A significant number of search and optimisation techniques whose principles seek inspiration from nature and biology phenomena have been proposed in the last decades. These methods have been successfully applied to solve a wide range of engineering problems. This is also the case of greenhouse environment control, which has been incorporating this type of techniques into its design. This paper addresses evolutionary and bio-inspired methods in the context of greenhouse environment control. Algorithm principles for reference techniques are reviewed, namely: simulated annealing, genetic algorithm, differential evolution and particle swarm optimisation. The last three techniques are considered using single and multiple objective formulations. A review of these algorithms within greenhouse environment control applications is presented, considering single and multiple objective problems, as well as their current trends.
AbbassH (2001) MBO: Marriage in honey bees optimization – a haplometrosis polygynous swarming approach. In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), Seoul, South Korea, 27–30 May 2001, Vol. 1, pp. 207–214. IEEE.
2.
AbdelhafidHAhmedHAbdelkrimSBelkacemD (2016) Cuckoo search optimization for reduction of a greenhouse climate model. International Journal of Advanced Computer Science and Applications7(7): 624–629.
3.
AliFXiujuanLXiaoX (2008) The aircraft departure scheduling based on particle swarm optimization combined with simulated annealing algorithm. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), Hong Kong, China, 1–6 June 2008, pp. 1393–1398. IEEE.
4.
AngelineP (1998) Using selection to improve particle swarm optimization. In: 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), Anchorage, AK, USA, 4–9 May 1998, pp. 84–89. IEEE.
5.
Avila-MirandaRBegovichORuiz-LeonJ (2013) An optimal and intelligent control strategy to ventilate a greenhouse. In: 2013 IEEE Congress on Evolutionary Computation, Cancun, Mexico, 20–23 June 2013, pp. 779–782. IEEE.
BabuBVJehanMML (2003) Differential evolution for multi-objective optimization. In: The 2003 Congress on Evolutionary Computation, 2003. CEC ’03, Canberra, ACT, Australia, 8–12 December 2003, Vol. 4, pp. 2696–2703.
8.
BalujaS (1994) Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning. Technical report, Technical Report CMU-CS-94-163, Carnegie Mellon Univ.
9.
BechikhSElarbiMBen SaidL (2017) Many-objective Optimization Using Evolutionary Algorithms: A Survey. Cham: Springer International Publishing.
10.
BelhaniAM’sirdiNK (2011) An observer based backstepping control for a greenhouse. In: 2011 International Conference on Communications, Computing and Control Applications (CCCA), Hammamet, Tunisia, 3–5 March 2011, pp. 1–7. IEEE.
11.
BlascoXMartínezMHerreroJ, et al. (2007) Model-based predictive control of greenhouse climate for reducing energy and water consumption. Computers and Electronics in Agriculture55(1): 49–70.
12.
BlascoXMartnezMSenentJSanchisJ (2001) Model based predictive control using genetic algorithms. application to greenhouses climate control. In: MiraJPrietoA (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. Berlin Heidelberg: Springer, pp. 457–465.
13.
CaponettoRFortunaLNunnariG, et al. (2000) Soft computing for greenhouse climate control. IEEE Transactions on Fuzzy Systems8(6): 753–760.
14.
ChatterjeeASiarryP (2006) Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Computers & Operations Research33(3): 859–871.
15.
ChenJYangJZhaoJ, et al. (2016) Energy demand forecasting of the greenhouses using nonlinear models based on model optimized prediction method. Neurocomputing174(B): 1087–1100.
16.
ChenJZhaoJXuF, et al. (2015) Modeling of energy demand in the greenhouse using PSO-GA hybrid algorithms. Mathematical Problems in Engineering2015: 1–6. DOI: 10.1155/2015/871075.
17.
ChiangTCChenCNLinYC (2013) Parameter control mechanisms in differential evolution: A tutorial review and taxonomy. In: 2013 IEEE Symposium on Differential Evolution (SDE), Singapore, 16–19 April 2013, pp. 1–8. IEEE.
18.
ClercMKennedyJ (2002) The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation6(1): 58–73.
19.
