Abstract
The wavefront cellular learning automata (WCLA), which is a generalization of asynchronous cellular learning automata equipped with a diffusion capability, is a new recently introduced model for dynamic environments such as social and information networks. The WCLA as a collection of connected learning automata mapped to a structure and uses the waves on this structure to diffuse state changes of the learning automata. The diffusion ability of WCLA opens a new learning paradigm for solving many real problems in online, non-deterministic, dynamic, distributed or decentralized environments such as online social networks, cloud computing, internet of things and Blockchain technologies. In this chapter, we first provide a brief overview on cellular learning automata models and then introduce the WCLA framework as an optimization tool. The mathematical analysis on behavior of WCLA also is provided as well.
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References
Adinehvand K, Sardari D, Hosntalab M, Pouladian M (2017) An efficient multistage segmentation method for accurate hard exudates and lesion detection in digital retinal images. J Intell Fuzzy Syst 33:1639–1649. https://doi.org/10.3233/JIFS-17199
Ahangaran M, Taghizadeh N, Beigy H et al (2017) Associative cellular learning automata and its applications. Appl Soft Comput J 53:1–18. https://doi.org/10.1016/j.asoc.2016.12.006
Aldrees M, Ykhlef M (2014) A seeding cellular learning automata approach for viral marketing in social network. In: Proceedings of the 16th International Conference on Information Integration and Web-based Applications & Services—iiWAS ’14. ACM Press, New York, New York, USA, pp 59–63
Amiri F, Yazdani N, Faili H, Rezvanian A (2013) A novel community detection algorithm for privacy preservation in social networks. In: Abraham A (ed), pp 443–450
Arish S, Javaherian M, Safari H, Amiri A (2016) Extraction of active regions and coronal holes from EUV images using the unsupervised segmentation method in the Bayesian framework. Sol Phys 291:1209–1224. https://doi.org/10.1007/s11207-016-0883-4
Beigy H, Meyb MR (2004) A mathematical framework for cellular learning automata. Adv Complex Syst 07:295–319. https://doi.org/10.1142/S0219525904000202
Beigy H, Meybodi MR (2007) Open synchronous cellular learning automata. Adv Complex Syst 10:527–556
Beigy H, Meybodi MR (2008) Asynchronous cellular learning automata. Automatica 44:1350–1357
Beigy H, Meybodi MRR (2010) Cellular learning automata with multiple learning automata in each cell and its applications. IEEE Trans Syst Man Cybern Part B 40:54–65. https://doi.org/10.1109/TSMCB.2009.2030786
Daliri Khomami MM, Rezvanian A, Meybodi MR (2014) Irregular cellular automata for multiple diffusion. In: 22th Iranian conference on electrical engineering (ICEE 2014). Tehran, Iran, pp 1–6
Daliri Khomami MM, Rezvanian A, Bagherpour N, Meybodi MR (2017) Irregular cellular automata based diffusion model for influence maximization. In: 2017 5th Iranian joint congress on fuzzy and intelligent systems (CFIS). IEEE, pp 69–74
Daliri Khomami MM, Rezvanian A, Bagherpour N, Meybodi MR (2018) Minimum positive influence dominating set and its application in influence maximization: a learning automata approach. Appl Intell 48:570–593. https://doi.org/10.1007/s10489-017-0987-z
Damerchilu B, Norouzzadeh MS, Meybodi MR (2016) Motion estimation using learning automata. Mach Vis Appl 27:1047–1061. https://doi.org/10.1007/s00138-016-0788-0
Esnaashari M, Meybodi MR (2008) A cellular learning automata based clustering algorithm for wireless sensor networks. Sens Lett 6:723–735
