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Evolutionary Intelligence
Erschienen in: Evolutionary Intelligence 2/2023

12.11.2021 | Review Article

Evolutionary algorithm applications for IoTs dedicated to precise irrigation systems: state of the art

verfasst von: Soumaya Ferhat Taleb, Nour El-Houda Benalia, Rabah Sadoun

Erschienen in: Evolutionary Intelligence | Ausgabe 2/2023

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Abstract

The world is currently undergoing water scarcity problems, even if it seems to be the most abundant resource on earth. However, the freshwater account is really in small amounts, while agriculture production consumes 70% of the majority of water withdrawals than any other source. Therefore, in order to preserve it, the irrigation operation has to be optimized by controlling efficiently the water used for irrigation. For that purpose, several technologies can be applied, such as the internet of things (IoT) technology which can perform as decision support in the irrigation process. The precise irrigation systems based on IoT involve several intricacies such as huge amounts of data and integration of large system components, which makes it difficult to be optimized analytically or with deterministic methods. For this reason, it was necessary to develop stochastic multi-objective optimization methods such as the evolutionary algorithms (EAs), which can solve complicated problems with a large number of parameters in relation. The EAs may be of relevant use except that they introduce processing time constraints. In this article, we aim at making a state of the art about the use of EAs combined with IoT and applied to precise irrigation. We will focus particularly on their uses classifications as well as the manner in which they have been implemented to reduce their computing times in distributed computing architectures, particularly those using the cloud, as well as in hardware accelerators forms.
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Literatur
1.
Burles D (1995) Dimensions of need: an atlas of food and agriculture. Food and Agriculture Organization, Rome
2.
Mekonnen MM, Hoekstra AY (2016) Four billion people facing severe water scarcity. Sci Adv 2(2):e1500323CrossRef
3.
4.
Bellware K (2016) Global water shortage risk is worse than scientists thought. Huffington Post, New York
5.
Ercin AE, Hoekstra AY (2014) Water footprint scenarios for 2050: a global analysis. Environ Int 64:71–82CrossRef
6.
Huang Z, Hejazi M, Tang Q, Vernon CR, Liu Y, Chen M, Calvin K (2019) Global agricultural green and blue water consumption under future climate and land use changes. J Hydrol 574:242–256CrossRef
7.
Puri V, Nayyar A, Raja L (2017) Agriculture drones: a modern breakthrough in precision agriculture. J Stat Manag Syst 20(4):507–518
8.
Sophie L, Antoine P (2016) L’agriculture de précision: pourquoi, pour qui et par oú commencer? , Département de génie des bioressources, Université McGill
9.
Pierce FJ, Nowak P (1999) Aspects of precision agriculture: In: Advances in agronomy, vol 67. Academic Press, , pp 1–85
10.
Angelopoulou T, Tziolas N, Balafoutis A, Zalidis G, Bochtis D (2019) Remote sensing techniques for soil organic carbon estimation: a review. Remote Sens 11(6):676CrossRef
11.
Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318CrossRef
12.
Islam SM, Gaihre YK, Biswas JC, Jahan MS, Singh U, Adhikary SK, Saleque MA (2018) Different nitrogen rates and methods of application for dry season rice cultivation with alternate wetting and drying irrigation: fate of nitrogen and grain yield. Agric Water Manag 196:144–153CrossRef
13.
Adeyemi O, Grove I, Peets S, Norton T (2017) Advanced monitoring and management systems for improving sustainability in precision irrigation. Sustainability 9(3):353CrossRef
14.
Wang J, Niu W, Guo L, Liang B, Li Y (2017) Suitable buried depth of drip irrigation improving yield and quality of tomato in greenhouse. Trans Chin Soc Agric Eng 33(20):90–97
15.
Mouradi A, Yacine ZA, El Harti A (2018) Study of the technical performance of localized irrigation and its environmental and agro economic impact in the first areas of collective reconversion at the irrigated perimeter of the Tadla Beni Moussa perimeter of the west Morocco. In: E3S Web of conferences vol 37. EDP Sciences, p 01009
16.
Cahn MD, Johnson LF (2017) New approaches to irrigation scheduling of vegetables. Horticulturae 3(2):28CrossRef
17.
Kumawat S, Bhamare M, Nagare A, Kapadnis A (2017) Sensor based automatic irrigation system and soil pH detection using image processing. Int Res J Eng Technol 4(4):3673–3675
18.