CoelhoJde Moura OliveiraPCunhaJB (2002) Greenhouse air temperature control using the particle swarm optimisation algorithm. IFAC Proceedings Volumes35(1): 43–47.
20.
CoelhoJde Moura OliveiraPCunhaJB (2005) Greenhouse air temperature predictive control using the particle swarm optimisation algorithm. Computers and Electronics in Agriculture49(3): 330–344.
21.
CoelloCAC (1999) An updated survey of evolutionary multiobjective optimization techniques: state of the art and future trends. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), Washington, DC, USA, 6–9 July 1999, Vol. 1, pp. 3–13.
22.
CoelloCACLechugaMS (2002) Mopso: A proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation, 2002. CEC ’02, Honolulu, HI, USA, 12–17 May 2002, Vol. 2, pp. 1051–1056.
23.
Coello CoelloCA (1999) A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowledge and Information Systems1(3): 269–308.
24.
CorneDWKnowlesJDOatesMJ (2000) The pareto envelope-based selection algorithm for multiobjective optimization. In: SchoenauerMDebKRudolphGetal. (eds) Parallel Problem Solving from Nature PPSN VI. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 839–848.
CruzILvan WilligenburgLvan StratenG (2003) Optimal control of nitrate in lettuce by a hybrid approach: differential evolution and adjustable control weight gradient algorithms. Computers and Electronics in Agriculture40(1–3): 179–197.
27.
DaiCYaoMXieZ, et al. (2009) Parameter optimization for growth model of greenhouse crop using genetic algorithms. Applied Soft Computing9(1): 13–19.
28.
DaoSDAbharyKMarianR (2017) A bibliometric analysis of genetic algorithms throughout the history. Computers & Industrial Engineering110: 395–403.
29.
DasSSuganthanPN (2011) Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation15(1): 4–31.
30.
de Moura OliveiraPB (2003) Continuous function optimisation using a hybrid split particle swarm algorithm. In: International Conference on Intelligent Control Systems and Signal Processing (ICONS’2003), Faro, Portugal, 8–11 April, pp. 90–94.
31.
de Moura OliveiraPBSolteiro PiresEJNovaisP (2016) Revisiting the simulated annealing algorithm from a teaching perspective. In: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16, San Sebastián, Spain, 19–21 October, pp. 718–727. Springer International Publishing.
32.
DebK (2001) Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, LTD.
33.
DebKPratapAAgarwalSMeyarivanT (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation6(2): 182–197.
34.
DorigoMBirattariMStutzleT (2006) Ant colony optimization. IEEE Computational Intelligence Magazine1(4): 28–39.
35.
DragoiENDafinescuV (2015) Parameter control and hybridization techniques in differential evolution: a survey. Artificial Intelligence Review45(4): 447–470.
36.
FerentinosKP (2005) Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms. Neural Networks18(7): 934–950.
37.
FerentinosKPArvanitisKGTantauHGSigrimisN (2006) Special aspects of IT for greenhouse cultivation. In: MunackAJosephS (eds) CIGR Handbook of Agricultural Eng., Vol. VI Information Technology, Michigan, USA: ASABE, pp. 294–312.
38.
FerreiraPRuanoA (2005) Soft-computing methods in greenhouse environmental and crop modelling. In: Proceedings of the EFITA/WCCA2005 Conference, the 5th Conference of the European Federation for Information Technology in Agriculture, Food and Environment, and the 3th World Congress on Computers in Agriculture and Natural Resources, Vila Real, Portugal, 25–28 July 2005, pp. 280–287.
39.
FerreiraPMRuanoAE (2008) Application of computational intelligence methods to greenhouse environmental modelling. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong, China, 1–8 June 2008, pp. 3582–3589. IEEE.
40.
FerreiraPRuanoAFonsecaC (2003) Genetic assisted selection of RBF model structures for greenhouse inside air temperature prediction. In: Proceedings of 2003 IEEE Conference on Control Applications, 2003. CCA 2003, Istanbul, Turkey, 25–25 June 2003, Vol. 1, pp. 576–581. IEEE.
41.