Esnaashari M, Meybodi MRR (2009) Dynamic point coverage in wireless sensor networks: a learning automata approach. In: Advances in computer science and engineering. Springer, pp 758–762
Esnaashari M, Meybodi MRM (2011) A cellular learning automata-based deployment strategy for mobile wireless sensor networks. J Parallel Distrib Comput 71:988–1001
Esnaashari M, Meybodi MR (2013) Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach. Wirel Networks 19:945–968
Esnaashari M, Meybodi MR (2018) Dynamic irregular cellular learning automata. J Comput Sci 24:358–370. https://doi.org/10.1016/j.jocs.2017.08.012
Ghavipour M, Meybodi MR (2016) An adaptive fuzzy recommender system based on learning automata. Electron Commer Res Appl 20:105–115. https://doi.org/10.1016/j.elerap.2016.10.002
Ghavipour M, Meybodi MR (2017) Irregular cellular learning automata-based algorithm for sampling social networks. Eng Appl Artif Intell 59:244–259. https://doi.org/10.1016/j.engappai.2017.01.004
Hadavi N, Nordin MJ, Shojaeipour A (2014) Lung cancer diagnosis using CT-scan images based on cellular learning automata. In: 2014 international conference on computer and information sciences (ICCOINS). IEEE, pp 1–5
Hasanzadeh M, Meybodi MR (2014) Grid resource discovery based on distributed learning automata. Computing 96:909–922. https://doi.org/10.1007/s00607-013-0337-x
Hasanzadeh M, Meybodi MR (2015) Distributed optimization grid resource discovery. J Supercomput 71:87–120. https://doi.org/10.1007/s11227-014-1289-4
Hasanzadeh Mofrad M, Sadeghi S, Rezvanian A, Meybodi MR (2015) Cellular edge detection: combining cellular automata and cellular learning automata. AEU Int J Electron Commun 69:1282–1290. https://doi.org/10.1016/j.aeue.2015.05.010
Hasanzadeh Mofrad M, Jalilian O, Rezvanian A, Meybodi MR (2016) Service level agreement based adaptive grid superscheduling. Futur Gener Comput Syst 55:62–73. https://doi.org/10.1016/j.future.2015.08.012
Hasanzadeh M, Meybodi MR, Ebadzadeh MM (2013) Adaptive cooperative particle swarm optimizer. Appl Intell 39:397–420. https://doi.org/10.1007/s10489-012-0420-6
Hasanzadeh-Mofrad M, Rezvanian A (2018) Learning automata clustering. J Comput Sci 24:379–388. https://doi.org/10.1016/j.jocs.2017.09.008
Hosein A, Navid F (2003) Cellular learning automata and its applications. J Sci Technol Univ Sharif 54–77
Jalali ZS, Rezvanian A, Meybodi MR (2016a) A two-phase sampling algorithm for social networks. In: Conference proceedings of 2015 2nd international conference on knowledge-based engineering and innovation, KBEI 2015. IEEE, pp 1165–1169
Jalali ZS, Rezvanian A, Meybodi MR (2016b) Social network sampling using spanning trees. Int J Mod Phys C 27:1650052. https://doi.org/10.1142/S0129183116500522
Khani M, Ahmadi A, Hajary H (2017) Distributed task allocation in multi-agent environments using cellular learning automata. Soft Comput. https://doi.org/10.1007/s00500-017-2839-5
Kheradmand S, Meybodi MR (2014) Price and QoS competition in cloud market by using cellular learning automata. In: 2014 4th international conference on computer and knowledge engineering (ICCKE). IEEE, pp 340–345
Khomami MMD, Bagherpour N, Sajedi H, Meybodi MR (2016a) A new distributed learning automata based algorithm for maximum independent set problem. 2016 artificial intelligence and robotics (IRANOPEN). IEEE, Qazvin, Iran, Iran, pp 12–17
Khomami MMD, Rezvanian A, Meybodi MR (2016b) Distributed learning automata-based algorithm for community detection in complex networks. Int J Mod Phys B 30:1650042. https://doi.org/10.1142/S0217979216500429