Kamienski C, Soininen JP, Taumberger M, Dantas R, Toscano A, Salmon Cinotti T, Torre Neto A (2019) Smart water management platform: Iot-based precision irrigation for agriculture. Sensors 19(2):276CrossRef
19.
Rao RN, Sridhar B (2018) IoT based smart crop-field monitoring and automation irrigation system. In: 2018 2nd international conference on inventive systems and control (ICISC). IEEE, pp 478–483
20.
Ray PP (2017) Internet of things for smart agriculture: technologies, practices and future direction. J Ambient Intell Smart Environ 9(4):395–420CrossRef
21.
22.
Sutton A (2018) 5G network architecture. J Inst Telecommun Prof 12(1):9–15
23.
Janga Reddy M, Nagesh Kumar D (2021) Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review. H2Open J 3(1):135–188CrossRef
24.
Maier HR, Razavi S, Kapelan Z, Matott LS, Kasprzyk J, Tolson BA (2019) Introductory overview: optimization using evolutionary algorithms and other metaheuristics. Environ Model Softw 114:195–213CrossRef
25.
Maitre O (2011) GPGPU for evolutionary algorithms. Doctoral dissertation, Strasbourg
26.
Alba E, Luque G, Nesmithnow S (2013) Parallel metaheuristics: recent advances and new trends. Int Trans Oper Res 20(1):1–48MATHCrossRef
27.
Fuentes S, Trejo-Alonso J, Quevedo A, Fuentes C, Chãvez C (2020) Modeling soil water redistribution under gravity irrigation with the Richards equation. Mathematics 8(9):1581CrossRef
28.
Yan H, Hui X, Li M, Xu Y (2020) Development in sprinkler irrigation technology in China. Irrig Drain 69:75–87CrossRef
29.
Wang Y, Li S, Qin S, Guo H, Yang D, Lam HM (2020) How can drip irrigation save water and reduce evapotranspiration compared to border irrigation in arid regions in northwest China. Agric Water Manag 239:106256CrossRef
30.
Zeng J, Sun X, Sun Z, Guan J, Han C, Zhao X, Zhao J (2019) Negative pressure wound therapy versus closed suction irrigation system in the treatment of deep surgical site infection after lumbar surgery. World Neurosurg 127:e389–e395CrossRef
31.
Hervé P (2002) FAO publication—How design. Emerging modernization procedures and design standards, management and policy affect the performance of irrigation projects
32.
Goblot H (1979) Les qanats: Une technique d’acquisition de l’eau. De Gruyter, Berlin
33.
Hassani I (1988) Les methodes traditionnelles de captage des eaux souterraines dans le Sahara algerien. Revue Techniques et Sciences 6:20–24
34.
Kendouci MA, Bendida A, Khelfaoui R, Kharroubi B (2013) The impact of traditional irrigation (Foggara) and modern (drip, pivot) on the resource non-renewable groundwater in the Algerian Sahara. Energy Procedia 36:154–162CrossRef
35.
36.
Brouwer C, Goffeau A, Heibloem M (1985) FAO (Food and Agriculture Organization of the United Nations), irrigation water management: Training Manual No. 1–Introduction to Irrigation, chapitre 5:
37.
Niels SÃ, de Paly M, Shamir U (2012) Novel simulation-based algorithms for optimal open-loop and closed-loop scheduling of deficit irrigation systems. J Hydroinf 14(1):136–151CrossRef
38.
Putjaika N, Phusae S, Chen-Im A, Phunchongharn P Akkarajitsakul K (2016) A control system in an intelligent farming by using arduino technologyIn: . Fifth ICT international student project conference (ICT-ISPC), Nakhon Pathom, pp 53–56
39.
Saraf SB, Gawali DH (2017) IoT based smart irrigation monitoring and controlling system. In: 2017 2nd IEEE international conference on recent trends in electronics, information and communication technology (RTEICT). IEEE, pp 815–819
40.
Zazueta FS, Smajstrla AG, Clark GA (1994) Irrigation system controllers. Institute of Food and Agriculture Science, University of Florida (AGE-32), New York
41.
Rhoads Fred M, Dean Yonts C (1991) Irrigation scheduling for Corn-Why and How. National Corn Handbook 20
42.
Fernandez JE (2017) Plant-based methods for irrigation scheduling of woody crops. Horticulturae 3(2):35CrossRef
43.
Caya MVC, Ibarra JBG, Avendano GO, Felipe DJDA, Fernando JAV, Galvez JMT, Sauli Z (2018) Evapotranspiration based irrigation system using raspberry pi for capsicum annuum ‘bell pepper’ plant nursery. J Telecommun Electron Comput Eng (JTEC) 10(1–14):21–24
44.
