multiobjective flexible job-shop scheduling problems. Comput Ind Eng 59(4):647–662
218.
Zhao F, Hong Y, Yu D, Yang Y (2010) A hybrid particle swarm optimization algorithm and fuzzy logic for processing planning and production scheduling integration in holonic manufacturing systems. Int J Comput Integr Manuf 23(1):20–39
219.
Akpinar S, Bayhan GM, Baykasoglu A (2013) Hybridizing ant colony optimization via genetic algorithm for mixed-model assembly line balancing problem with sequence dependent setup times between tasks. Appl Soft Comput 13(1):574–589
220.
Muller LF, Spoorendonk S, Pisinger D (2012) A hybrid adaptive large neighborhood search heuristic for lot-sizing with setup times. Eur J Oper Res 218(3):614–623MathSciNetMATH
221.
Moradinasab N, Shafaei R, Rabiee M, Ramezani P (2013) No-wait two stage hybrid flow shop scheduling with genetic and adaptive imperialist competitive algorithms. J Exp Theor Artif Intell 25(2):207–225
222.
Yun YS, Moon C, Kim D (2009) Hybrid genetic algorithm with adaptive local search scheme for solving multistage-based supply chain problems. Comput Ind Eng 56(3):821–838
223.
Praepanichawat C, Khompatraporn C, Jaturanonda C, Chotyakul C (2015) Water cycle and artificial bee colony based algorithms for optimal order allocation problem with mixed quantity discount scheme. In: Industrial engineering, management science and applications, pp 229–239
224.
Soheyl KS, Khalilpourazary S (2017) An efficient hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems. Soft Comput 23:1699–1722
225.
Mahdavi H, Rahimzadeh Rofooei F, Sadollah A, Xu C (2018) A wavelet-based scheme for impact identification of framed structures using combined genetic and water cycle algorithms. J Sound Vib
226.
Al-Rawashdeh G, Mamat R, Hafhizah Binti Abd Rahim N (2019) Hybrid water cycle optimization algorithm with simulated annealing for spam E-mail detection. IEEE Access 7:143721–143734
227.
Jeddi S, Sharifian S (2019) A water cycle optimized wavelet neural network algorithm for demand prediction in cloud computing. Cluster Comput 22:1–16
228.
Kandhway P, Kumar Bhandari A (2018) A water cycle algorithm-based multilevel thresholding system for color image segmentation using Masi entropy. Circuits Syst Signal Process 2018:1–49
229.
230.
231.
Soto R, Crawford B, Lanza-Gutierrez JM, Olivares R, Camacho P, Astorga G, de la Fuente-Mella H, Paredes F, Castro C (2019) Solving the manufacturing cell design problem through an autonomous water cycle algorithm. Appl Sci 9:4736
232.
Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249
233.
Kallrath J, Pardalos PM, Rebennack S, Scheidt M (2009) Optimization in the energy industry. Springer, BerlinMATH
234.
Eremia M, Liu CC, Edris AA (2016) Advanced solutions in power systems HVDC, facts, and artificial intelligence. IEEE Press-Wiley, New York
235.
Li X, Wang Z, Xu L, Liu J (1999) Combined construction of wavelet neural networks for nonlinear system modeling. IFAC Proc Vol 32(2):5153–5158
236.
Vinay Kumar K, Ravi V, Carr M, Raj Kiran N (2008) Software development cost estimation using wavelet neural networks. J Syst Softw 81:1853–1867
237.
Zhang Q, Benveniste A (1992) Wavelet networks. IEEE Trans Neural Netw 3:889–898
238.
Manuel GJ, Gutés A, Céspedes F, Valle M, Muñoz R (2008) Wavelet neural networks to resolve the overlapping signal in the voltammetric determination of phenolic compounds. Talanta 76:373–381
239.
Domínguez Mayorga CR, Espejel Rivera MA, Ramos Velasco LE, Ramos Fernández JC, Escamilla Hernández E (2011) Wavelet neural network algorithms with applications in approximation signals. In: Advances soft computing, pp 374–385
240.
Subasi A, Yilmaz M, Ozcalik H (2006) Classification of EMG signals using wavelet neural network. J Neurosci Methods 156:360–367
241.
Daubechies I (1992) Ten lectures on wavelets. CBMS-NSF regional series in applied mathematics, vol 61, SIAM, Philadelphia
242.
Sharma V et al (2016) Short term solar irradiance forecasting using a mixed wavelet neural network. Renew Energy 90:481–492
243.
Lutfy O (2014) Wavelet neural network model reference adaptive control trained by a modified artificial immune algorithm to control nonlinear systems. Arab J Sci Eng 39(6):4737–4751
244.
Duan F et al (2016) sEMG-based identification of hand motion commands using wavelet neural network combined with discrete wavelet transform. IEEE Trans Ind Electron 63(3):1923–1934
245.
Suryanarayana Ch et al (2014) An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India. Neurocomputing 145:324–335
246.
247.
Chen Q, Liu B, Zhang Q, Liang JJ, Suganthan PN, Qu BY (2014) Problem definition and evaluation criteria for CEC 2015 special session and competition on bound constrained single-objective computationally expensive numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Nanyang Technological University, Singapore, Technical Report
Metadaten
Titel
A comprehensive review on water cycle algorithm and its applications
verfasst von
Mohammad Nasir
Ali Sadollah
Young Hwan Choi
Joong Hoon Kim
Publikationsdatum
19.06.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 23/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05112-1

Weitere Artikel der Ausgabe 23/2020

Neural Computing and Applications 23/2020 Zur Ausgabe

Original Article

Extension of labeled multiple attribute decision making based on fuzzy neighborhood three-way decision

Correction

Correction to: Generative image completion with image-to-image translation

Original Article

Electronic word-of-mouth effects on studio performance leveraging attention-based model

Original Article

Camera calibration by using weighted differential evolution algorithm: a comparative study with ABC, PSO, COBIDE, DE, CS, GWO, TLBO, MVMO, FOA, LSHADE, ZHANG and BOUGUET

S.I. : Emerging applications of Deep Learning and Spiking ANN

A CNN–LSTM model for gold price time-series forecasting

S.I. : Emerging applications of Deep Learning and Spiking ANN

Affective analysis of patients in homecare video-assisted telemedicine using computational intelligence