Khomami MMD, Rezvanian A, Meybodi MR (2018) A new cellular learning automata-based algorithm for community detection in complex social networks. J Comput Sci 24:413–426. https://doi.org/10.1016/j.jocs.2017.10.009
Krishna PV, Misra S, Joshi D et al (2014) Secure socket layer certificate verification: a learning automata approach. Secur Commun Networks 7:1712–1718. https://doi.org/10.1002/sec.867
Kumar N, Lee J-HH (2015) Collaborative-learning-automata-based channel assignment with topology preservation for wireless mesh networks under QoS constraints. IEEE Syst J 9:675–685. https://doi.org/10.1109/JSYST.2014.2355113
Kumar N, Misra S, Obaidat MSMSMSMS (2015a) Collaborative learning automata-based routing for rescue operations in dense urban regions using vehicular sensor networks. IEEE Syst J 9:1081–1090. https://doi.org/10.1109/JSYST.2014.2335451
Kumar NN, Lee JHJ-H, Rodrigues JJJPC (2015b) Intelligent mobile video surveillance system as a Bayesian coalition game in vehicular sensor networks: learning automata approach. IEEE Trans Intell Transp Syst 16:1148–1161. https://doi.org/10.1109/TITS.2014.2354372
Liu S-C, Zhu F-X, Gan L (2016) A label-propagation-probability-based algorithm for overlapping community detection. Jisuanji Xuebao/Chinese Journal of Computers. 2016:717–729
Mahdaviani M, Kordestani JK, Rezvanian A, Meybodi MR (2015) LADE: learning automata based differential evolution. Int J Artif Intell Tools 24:1550023. https://doi.org/10.1142/s0218213015500232
Mason LG, Gu X (1986) Learning automata models for adaptive flow control in packet-switching networks. Adapt Learn Syst. Springer, US, Boston, MA, pp 213–227
Misra S, Interior B, Kumar N et al (2014) Networks of learning automata for the vehicular environment: a performance analysis study. IEEE Wirel Commun 21:41–47. https://doi.org/10.1109/MWC.2014.7000970
Misra S, Chatterjee SS, Guizani M (2015) Stochastic learning automata-based channel selection in cognitive radio/dynamic spectrum access for WiMAX networks. Int J Commun Syst 28:801–817
Moradabadi B, Beigy H (2014) A new real-coded Bayesian optimization algorithm based on a team of learning automata for continuous optimization. Genet Program Evolvable Mach 15:169–193. https://doi.org/10.1007/s10710-013-9206-9
Moradabadi B, Meybodi MR (2016) Link prediction based on temporal similarity metrics using continuous action set learning automata. Phys A Stat Mech Appl 460:361–373. https://doi.org/10.1016/j.physa.2016.03.102
Moradabadi B, Meybodi MR (2017) A novel time series link prediction method: Learning automata approach. Phys A Stat Mech Appl 482:422–432. https://doi.org/10.1016/j.physa.2017.04.019
Moradabadi B, Meybodi MR (2018) Wavefront cellular learning automata. Chaos 28:21101. https://doi.org/10.1063/1.5017852
Moradabadi B, Ebadzadeh MM, Meybodi MR (2016) A new real-coded stochastic Bayesian optimization algorithm for continuous global optimization. Genet Program Evolvable Mach 17:145–167. https://doi.org/10.1007/s10710-015-9255-3
Morshedlou H, Meybodi MR (2014) Decreasing impact of SLA violations: a proactive resource allocation approach for cloud computing environments. IEEE Trans Cloud Comput 2:156–167. https://doi.org/10.1109/TCC.2014.2305151
Morshedlou H, Meybodi MR (2017) A new local rule for convergence of ICLA to a compatible point. IEEE Trans Syst Man Cybern Syst 47:3233–3244. https://doi.org/10.1109/TSMC.2016.2569464
Mostafaei H, Obaidat MS (2018) Learning automaton-based self-protection algorithm for wireless sensor networks. IET Networks 7:353–361. https://doi.org/10.1049/iet-net.2018.0005
