Abstract
Learning automaton (LA) is one of the reinforcement learning techniques in artificial intelligence. Learning automata’s learning ability in unknown environments is a useful technique for modeling, controlling, and solving many real problems in the distributed and decentralized environments. In this chapter, first, we provide an overview of LA concepts and recent variants of LA models. Then, we present a brief description of the recent reinforcement learning mechanisms for solving optimization problems. Finally, the evolution of the recent LA models for optimization is presented.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdolzadeh, M., Rashidi, H.: An approach of cellular learning automata to job shop scheduling problem. Int. J. Simul. Syst. Sci. Technol. 34, 391–401 (2010)
Abedi Firouzjaee, H., Kazemi Kordestani, J., Meybodi, M.R.: Cuckoo search with composite flight operator for numerical optimization problems and its application in tunnelling. Eng. Opt. 49, 597–616 (2017). https://doi.org/10.1080/0305215X.2016.1206535
Abshouri, A.A,. Meybodi, M.R., Bakhtiary, A.: New firefly algorithm based on multi swarm & learning automata in dynamic environments. In: IEEE Proceedings, pp. 989–993 (2011)
Abtahi, F., Meybodi, M.R., Ebadzadeh, M.M., Maani, R.: Learning automata-based co-evolutionary genetic algorithms for function optimization. In: Proceedings of the 6th International Symposium on Intelligent Systems and Informatics, (SISY), pp. 1–5 (2008)
Adinehvand, K., Sardari, D., Hosntalab, M., Pouladian, M.: An efficient multistage segmentation method for accurate hard exudates and lesion detection in digital retinal images. J. Intell. Fuzzy Syst. 33, 1639–1649 (2017). https://doi.org/10.3233/JIFS-17199
Agache, M., Oommen, B.J.: Generalized pursuit learning schemes: new families of continuous and discretized learning automata. IEEE Trans. Syst. Man Cybern. Part B Cybern. 32, 738–749 (2002). https://doi.org/10.1109/TSMCB.2002.1049608
Aghababa, A.B., Fathinavid, A., Salari, A., Zavareh, S.E.H.: A novel approach for malicious nodes detection in ad-hoc networks based on cellular learning automata. In: 2012 World Congress on Information and Communication Technologies, pp. 82–88. IEEE (2012)
Aghazadeh, F., Meybodi, M.R.: Learning bees algorithm for optimization. In: International Conference on Information and Intelligent Computing, pp. 115–122 (2011)
Ahangaran, M., Taghizadeh, N., Beigy, H.: Associative cellular learning automata and its applications. Appl. Soft Comput. 53, 1–18 (2017). https://doi.org/10.1016/j.asoc.2016.12.006
Akbari Torkestani, J., Meybodi, M.R.: Learning automata-based algorithms for finding minimum weakly connected dominating set in stochastic graphs. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 18, 721–758 (2010). https://doi.org/10.1142/S0218488510006775
Akbari Torkestani, J., Meybodi, M.R.: A learning automata-based heuristic algorithm for solving the minimum spanning tree problem in stochastic graphs. J. Supercomputing 59, 1035–1054 (2012). https://doi.org/10.1007/s11227-010-0484-1
Akhtari, M., Meybodi, M.R.: Memetic-CLA-PSO: a hybrid model for optimization. In: 2011 UkSim 13th International Conference on Computer Modelling and Simulation, pp. 20–25. IEEE (2011)
Aldrees, M., Ykhlef, M.: 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 2014, pp. 59–63. ACM Press, New York (2014)
Ali, K.I., Brohi, K.: An adaptive learning automata for genetic operators allocation probabilities. In: 2013 11th International Conference on Frontiers of Information Technology, pp. 55–59. IEEE (2013)
Alipour, M.M., Razavi, S.N., Feizi Derakhshi, M.R., Balafar, M.A.: A hybrid algorithm using a genetic algorithm and multiagent reinforcement learning heuristic to solve the traveling salesman problem. Neural Comput. Appl. 30, 2935–2951 (2018). https://doi.org/10.1007/s00521-017-2880-4