Shafian S, Maas SJ (2015) Index of soil moisture using raw Landsat image digital count data in Texas high plains. Remote Sens 7(3):2352–2372CrossRef
45.
Norman JM, Campbell G (1983) Application of a plant-environment model to problems in irrigation. In: Advances in irrigation vol 2. Elsevier, pp 155–188
46.
Mechsy LSR, Dias MUB, Pragithmukar W, Kulasekera AL (2017) A mobile robot based watering system for smart lawn maintenance. In: 17th international conference on control, automation and systems (ICCAS)
47.
Khelifa B, Amel D, Amel B, Mohamed C, Tarek B (2015) Smart: irrigation using internet of things. In: 2015 Fourth international conference on future generation communication technology (FGCT)
48.
Shiraz Pasha BR (2014) Dr. B Yogesha: micro-controller Based Automated Irrigation System. Int J Eng Sci (IJES) 3(7):06–09
49.
Yunseop J, Evans RG, Iversen WM (2008) Remote sensing and control of an irrigation system using a distributed wireless sensor network. IEEE Trans Instrum Meas 57:7
50.
Harishankar S, Sathish Kumar R, Sudharsan KP, Vignesh U, Viveknath T (2014) Solar powered smart irrigation system. Adv Electron Electric Eng 4(4):341–346
51.
Pavithra DS, Srinath MS (2014) GSM based automatic irrigation control system for efficient use of resources and crop planning by using an android mobile. IOSR J Mech Civ Eng (IOSR-JMCE) 11(I):49–55
52.
Symeonaki E, Arvanitis K, Piromalis D (2020) A context-aware middleware cloud approach for integrating precision farming facilities into the IoT toward agriculture 4.0. Appl Sci 10(3):813CrossRef
53.
Karim F, Karim F (2017) Monitoring system using web of things in precision agriculture. Procedia Comput Sci 110:402–409CrossRef
54.
55.
Ai Y, Peng M, Zhang K (2018) Edge computing technologies for internet of things: a primer. Digital Commun Netw 4(2):77–86CrossRef
56.
Shi W, Dustdar S (2016) The promise of edge computing. Computer 49(5):78–81CrossRef
57.
Stojmenovic I, Wen S (2014) The fog computing paradigm: Scenarios and security issues. In: 2014 federated conference on computer science and information systems. IEEE, pp 1–8
58.
Rani S, Ahmed SH (2018) Secure edge computing: an architectural approach and industrial use case. Internet Technol Lett 1:e68CrossRef
59.
Vineela MT, NagaHarini J, Kiranmai C, Harshitha G, AdiLakshmi B (2018) IoT based agriculture monitoring and smart irrigation system using Raspberry Pi. Int Res J Eng Technol 5(1):1417–1420
60.
Ghosh S, Sayyed S, Wani K, Mhatre M, Hingoliwala HA (2016) Smart irrigation: a smart drip irrigation system using cloud, android and data mining. In: 2016 IEEE international conference on advances in electronics, communication and computer technology (ICAECCT)
61.
Wang P, Yao C, Zheng Z, Sun G, Song L (2018) Joint task assignment, transmission, and computing resource allocation in multilayer mobile edge computing systems. IEEE Internet Things J 6(2):2872–2884CrossRef
62.
Shabadi L, Patil N, Nikita M, Shruti J, Smitha P, Swati C (2014) Irrigation control system using android and GSM for efficient use of water and power. Int J Adv Res Comput Sci Softw Eng 4(7):607–611
63.
Anbarasi M, Karthikeyan T, Ramanathan L, Ramani S, Nalini N (2019) Smart multi-crop irrigation system using IOT. SCOPE, VIT, Vellore, India
64.
Oh SM, Shin J (2016) An efficient small data transmission scheme in the 3GPP NB-IoT system. IEEE Commun Lett 21(3):660–663CrossRef
65.
66.
Fraga-Lamas P et al. (2020) Design and empirical validation of a lorawan IoT smart irrigation system. In: Multidisciplinary digital publishing institute proceedings, vol 42, No. 1
67.
Reddy MJ, Kumar DN (2012) Computational algorithms inspired by biological processes and evolution. Curr Sci 103:370–380
68.
Pellerin é (2005) Méta-apprentissage des algorithmes génétiques (Doctoral dissertation, Université du Québec á Trois-Rivières)
69.
70.