FerreiraPRuanoAFonsecaC (2005) Evolutionary multiobjective design of radial basis function networks for greenhouse environmental control. IFAC Proceedings Volumes38(1): 63–68.
42.
FogelLJOwensAJWalshMJ (1966) Artificial intelligence through simulated evolution. In: Evolutionary Computation. New York: John Wiley & Sons.
GaoYXuLWeiR (2015) Real-time multi-objective optimal control algorithm for greenhouse environment using particle swarm optimization. In: 2015 8th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, 12–13 December 2015, pp. 11–15. IEEE.
45.
GhoreishiSNSorensenJCJorgensenBN (2015) Comparative study of evolutionary multi-objective optimization algorithms for a non-linear greenhouse climate control problem. In: 2015 IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan, 25–28 May 2015, pp. 1909–1917. IEEE.
46.
GoggosVKingR (2000) Qualitative-evolutionary design of greenhouse environment control agents. Computers and Electronics in Agriculture26(3): 271–282.
47.
GoldbergDE (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Boston, MA: Addison-Wesley Professional.
48.
GurbanEHAndreescuGD (2014) Comparison of modified smith predictor and PID controller tuned by genetic algorithms for greenhouse climate control. In: 2014 IEEE 9th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE.
49.
Guzmán-CruzRCastañeda-MirandaRGarcía-EscalanteJ, et al. (2009) Calibration of a greenhouse climate model using evolutionary algorithms. Biosystems Engineering104(1): 135–142.
50.
Guzmán-CruzROlvera-GonzálezELópez-CruzILMontoya-ZamoraR (2013) Modelling greenhouse air temperature using evolutionary algorithms in auto regressive models. African Journal of Agricultural Research8(3): 251–261.
51.
HasniATaibiRDraouiBBoulardT (2011) Optimization of greenhouse climate model parameters using particle swarm optimization and genetic algorithms. Energy Procedia6: 371–380.
52.
HerreroJBlascoXMartínezM, et al. (2007) Non-linear robust identification of a greenhouse model using multi-objective evolutionary algorithms. Biosystems Engineering98(3): 335–346.
53.
HollandJH (1975) Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.
54.
HuHXuLGoodmanED (2010) A control optimization algorithm for greenhouse climate control problems. In: Proceedings of the 12th Annual Conference Comp on Genetic and Evolutionary Computation - GECCO’10, July 2010, pp. 2081–2082. New York, NY: ACM Press.
55.
HuHXuLGoodmanEDZengS (2014) NSGA-II-based nonlinear PID controller tuning of greenhouse climate for reducing costs and improving performances. Neural Computing and Applications24(3-4): 927–936.
56.
HuHXuLWeiRZhuB (2011a) Multi-objective control optimization for greenhouse environment using evolutionary algorithms. Sensors11(12): 5792–5807.
57.
HuHXuLZhuBWeiR (2011b) A compatible control algorithm for greenhouse environment control based on MOCC strategy. Sensors11(12): 3281–3302.
JordehiARJasniJ (2013) Parameter selection in particle swarm optimisation: A survey. Journal of Experimental & Theoretical Artificial Intelligence25(4): 527–542.
60.
KennedyJ (2003) Bare bones particle swarms. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS’03 (Cat. No.03EX706), Indianapolis, IN, USA, 26–26 April 2003, pp. 80–87. IEEE.
61.
KennedyJEberhartR (1995) Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks, Perth, WA, Australia, 27 November–1 December 1995, Vol. 4, pp. 1942–1948. IEEE.
62.
KirkpatrickSGelattCDVecchiMP (1983) Optimization by simulated annealing. Science, 220(4598): 671–680.
63.
KnowlesJCorneD (2004) Bounded Pareto archiving: Theory and practice. In: GandibleuxXSevauxMSörensenKT’kindtV (eds) Metaheuristics for Multiobjective Optimisation, Lecture Notes in Economics and Mathematical Systems, Springer, Berlin, Heidelberg, Vol. 535. Berlin, Heidelberg: Springer, pp. 39–64.
64.