Alirezanejad, M., Enayatifar, R., Motameni, H., Nematzadeh, H.: GSA-LA: gravitational search algorithm based on learning automata. J. Exp. Theoret. Artif. Intell. 1–17 (2020). https://doi.org/10.1080/0952813X.2020.1725650
Amirazodi, N., Saghiri, A.M., Meybodi, M.: An adaptive algorithm for super-peer selection considering peer’s capacity in mobile peer-to-peer networks based on learning automata. Peer-to-Peer Network. Appl. 11, 74–89 (2018). https://doi.org/10.1007/s12083-016-0503-y
Amiri, F., Yazdani, N., Faili, H., Rezvanian, A.: A novel community detection algorithm for privacy preservation in social networks. In: Intelligent Informatics, pp. 443–450 (2013)
Arora, S., Anand, P.: Learning automata-based butterfly optimization algorithm for engineering design problems. Int. J. Comput. Mater. Sci. Eng. 07, 1850021 (2018). https://doi.org/10.1142/S2047684118500215
Aso, H., Kimura, M.: Absolute expediency of learning automata. Inf. Sci. 17, 91–112 (1979). https://doi.org/10.1016/0020-0255(79)90034-3
Barnard, C.J., Sibly, R.M.: Producers and scroungers: a general model and its application to captive flocks of house sparrows. Anim. Behav. 29, 543–550 (1981)
Barto, A.G., Sutton, R.S., Anderson, C.W.: Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Trans. Syst. Man Cybern. SMC-13, 834–846 (1983). https://doi.org/10.1109/TSMC.1983.6313077
Beheshtifard, Z., Meybodi, M.R.: An adaptive channel assignment in wireless mesh network: the learning automata approach. Comput. Electr. Eng. 72, 79–91 (2018). https://doi.org/10.1016/j.compeleceng.2018.09.004
Beigy, H., Meybodi, M.R.: A mathematical framework for cellular learning automata. Adv. Complex Syst. 07, 295–319 (2004). https://doi.org/10.1142/S0219525904000202
Beigy, H., Meybodi, M.R.: Utilizing distributed learning automata to solve stochastic shortest path problems. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 14, 591–615 (2006a). https://doi.org/10.1142/S0218488506004217
Beigy, H., Meybodi, M.R.: A new continuous action-set learning automaton for function optimization. J. Franklin Inst. 343, 27–47 (2006b)
Beigy, H., Meybodi, M.R.: Open synchronous cellular learning automata. Adv. Complex Syst. 10, 527–556 (2007)
Beigy, H., Meybodi, M.R.: Asynchronous cellular learning automata. Automatica 44, 1350–1357 (2008)
Beigy, H., Meybodi, M.R.: Cellular learning automata with multiple learning automata in each cell and its applications. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 40, 54–65 (2010). https://doi.org/10.1109/TSMCB.2009.2030786
Betka, A., Terki, N., Toumi, A., Dahmani, H.: Grey wolf optimizer-based learning automata for solving block matching problem. Signal Image Video Process. 14, 285–293 (2020). https://doi.org/10.1007/s11760-019-01554-w
Boveiri, H.R., Javidan, R., Khayami, R.: An intelligent hybrid approach for task scheduling in cluster computing environments as an infrastructure for biomedical applications. Expert Syst. (2020). https://doi.org/10.1111/exsy.12536
Bushehrian, O., Nejad, S.E.: Health-care pervasive environments: a CLA based trust management. pp. 247–257 (2017)
Chen, Y., He, H., Zhou, N.: Traffic flow modeling and simulation based on a novel cellular learning automaton. In: 2018 IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE), pp. 233–237. IEEE (2018)
Dai, C., Wang, Y., Ye, M., Xue, X., Liu, H.: An orthogonal evolutionary algorithm with learning automata for multiobjective optimization. IEEE Trans. Cybern. 46, 3306–3319 (2016). https://doi.org/10.1109/TCYB.2015.2503433
Daliri Khomami, M.M., Haeri, M.A., Meybodi, M.R., Saghiri, A.M.: An algorithm for weighted positive influence dominating set based on learning automata. In: 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), pp. 0734–0740. IEEE (2017)