Rechenberg I (1965) Cybernetic solution path of an experimental problem. Royal Aircraft Establishment Library Translation 1122
71.
Hayes-Roth F (1975) Review of adaptation in natural and artificial systems by John H. Holland, The University of Michigan Press, 1975. ACM SIGART Bulletin 53: 15–15
72.
Gong YJ, Chen WN, Zhan ZH, Zhang J, Li Y, Zhang Q, Li JJ (2015) Distributed evolutionary algorithms and their models: a survey of the state-of-the- art. Appl Soft Comput 34:286–300CrossRef
73.
Hereford JM (2006) A distributed particle swarm optimization algorithm for swarm robotic applications. In: 2006 IEEE international conference on evolutionary computation. IEEE, pp 1678–1685
74.
Lim D, Ong YS, Jin Y, Sendhoff B, Lee BS (2007) Efficient hierarchical parallel genetic algorithms using grid computing. Futur Gener Comput Syst 23(4):658–670CrossRef
75.
Belaqziz S, Mangiarotti S, Le Page M, Khabba S, Er-Raki S, Agouti T, Jarlan L (2014) Irrigation scheduling of a classical gravity network based on the covariance matrix adaptation evolutionary strategy algorithm. Comput Electron Agric 102:64–72CrossRef
76.
Pau M, Locci N, Muscas C (2014) A tool to define the position and the number of irradiance sensors in large PV plants. In: 2014 IEEE international energy conference (ENERGYCON). IEEE, pp 374–379
77.
Mantri G, Kulkarni NR (2013) Design and optimization of PID controller using genetic algorithm. Int J Res Eng Technol 2(6):926–930CrossRef
78.
Kale AP, Sonavane SP (2019) IoT based smart farming: feature subset selection for optimized high-dimensional data using improved GA based approach for ELM. Comput Electron Agric 161:225–232CrossRef
79.
Raju KS, Kumar DN (2004) Irrigation planning using genetic algorithms. Water Resour Manag 18(2):163–176CrossRef
80.
Montgomery J, Fitzgerald A, Randall M, Lewis A (2018) A computational comparison of evolutionary algorithms for water resource planning for agricultural and environmental purposes-2015. In: IEEE congress on evolutionary computation-(CEC)
81.
Creaco E, Fortunato A, Franchini M, Mazzola MR (2014) Comparison between entropy and resilience as indirect measures of reliability in the framework of water distribution network design. Procedia Eng 70:379–388CrossRef
82.
Sirsant S, Reddy MJ (2020) Assessing the performance of surrogate measures for water distribution network reliability. J Water Resour Plan Manag 146(7):04020048CrossRef
83.
Pant M, Rani D (2021) Dynamic programming integrated differential evolution algorithm for determining optimal policy of reservoir. In: Water management and water governance. Springer, Cham, pp 435–447
84.
Kasiviswanathan KS, Sudheer KP, Soundharajan BS, Adeloye AJ (2021) Implications of uncertainty in inflow forecasting on reservoir operation for irrigation. Paddy Water Environ 19(1):99–111CrossRef
85.
Na L et al (2020) Fertigation management for sustainable precision agriculture based on Internet of Things. J Clean Prod 277(2020):124119
86.
EkbataniFard GH, Monsefi R, Akbarzadeh-T MR, Yaghmaee MH (2010) A multi-objective genetic algorithm based approach for energy efficient QoS-routing in two-tiered wireless sensor networks. In: IEEE 5th international symposium on wireless pervasive computing 2010. IEEE, pp 80–85
87.
Elhoseny M, Yuan X, Yu Z, Mao C, El-Minir HK, Riad AM (2014) Balancing energy consumption in heterogeneous wireless sensor networks using genetic algorithm. IEEE Commun Lett 19(12):2194–2197CrossRef
88.
Amine A, Bellatreche L, Elberrichi Z, Neuhold EJ, Wrembel R (eds) (2015) Computer science and its applications: 5th IFIP TC 5 international conference, CIIA 2015, Saida, Algeria, May 20–21, 2015, proceedings, vol 456. Springer
89.
Baranidharan B, Santhi B (2015) GAECH: genetic algorithm based energy efficient clustering hierarchy in wireless sensor networks. Journal of Sens, vol 2015
90.
Baraá AA, Khalil EA, ozdemir S, Yildiz O (2015) A multi-objective disjoint set covers for reliable lifetime maximization of wireless sensor networks. Wirel Pers Commun 81(2):819–838CrossRef
91.