KnowlesJDCorneDW (2000) Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation8(2): 149–172.
65.
KozaJR (1990) Genetic programming: a paradigm for breeding populations of computers pro-grams to solve problems. Technical report, Stanford Univ. Technical report STAN-CS-90-1314.
66.
KrinkTRasmusKUBogdanF (2001) Evolutionary algorithms in control optimization: the greenhouse problem. In: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation. Morgan Kaufmann Publishers Inc, San Francisco, CA, 7–11 July, pp. 440–447.
67.
KrohlingRAMendelE (2009) Bare bones particle swarm optimization with gaussian or cauchy jumps. In: 2009 IEEE Congress on Evolutionary Computation. IEEE.
68.
KukkonenSLampinenJ (2004) An extension of generalized differential evolution for multi-objective optimization with constraints. In: YaoXBurkeEKLozanoJA, et al (eds) Parallel Problem Solving from Nature - PPSN VIII. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 752–761.
69.
LammariKBounaamaFDraouiB, et al. (2012) GA optimization of the coupled climate model of an order two of a greenhouse. Energy Procedia18: 416–425.
70.
LampinenJ (2001) DE’s selection rule for multiobjective optimization. Technical report, Department of Information Technology, Lappeenranta University of Technology.
71.
LaumannsMThieleLDebKZitzlerE (2002a) Archiving with guaranteed convergence and diversity in multi-objective optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 439–447. New York City, NY: Morgan Kaufmann Publishers.
72.
LaumannsMThieleLDebKZitzlerE (2002b) Combining convergence and diversity in evolutionary multiobjective optimization. Evolutionary Computation10(3): 263–282.
73.
LeeCYLeeD (2013) Determination of initial temperature in fast simulated annealing. Computational Optimization and Applications58(2): 503–522.
MahdavianMWattanapongsakornN (2017) Optimizing greenhouse lighting for advanced agriculture based on real time electricity market price. Mathematical Problems in Engineering2017: 1–11. DOI: 10.1155/2017/6862038.
76.
MahdavianMSudengSWattanapongsakornN (2016) Multi-objective optimization and decision making for greenhouse climate control system. In: 2016 International Conference on Information Science and Security (ICISS), Pattaya, pp. 1–5. IEEE.
77.
MahdavianMSudengSWattanapongsakornN (2017) Multi-objective optimization and decision making for greenhouse climate control system considering user preference and data clustering. Cluster Computing20(1): 835–853.
78.
ManonmaniAThyagarajanTElangoMSuthaS (2018) Modelling and control of greenhouse system using neural networks. Transactions of the Institute of Measurement and Control40(3): 918–929.
79.
ManonmaniAThyagarajanTSuthaSGayathriV (2016) Design of soft computing based optimal PI controller for greenhouse system. Circuits and Systems7(11): 3431–3447.
80.
MárquezALBañosRGilC, et al. (2011) Multi-objective crop planning using pareto-based evolutionary algorithms. Agricultural Economics42(6): 649–656.
81.
MemmahMMLescourretFYaoXLavigneC (2015) Metaheuristics for agricultural land use optimization. A review. Agronomy for Sustainable Development35(3): 975–998.
82.
MendesJMOliveiraPMdos SantosFNMorais dos SantosR (2019) Nature inspired metaheuristics and their applications in agriculture: A short review. In: Moura OliveiraPNovaisPReisLP (eds) Progress in Artificial Intelligence. Cham: Springer International Publishing, pp. 167–179.
83.
MetropolisNRosenbluthAWRosenbluthMNTellerAHTellerE (1953) Equation of state calculations by fast computing machines. Technical report.
84.
Mezura-MontesEReyes-SierraMCoelloCAC (2008) Multi-objective Optimization Using Differential Evolution: A Survey of the State-of-the-Art. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 173–196.
85.
MichalewiczZ (1994) Genetic Algorithms + Data Structures = Evolution Programs. Berlin, Heidelberg: Springer Berlin Heidelberg.
86.
MirjaliliS (2015) The ant lion optimizer. Advances in Engineering Software83: 80–98.
87.