Daliri Khomami, M.M., Rezvanian, A., Bagherpour, N., Meybodi, M.R.: Minimum positive influence dominating set and its application in influence maximization: a learning automata approach. Appl. Intell. 48, 570–593 (2018). https://doi.org/10.1007/s10489-017-0987-z
Daliri Khomami, M.M., Rezvanian, A., Saghiri, A.M., Meybodi, M.R.: SIG-CLA: a significant community detection based on cellular learning automata. In: 2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS). pp. 039–044 (2020b)
Daliri Khomami, M.M., Rezvanian, A., Saghiri, A.M., Meybodi, M.R.: Utilizing cellular learning automata for finding communities in weighted networks. In: 2020 6th International Conference on Web Research (ICWR), pp. 325–329 (2020a)
Damerchilu, B., Norouzzadeh, M.S., Meybodi, M.R.: Motion estimation using learning automata. Mach. Vis. Appl. 27, 1047–1061 (2016). https://doi.org/10.1007/s00138-016-0788-0
Deng, X., Jiang, Y., Yang, L.T., Yi, L., Chen, J., Liu, Y., Li, X.: Learning automata based confident information coverage barriers for smart ocean Internet of Things. IEEE Internet Things J. 1 (2020). https://doi.org/10.1109/JIOT.2020.2989696
Di, C., Zhang, B., Liang, Q., Li, S., Guo, Y.: Learning automata based access class barring scheme for massive random access in machine-to-machine communications. IEEE Internet Things J. 1 (2018). https://doi.org/10.1109/JIOT.2018.2867937
Di, C., Su, Y., Han, Z., Li, S.: Learning automata based SVM for intrusion detection, pp. 2067–2074 (2019)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2015)
El Hatri, C., Boumhidi, J.: Q-learning based intelligent multi-objective particle swarm optimization of light control for traffic urban congestion management. In: 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt), pp. 794–799. IEEE (2016)
Enayatifar, R., Yousefi, M., Abdullah, A.H., Darus, A.N.: LAHS: a novel harmony search algorithm based on learning automata. Commun. Nonlinear Sci. Numer. Simul. 18, 3481–3497 (2013). https://doi.org/10.1016/j.cnsns.2013.04.028
Esnaashari, M., Meybodi, M.R.: A cellular learning automata based clustering algorithm for wireless sensor networks. Sensor Lett. 6, 723–735 (2008)
Esnaashari, M., Meybodi, M.R.M.: A cellular learning automata-based deployment strategy for mobile wireless sensor networks. J. Parallel Distrib. Comput. 71, 988–1001 (2011)
Esnaashari, M., Meybodi, M.R.: Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach. Wirel. Netw. 19, 945–968 (2013). https://doi.org/10.1007/s11276-012-0511-7
Esnaashari, M., Meybodi, M.R.: Irregular cellular learning automata. IEEE Trans. Cybern. 45, 1622–1632 (2018). https://doi.org/10.1016/j.jocs.2017.08.012
Estahbanati, M.J.: Hybrid probabilistic-harmony search algorithm methodology in generation scheduling problem. J. Exp. Theoret. Artif. Intell. 26, 283–296 (2014)
Fahimi, M., Ghasemi, A.: A distributed learning automata scheme for spectrum management in self-organized cognitive radio network. IEEE Trans. Mob. Comput. 16, 1490–1501 (2017). https://doi.org/10.1109/TMC.2016.2601926
FathiNavid, A., Aghababa, A.B.: Irregular cellular learning automata-based method for intrusion detection in mobile ad hoc networks. In: 51st International FITCE (Federation of Telecommunications Engineers of the European Community), pp. 1–6 (2012)
Friedman, E., Shenker, S.: Synchronous and asynchronous learning by responsive learning automata (1996)
Ge, H., Huang, J., Di, C., Li, J., Li, S.: Learning automata based approach for influence maximization problem on social networks. In: 2017 IEEE Second International Conference on Data Science in Cyberspace (DSC), pp. 108–117. IEEE (2017)