Ghosh S, Snigdh I, Singh A (2016) GA optimal sink placement for maximizing coverage in wireless sensor networks. In: 2016 international conference on wireless communications, signal processing and networking (WiSPNET). IEEE, pp 737–741
92.
Jain TK, Saini DS, Bhooshan SV (2015) Lifetime optimization of a multiple sink wireless sensor network through energy balancing. J Sens, vol 2015
93.
Khan MA, Islam MZ, Hafeez M (2011) Irrigation water requirement prediction through various data mining techniques applied on a carefully pre-processed dataset. J Res Pract Inf Technol 43(22):1–17
94.
Freitas AA (2013) Data mining and knowledge discovery with evolutionary algorithms. Springer, Berlin
95.
Goldstein A, Fink L, Meitin A, Bohadana S, Lutenberg O, Ravid G (2018) Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist’s tacit knowledge. Precis Agric 19(3):421–444
96.
Elferchichi A, Gharsallah O, Nouiri I, Lebdi F, Lamaddalena N (2009) The genetic algorithm approach for identifying the optimal operation of a multi-reservoirs on-demand irrigation system. Biosys Eng 102(3):334–344CrossRef
97.
Safavi HR, Enteshari S (2016) Conjunctive use of surface and ground water resources using the ant system optimization. Agric Water Manag 173:23–34CrossRef
98.
Hendrawan Y, Murase H (2011) Neural-intelligent water drops algorithm to select relevant textural features for developing precision irrigation system using machine vision. Comput Electron Agric 77(2):214–228CrossRef
99.
Dursun M, Karaman MR (2009) Artificial neural network based modeling of spatial distribution of phosphorus on the tomato area. Asian J Chem 21(1):239–247
100.
Khadra R, Lamaddalena N (2006) A simulation model to generate the demand hydrographs in large-scale irrigation systems. Biosys Eng 93(3):335–346CrossRef
101.
Pulido-Calvo I, Roldan J, Lopez-Luque R, Gutierrez-Estrada JC (2003) Water delivery system planning considering irrigation simultaneity. J Irrig Drain Eng 129(4):247–255CrossRef
102.
Dursun M, ozden S (2017) Optimization of soil moisture sensor placement for a PV-powered drip irrigation system using a genetic algorithm and artificial neural network. Electr Eng 99(1):407–419CrossRef
103.
Kuo SF, Merkley GP, Liu CW (2000) Decision support for irrigation project planning using a genetic algorithm. Agric Water Manag 45(3):243–266CrossRef
104.
Huang Y, Lan Y, Thomson SJ, Fang A, Hoffmann WC, Lacey RE (2010) Development of soft computing and applications in agricultural and biological engineering. Comput Electron Agric 71(2):107–127CrossRef
105.
Barros RC, Basgalupp MP, De Carvalho AC, Freitas AA (2011) A survey of evolutionary algorithms for decision-tree induction. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42(3):291–312CrossRef
106.
El-Ghazali T (2009) Metaheuristics from design to implementation. Wiley, LondonMATH
107.
Maitre O, Lachiche N, Clauss P, Baumes L, Corma A, Collet P (2009) Ecient parallel implementation of evolutionary algorithms on GPGPU cards. In: European conference on parallel processing. Springer, Berlin, Heidelberg, pp 974–985
108.
109.
Allaire FC, Tarbouchi M, Labont G, Fusina G (2008) FPGA implementation of genetic algorithms for UAV real-time path planning. In: Unmanned aircraft systems. Springer, Dordrecht, pp 495–510
110.
Walton M, Grewal G, Darlington G (2010)Parallel FPGA-based implementation of scatter search. In: Proceedings of the 12th annual genetic and evolutionary computation conference, GECCO 10, pp 10751082
111.
Fernando PR, Katkoori S, Keymeulen D, Zebulum R, Stoica A (2009) Customizable FPGA IP core implementation of a general-purpose genetic algorithm engine. IEEE Trans Evol Comput 14(1):133–149CrossRef
112.
Jewajinda Y, Chongstitvatana P (2008) FPGA implementation of a cellular compact genetic algorithm. In: 2008 NASA/ESA conference on adaptive hardware and systems. IEEE, pp 385–390
113.
114.
Kok J, Gonzalez LF, Kelson NA, Periaux J (2011) An FPGA-based approach to multi-objective evolutionary algorithms for multi-disciplinary design optimisation
115.
Alba E, Luna F, Nebro AJ, Troya JM (2004) Parallel heterogeneous genetic algorithms for continuous optimization. Parallel Comput 30(5–6):699–719MATHCrossRef
116.