MirjaliliSMirjaliliSMLewisA (2014) Grey wolf optimizer. Advances in Engineering Software69: 46–61.
88.
NeriFTirronenV (2009) Recent advances in differential evolution: a survey and experimental analysis. Artificial Intelligence Review33(1–2): 61–106.
89.
OliveiraJBoaventura-CunhaJOliveiraPM (2016) Automation and control in greenhouses: State-of-the-art and future trends. In: GarridoPSoaresFMoreiraA (eds) Lecture Notes in Electrical Engineering, Vol. 402. Springer, Cham: Springer International Publishing, pp. 597–606.
90.
ParkMWKimYD (1998) A systematic procedure for setting parameters in simulated annealing algorithms. Computers & Operations Research25(3): 207–217.
91.
PengHGuoZDengCWuZ (2017) Enhancing differential evolution with random neighbors based strategy. Journal of Computational Science26: 501–511.
92.
Perez-GonzalezABegovichORuiz-LeonJ (2014) Modeling of a greenhouse prototype using PSO algorithm based on a LabView application. In: 2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), Campeche, pp. 1–6. IEEE.
93.
PiotrowskiAP (2017) Review of differential evolution population size. Swarm and Evolutionary Computation32: 1–24.
94.
PlagianakosVPTasoulisDKVrahatisMN (2008) A review of major application areas of differential evolution. In: Advances in Differential Evolution. Springer Berlin Heidelberg, pp. 197–238.
95.
PohlheimHHeißnerA (1997) Optimal control of greenhouse climate using a short time climate model and evolutionary algorithms. IFAC Proceedings Volumes30(26): 113–118.
96.
PohlheimHHeißnerA (1999) Optimal control of greenhouse climate using real-world weather data and evolutionary algorithms. In: BanzhafW (ed.) GECCO’99 – Proceedings of the Genetic and Evolutionary Computation Conference. San Francisco, CA: Morgan Kaufmann, pp. 1672–1677.
97.
PrakashOKumarA (2014) Thermal performance evaluation of modified active greenhouse dryer. Journal of Building Physics37(4): 395–402.
98.
PriceKStornRMLampinenJA (2005) Differential Evolution – A Practical Approach to Global Optimization. 1st edn. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg.
99.
RashediENezamabadi-pourHSaryazdiS (2009) GSA: A gravitational search algorithm. Information Sciences179(13): 2232–2248.
100.
RechenbergIEigenM (1973) Evolutionsstrategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution. Stuttgart: Frommann-Holzboog-Verlag.
101.
ReffyePDHuB (2003) Relevant qualitative and quantitative choices for building an efficient dynamic plant growth model: Greenlab case. In: HuBJaegerM (eds) International Symposium on Plant Growth Modeling, Simulation, Visualization and their Applications – PMA’03. Beijing, China: Springer and Tsinghua University Press, pp. 87–107.
102.
Reyes-sierraMCoelloCAC (2006) Multi-objective particle swarm optimizers: A survey of the state-of-the-art. International Journal of Computational Intelligence Research2(3): 287–308.
103.
SchafferJD (1985) Multiple objective optimization with vector evaluated genetic algorithms. In: GrefensetteGJELawrenceJJE (eds) Proceedings of the 1st International Conference on Genetic Algorithms. Hillsdale, NJ: L. Erlbaum Associates Inc, pp. 93–100.
SenentJSMartinezMABlascoXSanchisJ (1998) MIMO predictive control of temperature and humidity inside a greenhouse using simulated annealing (SA) as optimizer of a multicriteria index. In: Tasks and Methods in Applied Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 271–279.
106.
ShiYEberhartR (1998a) A modified particle swarm optimizer. In: 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), Anchorage, AK, USA, 4–9 May 1998, pp. 69–73. IEEE.
107.
ShiYEberhartRC (1998b) Parameter selection in particle swarm optimization. In: Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 591–600.
108.
SinghalRKumarR (2016) Receding horizon based greenhouse air temperature control using grey wolf optimization algorithm. In: 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON), Varanasi, India, 9–11 December 2016, pp. 32–37. IEEE.