Geshlag, M.B.M., Sheykhzadeh, J.: A new particle swarm optimization model based on learning automata using deluge algorithm for dynamic environments. J. Basic Appl. Sci. Res. 3, 394–404 (2012)
Ghamgosar, M., Khomami, M.M.D., Bagherpour, N., Meybodi, M.R.: An extended distributed learning automata based algorithm for solving the community detection problem in social networks. In: 2017 Iranian Conference on Electrical Engineering (ICEE), pp. 1520–1526. IEEE (2017)
Ghavipour, M., Meybodi, M.R.: An adaptive fuzzy recommender system based on learning automata. Electron. Commer. Res. Appl. 20, 105–115 (2016). https://doi.org/10.1016/j.elerap.2016.10.002
Ghavipour, M., Meybodi, M.R.: Irregular cellular learning automata-based algorithm for sampling social networks. Eng. Appl. Artif. Intell. 59, 244–259 (2017). https://doi.org/10.1016/j.engappai.2017.01.004
Ghavipour, M., Meybodi, M.R.: A dynamic algorithm for stochastic trust propagation in online social networks: learning automata approach. Comput. Commun. 123, 11–23 (2018a). https://doi.org/10.1016/j.comcom.2018.04.004
Ghavipour, M., Meybodi, M.R.: Trust propagation algorithm based on learning automata for inferring local trust in online social networks. Knowl. Based Syst. 143, 307–316 (2018b). https://doi.org/10.1016/j.knosys.2017.06.034
Ghavipour, M., Meybodi, M.R.: A streaming sampling algorithm for social activity networks using fixed structure learning automata. Appl. Intell. 48, 1054–1081 (2018c). https://doi.org/10.1007/s10489-017-1005-1
Ghosh, L., Ghosh, S., Konar, D., Konar, A., Nagar, A.K.: EEG-induced error correction in path planning by a mobile robot using learning automata. In: Soft Computing for Problem Solving, pp. 273–285 (2019)
Goodwin, M., Yazidi, A.: Distributed learning automata-based scheme for classification using novel pursuit scheme. Appl. Intell. (2020). https://doi.org/10.1007/s10489-019-01627-w
Hadavi, N., Nordin, M.d.J., Shojaeipour, A.: Lung cancer diagnosis using CT-scan images based on cellular learning automata. In: 2014 International Conference on Computer and Information Sciences (ICCOINS), pp. 1–5. IEEE (2014)
Han, Z., Li, S.: Opportunistic routing algorithm based on estimator learning automata, pp. 2486–2492 (2019)
Hariri, A., Rastegar, R., Zamani, M.S., Meybodi, M.R.: Parallel hardware implementation of cellular learning automata based evolutionary computing (CLA-EC) on FPGA. In: 13th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM 2005), pp. 311–314. IEEE (2005)
Farsi, H., Nasiripour, R., Mohammadzadeh, S.: Eye gaze detection based on learning automata by using SURF descriptor. J. Inf. Syst. Telecommun. (JIST) 21, 1–10 (2018). https://doi.org/10.7508/jist.2018.21.006
Hasanzadeh, M., Meybodi, M.R.: Grid resource discovery based on distributed learning automata. Computing 96, 909–922 (2014). https://doi.org/10.1007/s00607-013-0337-x
Hasanzadeh, M., Meybodi, M.R., Ebadzadeh, M.M.: A robust heuristic algorithm for cooperative particle swarm optimizer: a learning automata approach. In: ICEE 2012 - 20th Iranian Conference on Electrical Engineering, Tehran, Iran, pp. 656–661 (2012)
Hasanzadeh, M., Meybodi, M.R., Ebadzadeh, M.M.: Adaptive cooperative particle swarm optimizer. Appl. Intell. 39, 397–420 (2013). https://doi.org/10.1007/s10489-012-0420-6
Hasanzadeh, M., Meybodi, M.R., Ebadzadeh, M.M.: A learning automata approach to cooperative particle swarm optimizer. J. Inf. Syst. Telecommun. 6, 56–661 (2014). Tehran, Iran
Hasanzadeh, M., Sadeghi, S., Rezvanian, A., Meybodi, M.R.: Success rate group search optimiser. J. Exp. Theoret. Artif. Intell. 28, 53–69 (2016)
Hasanzadeh Mofrad, M, Sadeghi, S., Rezvanian, A., Meybodi, M.R.: Cellular edge detection: combining cellular automata and cellular learning automata. AEU Int. J. Electron. Commun. 69, 1282–1290 (2015). https://doi.org/10.1016/j.aeue.2015.05.010