Alba E (2006) Parallel evolutionary computations. In: Nedjah N, de Macedo Mourelle L (eds) Springer, Berlin
117.
Homberger J (2008) A parallel genetic algorithm for the multilevel unconstrained lot-sizing problem. Inf J Comput 20(1):124–132MATHCrossRef
118.
Homberger J, Gehring H (2008) A two-level parallel genetic algorithm for the uncapacitated warehouse location problem. In: Proceedings of the 41st annual Hawaii international conference on system sciences (HICSS 2008). IEEE, pp 67–67
119.
Huang HC, Tsai CC, Lin SC (2009) SoPC-based parallel elite genetic algorithm for global path planning of an autonomous omnidirectional mobile robot. In: 2009 IEEE international conference on systems, man and cybernetics. IEEE, pp 1959–1964
120.
Laredo JLJ, Guervos JJM, Valdivieso PAC (2010) Evolvable agents: a framework for peer-topeer evolutionary algorithms. In: Parallel and distributed computational intelligence. Springer, Berlin, Heidelberg, pp 43–62
121.
Nesmachnow S, Alba E, Cancela (2012) HScheduling in heterogeneous computing and grid environments using a parallel CHC evolutionary algorithm. Comput Intell 28(2):131–155MathSciNetCrossRef
122.
Nesmachnow S, Cancela H, Alba E (2007) Evolutionary algorithms applied to reliable communication network design. Eng Optim 39(7):831–855MathSciNetCrossRef
123.
Nesmachnow S, Cancela H, Alba E (2010) Heterogeneous computing scheduling with evolutionary algorithms. Soft Comput 15(4):685–701CrossRef
124.
Nesmachnow S, Cancela H, Alba E (2012) A parallel micro evolutionary algorithm for heterogeneous computing and grid scheduling. Appl Soft Comput 12(2):626–639CrossRef
125.
126.
Dong G, Fu XA (2010) Hierarchical parallel algorithm of ant system and local search for TSPs. In: The 2nd international conference on information science and engineering. IEEE, pp 4834–4837
127.
Chu D, Zomaya A (2006) Parallel ant colony optimization for 3D protein structure prediction using the HP lattice model. In: Parallel evolutionary computations. Springer, Berlin, Heidelberg, pp 177–198
128.
Hongwei X, Yanhua L (2009) Parallel ACO for DNA sequencing by hybridization. In; 2009 WRI World congress on computer science and information engineering, vol 4. IEEE, pp 602–606
129.
Takova K, Koroec P, Ilc J (2009) A distributed multilevel ant-colony approach for nite element mesh decomposition. In: International conference on parallel processing and applied mathematics. Springer, Berlin, Heidelberg, pp 398–407
130.
Jie X, CaiYun L, Zhong CA (2008) New parallel ant colony optimization algorithm based on message passing interface. In: 2008 IEEE Pacic-Asia workshop on computational intelligence and industrial application, vol 2. IEEE, pp 178–182
131.
Xiong J, Meng X, Liu C (2010) An improved parallel ant colony optimization based on message passing interface. In: International conference in swarm intelligence. Springer, Berlin, Heidelberg, pp 249–256
132.
Yang Z, Yu B, Cheng C (2007) A parallel ant colony algorithm for bus network optimization. Comput Aided Civ Infrastruct Eng 22(1):44–55CrossRef
133.
Bouamama S (2010) A new distributed particle swarm optimization algorithm for constraint reasoning. In: International conference on knowledge-based and intelligent information and engineering systems. Springer, Berlin, Heidelberg, pp 312–321
134.
Hereford JM (2006) A distributed particle swarm optimization algorithm for swarm robotic applications. In: 2006 IEEE international conference on evolutionary computation. IEEE, pp 1678–1685
135.
Durillo JJ, Nebro AJ, Luna F, Alba E (2008) A study of master-slave approaches to parallelize NSGAII. In: 2008 IEEE international symposium on parallel and distributed processing. IEEE, pp 1–8
136.
Boisson JC, Jourdan L, Talbi EG, Horvath D (2008) Parallel multi-objective algorithms for the molecular docking problem. In: 2008 IEEE symposium on computational intelligence in bioinformatics and computational biology. IEEE, pp 187–194
137.
Cancino W, Jourdan L, Talbi EG, Delbem AC (2010) A parallel multi-objective evolutionary algorithm for phylogenetic inference. In: International conference on learning and intelligent optimization. Springer, Berlin, Heidelberg, pp 196–199
138.