Mousavian A, Rezvanian A, Meybodi MR (2013) Solving minimum vertex cover problem using learning automata. In: 13th Iranian conference on fuzzy systems (IFSC 2013), pp 1–5
Mousavian A, Rezvanian A, Meybodi MR (2014) Cellular learning automata based algorithm for solving minimum vertex cover problem. In: 2014 22nd Iranian conference on electrical engineering (ICEE). IEEE, pp 996–1000
Mozafari M, Shiri ME, Beigy H (2015) A cooperative learning method based on cellular learning automata and its application in optimization problems. J Comput Sci 11:279–288. https://doi.org/10.1016/j.jocs.2015.08.002
Narendra KS, Thathachar MAL (1989) Learning automata: an introduction. Prentice-Hall
Nicopolitidis P (2015) Performance fairness across multiple applications in wireless push systems. Int J Commun Syst 28:161–166. https://doi.org/10.1002/dac.2648
Packard NH, Wolfram S (1985) Two-dimensional cellular automata. J Stat Phys 38:901–946. https://doi.org/10.1007/BF01010423
Rezapoor Mirsaleh M, Meybodi MR (2015) A learning automata-based memetic algorithm. Genet Program Evolvable Mach 16:399–453. https://doi.org/10.1007/s10710-015-9241-9
Rezapoor Mirsaleh M, Meybodi MR (2016) A new memetic algorithm based on cellular learning automata for solving the vertex coloring problem. Memetic Comput 8:211–222. https://doi.org/10.1007/s12293-016-0183-4
Rezapoor Mirsaleh M, Reza Meybodi M (2016) A Michigan memetic algorithm for solving the community detection problem in complex network. Neurocomputing 214:535–545. https://doi.org/10.1016/j.neucom.2016.06.030
Rezvanian A, Meybodi MR (2010a) An adaptive mutation operator for artificial immune network using learning automata in dynamic environments. In: 2010 second world congress on nature and biologically inspired computing (NaBIC). IEEE, pp 479–483
Rezvanian A, Meybodi MR (2010b) LACAIS: learning automata based cooperative artificial immune system for function optimization. Communications in computer and information science. Springer, Berlin Heidelberg, pp 64–75
Rezvanian A, Meybodi MR (2015a) Finding maximum clique in stochastic graphs using distributed learning automata. Int J Uncertainty Fuzziness Knowl Based Syst 23:1–31. https://doi.org/10.1142/S0218488515500014
Rezvanian A, Meybodi MR (2015b) Finding minimum vertex covering in stochastic graphs: a learning automata approach. Cybern Syst 46:698–727. https://doi.org/10.1080/01969722.2015.1082407
Rezvanian A, Meybodi MR (2015c) Sampling social networks using shortest paths. Phys A Stat Mech Appl 424:254–268. https://doi.org/10.1016/j.physa.2015.01.030
Rezvanian A, Meybodi MR (2016a) Stochastic social networks: measures and algorithms. LAP LAMBERT Academic Publishing
Rezvanian A, Meybodi MR (2016b) Stochastic graph as a model for social networks. Comput Human Behav 64:621–640. https://doi.org/10.1016/j.chb.2016.07.032
Rezvanian A, Meybodi MR (2016c) Sampling algorithms for weighted networks. Soc Netw Anal Min 6:60. https://doi.org/10.1007/s13278-016-0371-8
Rezvanian A, Meybodi MR (2017) Sampling algorithms for stochastic graphs: A learning automata approach. Knowl Based Syst 127:126–144. https://doi.org/10.1016/j.knosys.2017.04.012
Rezvanian A, Rahmati M, Meybodi MR (2014) Sampling from complex networks using distributed learning automata. Phys A Stat Mech its Appl 396:224–234. https://doi.org/10.1016/j.physa.2013.11.015
Rezvanian A, Saghiri AM, Vahidipour SM, et al (2018a) Recent advances in learning automata. Springer
Rezvanian A, Vahidipour SM, Esnaashari M (2018b) New applications of learning automata-based techniques in real-world environments. J Comput Sci 24:287–289. https://doi.org/10.1016/j.jocs.2017.11.012