Hasanzadeh-Mofrad, M., Rezvanian, A.: Learning automata clustering. J. Comput. Sci. 24, 379–388 (2018). https://doi.org/10.1016/j.jocs.2017.09.008
Hashemi, A.B., Meybodi, M.R.: A note on the learning automata based algorithms for adaptive parameter selection in PSO. Appl. Soft Comput. J. 11, 689–705 (2011). https://doi.org/10.1016/j.asoc.2009.12.030
Hassanzadeh, T., Meybodi, M.R.: A new hybrid algorithm based on firefly algorithm and cellular learning automata. In: 20th Iranian Conference on Electrical Engineering (ICEE 2012), pp. 628–633. IEEE (2012)
He, S., Wu, Q., Saunders, J.: Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans. Evol. Comput. 13, 973–990 (2009)
Howell, M.N., Gordon, T.J., Brandao, F.V.: Genetic learning automata for function optimization. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 32, 804–815 (2002). https://doi.org/10.1109/TSMCB.2002.1049614
Huang, J., Ge, H., Guo, Y., Zhang, Y., Li, S.: A learning automaton-based algorithm for influence maximization in social networks, pp. 715–722 (2018)
Iima, H., Kuroe, Y.: Swarm reinforcement learning algorithms based on Sarsa method. In: 2008 SICE Annual Conference, pp. 2045–2049. IEEE (2008)
Irandoost, M.A., Rahmani, A.M., Setayeshi, S.: A novel algorithm for handling reducer side data skew in MapReduce based on a learning automata game. Inf. Sci. 501, 662–679 (2019a). https://doi.org/10.1016/j.ins.2018.11.007
Irandoost, M.A., Rahmani, A.M., Setayeshi, S.: Learning automata-based algorithms for MapReduce data skewness handling. J. Supercomput. 75, 6488–6516 (2019b). https://doi.org/10.1007/s11227-019-02855-0
Jafarpour, B., Meybodi, M.R.: Recombinative CLA-EC. In: Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI. IEEE, pp. 415–422 (2007)
Jafarpour, B., Meybodi, M.R., Shiry, S.: A hybrid method for optimization (Discrete PSO + CLA). In: 2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007, pp. 55–60 (2007)
Jalali Moghaddam, M., Esmaeilzadeh, A., Ghavipour, M., Zadeh, A.K.: Minimizing virtual machine migration probability in cloud computing environments. Cluster Comput. (2020). https://doi.org/10.1007/s10586-020-03067-5
Javadi, M.S., Saniei, M., Rajabi Mashhadi, H.: An augmented NSGA-II technique with virtual database to solve the composite generation and transmission expansion planning problem. J. Exp. Theoret. Artif. Intell. 26, 211–234 (2014). https://doi.org/10.1080/0952813X.2013.815280
Javadi, M., Mostafaei, H., Chowdhurry, M.U., Abawajy, J.H.: Learning automaton based topology control protocol for extending wireless sensor networks lifetime. J. Netw. Comput. Appl. 122, 128–136 (2018). https://doi.org/10.1016/j.jnca.2018.08.012
Javadzadeh, R., Afsahi, Z., Meybodi, M.R.: Hybrid model base on artificial immune system and cellular learning automata (CLA-AIS). In: IASTED Technology Conferences/705: ARP/706: RA/707: NANA/728: CompBIO. ACTAPRESS, Calgary, AB, Canada (2010)
Jobava, A., Yazidi, A., Oommen, B.J., Begnum, K.: On achieving intelligent traffic-aware consolidation of virtual machines in a data center using Learning Automata. J. Comput. Sci. 24, 290–312 (2018). https://doi.org/10.1016/j.jocs.2017.08.005
John Oommen, B., Agache, M.: Continuous and discretized pursuit learning schemes: various algorithms and their comparison. IEEE Trans. Syst. Man Cybern. Part B Cybern. 31, 277–287 (2001). https://doi.org/10.1109/3477.931507
Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996). https://doi.org/10.1613/jair.301
Kahani, N., Fallah, M.S.: A reactive defense against bandwidth attacks using learning automata. In: Proceedings of the 13th International Conference on Availability, Reliability and Security - ARES 2018, pp. 1–6. ACM Press, New York (2018)