Nesmachnow S, Iturriaga S (2013) Multiobjective grid scheduling using a domain decomposition based parallel micro evolutionary algorithm. Int J Grid Util Comput 6 4(1):70–84CrossRef
139.
Sasaki D, Keane A, Shahpar S (2006) Multiobjective evolutionary optimization of a compressor stage using a grid-enabled environment. In: 44th AIAA aerospace sciences meeting and exhibit, p 340
140.
Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731CrossRef
141.
Zhang Q, Liu W, Li H (2009) The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances. In: 2009 IEEE congress on evolutionary computation. IEEE, pp 203–208
142.
Mendes R, Mohais AS (2005) DynDE: a differential evolution for dynamic optimization problems. In: IEEE congress on evolutionary computation, vol 3. IEEE, pp 2808–2815
143.
Du W, Li B (2005) Multi-strategy ensemble particle swarm optimization for dynamic optimization. Inf Sci 178(15):3096–3109 (2008)MATHCrossRef
144.
Mostaghim S (2010) Parallel multi-objective optimization using self-organized heterogeneous resources. In: Parallel and distributed computational intelligence. Springer, Berlin, Heidelberg, pp 165–179
145.
Imade H, Morishita R, Ono I, Ono N, Okamoto M (2004) A grid-oriented genetic algorithm framework for bioinformatics. N Gener Comput 22(2):177–186MATHCrossRef
146.
Nebro AJ, Luque G, Luna F, Alba E (2008) DNA fragment assembly using a grid-based genetic algorithm. Comput Oper Res 35(9):2776–2790MATHCrossRef
147.
Luna F, Nebro AJ, Alba E, Durillo JJ (2008) Solving large-scale real-world telecommunication problems using a grid-based genetic algorithm. Eng Optim 40(11):1067–1084CrossRef
148.
Melab N, Mezmaz M, Talbi EG (2006) Parallel cooperative meta-heuristics on the computational grid: a case study: the bi-objective ow-shop problem. Parallel Comput 32(9):643–659MathSciNetCrossRef
149.
Talbi EG, Cahon S, Melab N (2007) Designing cellular networks using a parallel hybrid metaheuristic on the computational grid. Comput Commun 30(4):698–713CrossRef
150.
Douguet D, Thoreau E, Grassy G (2000) A genetic algorithm for the automated generation of small organic molecules: drug design using an evolutionary algorithm. J Comput Aided Mol Des 14(5):449–466CrossRef
151.
Luque G, Alba E, Dorronsoro B (2009) An asynchronous parallel implementation of a cellular genetic algorithm for combinatorial optimization. In: Proceedings of the 11th annual conference on genetic and evolutionary computation, pp 1395–1402
152.
Onga DLYS, Sendhob YJB, Leea BS (2006) Ecient hierarchical parallel genetic algorithms using grid computing
153.
Sait SM, Ali MI, Zaidi AM (2007) Evaluating parallel simulated evolution strategies for vlsi cell placement. J Math Model Algor 6(3):433–454MathSciNetMATHCrossRef
154.
Zhao JF, Zeng WH, Li GM, Liu M (2012) Simple parallel genetic algorithm using cloud computing. In: Applied mechanics and materials, vol 121. Trans Tech Publications Ltd, , pp 4151–4155
155.
Guzek M, Bouvry P, Talbi EG (2015) A survey of evolutionary computation for resource management of processing in cloud computing. IEEE Comput Intell Mag 10(2):53–67CrossRef
156.
Devi R, Barlaskar E, Devi O, Medhi S, Shimray R (2014) Survey on evolutionary computation tech techniques and its application in different fields. Int J Inf Theory (IJIT) 3(3):73–82
157.
Malmir H, Farokhi F, Sabbaghi-Nadooshan R (2014) Ecient data mining with evolutionary algorithms for cloud computing application. Int J Smart Electr Eng 3(1):47–53
158.
Yar MH, Rahmati V, Oskouei HRD (2016) A survey on evolutionary computation: methods and their applications in engineering. Mod Appl Sci 10(11):131CrossRef
159.
Liu L, Gu S, Fu D, Zhang M, Buyya R (2018) A new multi-objective evolutionary algorithm for inter-cloud service composition. TIIS 12(1):1–20CrossRef
160.
Zheng L, Lu Y, Ding M, Shen Y, Guoz M, Guo S (2011) Architecture-based performance evaluation of genetic algorithms on multi/many-core systems. In: 2011 IEEE 14th international conference on computational science and engineering (CSE). IEEE, pp 321–334
161.