Safavi SM, Meybodi MR, Esnaashari M (2014) Learning automata based face-aware mobicast. Wirel Pers Commun 77:1923–1933
Saghiri AM, Meybodi MR (2016) An approach for designing cognitive engines in cognitive peer-to-peer networks. J Netw Comput Appl 70:17–40. https://doi.org/10.1016/j.jnca.2016.05.012
Saghiri AM, Meybodi MR (2017a) A closed asynchronous dynamic model of cellular learning automata and its application to peer-to-peer networks. Genet Program Evolvable Mach 18:313–349. https://doi.org/10.1007/s10710-017-9299-7
Saghiri AM, Meybodi MR (2017b) A distributed adaptive landmark clustering algorithm based on mOverlay and learning automata for topology mismatch problem in unstructured peer-to-peer networks. Int J Commun Syst 30:e2977. https://doi.org/10.1002/dac.2977
Saghiri AM, Meybodi MR (2018) An adaptive super-peer selection algorithm considering peers capacity utilizing asynchronous dynamic cellular learning automata. Appl Intell 48:271–299. https://doi.org/10.1007/s10489-017-0946-8
Santoso J, Riyanto B, Adiprawita W (2016) Dynamic path planning for mobile robots with cellular learning automata. J ICT Res Appl 10:1–14. https://doi.org/10.5614/itbj.ict.res.appl.2016.10.1.1
Sohrabi MK, Roshani R (2017) Frequent itemset mining using cellular learning automata. Comput Human Behav 68:244–253. https://doi.org/10.1016/j.chb.2016.11.036
Soleimani-Pouri M, Rezvanian A, Meybodi MR (2012) Solving maximum clique problem in stochastic graphs using learning automata. In: 2012 fourth international conference on computational aspects of social networks (CASoN). IEEE, pp 115–119
Thathachar MAL, Sastry PS (2003) Networks of learning automata: techniques for online stochastic optimization. Springer, Boston, MA
Toozandehjani H, Zare-Mirakabad M-R, Derhami V (2014) Improvement of recommendation systems based on cellular learning automata. In: 2014 4th international conference on computer and knowledge engineering (ICCKE). IEEE, pp 592–597
Vafaee Sharbaf F, Mosafer S, Moattar MH (2016) A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization. Genomics 107:231–238. https://doi.org/10.1016/j.ygeno.2016.05.001
Vafashoar R, Meybodi MR (2016) Multi swarm bare bones particle swarm optimization with distribution adaption. Appl Soft Comput J 47:534–552. https://doi.org/10.1016/j.asoc.2016.06.028
Vafashoar R, Meybodi MR (2018) Multi swarm optimization algorithm with adaptive connectivity degree. Appl Intell 48:909–941. https://doi.org/10.1007/s10489-017-1039-4
Vahidipour SM, Meybodi MR, Esnaashari M (2017) Adaptive Petri net based on irregular cellular learning automata with an application to vertex coloring problem. Appl Intell 46:272–284. https://doi.org/10.1007/s10489-016-0831-x
Zhang F, Wang X, Li P, Zhang L (2016) An energy aware cellular learning automata based routing algorithm for opportunistic networks. Int J Grid Distrib Comput 9:255–272. https://doi.org/10.14257/ijgdc.2016.9.2.22
Zhao Y, Jiang W, Li S et al (2015) A cellular learning automata based algorithm for detecting community structure in complex networks. Neurocomputing 151:1216–1226. https://doi.org/10.1016/j.neucom.2014.04.087
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Rezvanian, A., Moradabadi, B., Ghavipour, M., Daliri Khomami, M.M., Meybodi, M.R. (2019). Wavefront Cellular Learning Automata: A New Learning Paradigm. In: Learning Automata Approach for Social Networks. Studies in Computational Intelligence, vol 820. Springer, Cham. https://doi.org/10.1007/978-3-030-10767-3_2
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