SørensenJCJørgensenBNKleinMDemazeauY (2011) An agent-based extensible climate control system for sustainable greenhouse production. In: Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 218–233.
StornRPriceK (1995) Differential evolution – A simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report, TR-95-012, ICSI.
113.
StornRPriceK (1997) Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization11(4): 341–359.
114.
TingLYuhanJManZ, et al. (2017) Universality of an improved photosynthesis prediction model based on PSO-SVM at all growth stages of tomato. International Journal of Agricultural and Biological Engineering10(2): 63–73.
115.
UmashankariRValarmathiKSaravanakumarG (2013) Temperature control of green house system using evolutionary computation. In: 2013 International Conference on Energy Efficient Technologies for Sustainability, Nagercoil, India, 10–12 April 2013, pp. 814–820.
116.
UrsemRFilipicBKrinkT (2002) Exploring the performance of an evolutionary algorithm for greenhouse control. Journal of Computing and Information Technology10(3): 195–201.
117.
UrsemRK (2003) Models for Evolutionary Algorithms and Their Applications in System Identification and Control Optimization. PhD Thesis, Faculty of Science of the University of Aarhus.
118.
van StratenGvan HentenEJ (2010) Optimal greenhouse cultivation control: Survey and perspectives. IFAC Proceedings Volumes43(26): 18–33.
WuGShenXLiH, et al. (2018) Ensemble of differential evolution variants. Information Sciences423: 172–186.
121.
WuLReffyePDHuBG, et al. (2005) A water supply optimization problem for plant growth based on greenlab model. Revue Africaine de la Recherche en Informatique et Mathmatiques Appliques, INRIA3: 197–207.
122.
YangXS (2009) Firefly algorithm, lévy flights and global optimization. In: Research and Development in Intelligent Systems XXVI. London: Springer London, pp. 209–218.
123.
YangXSDebS (2009) Cuckoo search via lévy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), Coimbatore, India, 9–11 December 2009, pp. 210–214. IEEE.
124.
ZhanZHZhangJLiYChungHH (2009) Adaptive particle swarm optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)39(6): 1362–1381.
125.
ZhangJSandersonAC (2009) Adaptive Differential Evolution – A Robust Approach to Multimodal Problem Optimization. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg.
126.
ZhangYWangSJiG (2015) A comprehensive survey on particle swarm optimization algorithm and its applications. Mathematical Problems in Engineering2015: 1–38.
127.
ZhangZ (2008) Multiobjective optimization immune algorithm in dynamic environments and its application to greenhouse control. Applied Soft Computing8(2): 959–971.
128.
ZhaoBWangK (2010) Study on greenhouse environment neural network model based on PSO algorithm. In: 2010 International Conference on Intelligent Computing and Cognitive Informatics, Kuala Lumpur, Malaysia, 22–23 June 2010, pp. 187–190. IEEE.
129.
ZhaoXLiuHLiuDAiWZuoX (2016) New modified bare-bones particle swarm optimization. In: 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Canada, 24–29 July 2016, pp. 416–422. IEEE.
130.
ZhouWLiKXueW, et al. (2017) Multi-objective optimization of fan-pad system operation for venlo greenhouse using CFD model based data interactive mechanism. In: 2017 36th Chinese Control Conference (CCC), Dalian, China, 26–28 July 2017, pp. 3020–3025. IEEE.
131.
ZiapourBMHashtroudiA (2017) Performance study of an enhanced solar greenhouse combined with the phase change material using genetic algorithm optimization method. Applied Thermal Engineering110: 253–264.
132.
ZitzlerEThieleL (1999) Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation3(4): 257–271.
133.
ZouQJiJZhangSShiMLuoY (2010) Model predictive control based on particle swarm optimization of greenhouse climate for saving energy consumption. In: 2010 World Automation Congress, Kobe, Japan, 19–23 September 2010, pp. 123–128.
134.
ZúñigaECTCruzILLGarcíaAR (2014) Parameter estimation for crop growth model using evolutionary and bio-inspired algorithms. Applied Soft Computing23: 474–482.