CRISTEA V (2004) Conception and design of parallel and distributed applications. Proc Roman Acad Ser A 5(1):1–8
162.
Zhuang W, Hanyang F, Zhaoxuan S, Rajesh D (2000, May) HPC application in DSM/VDSM IC chip planning. In: Proceedings. The fourth international conference/exhibition on high performance computing in the Asia-Pacic region, 2000, vol 2. IEEE, pp 1125–1131
163.
Dunlop D, Varrette S, Bouvry P (2008) On the use of a genetic algorithm in high performance computer benchmark tuning. In: International symposium on performance evaluation of computer and telecommunication systems, 2008. SPECTS 2008. IEEE, pp 105–113
164.
Cardenas M, Melin P, Cruz L (2010) Parallel genetic algorithms for architecture optimization of neural networks for pattern recognition. In: Soft computing for recognition based on biometrics. Springer, Berlin, Heidelberg, pp 303–315
165.
Byun JH, Datta K, Ravindran A, Mukherjee A, Joshi B (2009) Performance analysis of coarse-grained parallel genetic algorithms on the multi-core sun Ultra- SPARC T1. In: Southeastcon, 2009. SOUTHEASTCON’09. IEEE. IEEE, pp 301–306
166.
He H, Skora O, Salagean A, Mkinen E (2007) Parallelisation of genetic algorithms for the 2-page crossing number problem. J Parallel Distrib Comput 67(2):229–241CrossRef
167.
Tsutsui S (2009) Parallelization of an evolutionary algorithm on a platform with multi-core processors. In: International conference on articial evolution (evolution articielle). Springer, Berlin, Heidelberg, pp 61–73
168.
Kan G, Lei T, Liang K, Li J, Ding L, He X, Amo-Boateng M (2017) A multi-core CPU and many-core GPU based fast parallel shued complex evolution global optimization approach. IEEE Trans Parallel Distrib Syst 28(2):332–344
169.
Mouret JB, Doncieux S (2010) Sferes v2: Evolvin’in the multi-core world. In: CEC, pp 1–8
170.
Brown M, Johnston MD (2013) Experiments with a parallel multi-objective evolutionary algorithm for scheduling
171.
Umbarkar AJ, Joshi MS (2013) Review of parallel genetic algorithm based on computing paradigm and diversity in search space. ICTACT J Soft Comput 3(4):615–622CrossRef
172.
Arora R, Tulshyan R, Deb K (2010) Parallelization of binary and real-coded genetic algorithms on GPU using CUDA. In: IEEE congress on evolutionary computation. IEEE, pp 1–8
173.
Maitre O, Krger F, Querry S, Lachiche N, Collet P (2012) EASEA: specication and execution of evolutionary algorithms on GPGPU. Soft Comput 16(2):261–279CrossRef
174.
Pospichal P, Jaros J, Schwarz J (2010) Parallel genetic algorithm on the CUDA architecture. In: European conference on the applications of evolutionary computation. Springer, Berlin, Heidelberg, pp 442–451
175.
Jaros J, Pospichal P (2012) A fair comparison of modern CPUs and GPUs running the genetic algorithm under the knapsack benchmark. In: European conference on the applications of evolutionary computation. Springer, Berlin, Heidelberg, pp 426–435
176.
Zhu WA (2009) study of parallel evolution strategy: pattern search on a GPU computing platform. In: Proceedings of the rst ACM/SIGEVO summit on genetic and evolutionary computation, pp 765–772
177.
Shah R, Narayanan PJ, Kothapalli K (2010) GPU-accelerated genetic algorithms. cvit. iiit. ac. in
178.
Kromer P, Snasel V, Platos J, Abraham A (2011) Many-threaded implementation of di erential evolution for the CUDA platform. In: Proceedings of the 13th annual conference on Genetic and evolutionary computation, pp 1595–1602
179.
Oiso M, Matsumura Y, Yasuda T, Ohkura K (2011) Implementing genetic algorithms to CUDA environments using data parallelization. Tech Gazette 18(4):511–517
Metadaten
Titel
Evolutionary algorithm applications for IoTs dedicated to precise irrigation systems: state of the art
verfasst von
Soumaya Ferhat Taleb
Nour El-Houda Benalia
Rabah Sadoun
Publikationsdatum
12.11.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Evolutionary Intelligence / Ausgabe 2/2023
Print ISSN: 1864-5909
Elektronische ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-021-00676-w

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