Kamarian, S., Yas, M.H., Pourasghar, A., Daghagh, M.: Application of firefly algorithm and ANFIS for optimisation of functionally graded beams. J. Exp. Theoret. Artif. Intell. 26, 197–209 (2014). https://doi.org/10.1080/0952813X.2013.813978
Kavousi-Fard, A., Kavousi-Fard, F.: A new hybrid correction method for short-term load forecasting based on ARIMA, SVR and CSA. J. Exp. Theoret. Artif. Intell. 25, 559–574 (2013). https://doi.org/10.1080/0952813X.2013.782351
Kazemi Kordestani, J., Meybodi, M.R., Rahmani, A.M.: A two-level function evaluation management model for multi-population methods in dynamic environments: hierarchical learning automata approach. J. Exp. Theoret. Artif. Intell. 1–26 (2020). https://doi.org/10.1080/0952813X.2020.1721568
Khadangi, E., Bagheri, A., Shahmohammadi, A.: Biased sampling from facebook multilayer activity network using learning automata. Appl. Intell. 45, 829–849 (2016). https://doi.org/10.1007/s10489-016-0784-0
Khani, M., Ahmadi, A., Hajary, H.: Distributed task allocation in multi-agent environments using cellular learning automata. Soft Comput. (2017). https://doi.org/10.1007/s00500-017-2839-5
Kheradmand, S., Meybodi, M.R.: Price and QoS competition in cloud market by using cellular learning automata. In: 2014 4th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 340–345. IEEE (2014)
Khezri, S., Meybodi, M.R.: Improving imperialist competitive algorithm using learning automata. In: 16th Annual CSI Computer Conference (CSI 2011), Tehran, Iran (2011)
Khomami, M.M.D., Rezvanian, A., Meybodi, M.R.: Distributed learning automata-based algorithm for community detection in complex networks. Int. J. Mod. Phys. B 30, 1650042 (2016b). https://doi.org/10.1142/S0217979216500429
Khomami, M.M.D., Bagherpour, N., Sajedi, H., Meybodi, M.R.: A new distributed learning automata based algorithm for maximum independent set problem. In: 2016 Artificial Intelligence and Robotics (IRANOPEN), Qazvin, Iran, Iran, pp. 12–17. IEEE (2016a)
Khomami, M.M.D., Rezvanian, A., Meybodi, M.R.: A new cellular learning automata-based algorithm for community detection in complex social networks. J. Comput. Sci. 24, 413–426 (2018). https://doi.org/10.1016/j.jocs.2017.10.009
Khomami, M.M.D., Rezvanian, A., Saghiri, A.M., Meybodi, M.R.: Overlapping community detection in social networks using cellular learning automata. In: 2020 28th Iranian Conference on Electrical Engineering (ICEE), pp. 1–6. IEEE (2020)
Khomami, M.M.D., Rezvanian, A., Meybodi, M.R., Bagheri, A.: CFIN: a community-based algorithm for finding influential nodes in complex social networks. J. Supercomput. 2207–2236 (2021). https://doi.org/10.1007/s11227-020-03355-2
King-Sun, F.: Learning control systems–review and outlook. IEEE Trans. Autom. Control 15, 210–221 (1970). https://doi.org/10.1109/TAC.1970.1099405
Kordestani, J.K., Rezvanian, A., Meybodi, M.R.: CDEPSO: a bi-population hybrid approach for dynamic optimization problems. Appl. Intell. 40, 682–694 (2014a). https://doi.org/10.1007/s10489-013-0483-z
Kordestani, J.K., Ahmadi, A., Meybodi, M.R.: An improved differential evolution algorithm using learning automata and population topologies. Appl. Intell. 41, 1150–1169 (2014b). https://doi.org/10.1007/s10489-014-0585-2
Kordestani, J.K., Firouzjaee, H.A., Meybodi, M.R.: An adaptive bi-flight cuckoo search with variable nests for continuous dynamic optimization problems. Appl. Intell. 48, 97–117 (2018). https://doi.org/10.1007/s10489-017-0963-7
Kordestani, J.K., Ranginkaman, A.E., Meybodi, M.R., Novoa-Hernández, P.: A novel framework for improving multi-population algorithms for dynamic optimization problems: a scheduling approach. Swarm Evol. Comput. 44, 788–805 (2019). https://doi.org/10.1016/j.swevo.2018.09.002
Krishna, P.V., Misra, S., Joshi, D., Obaidat, M.S.: Learning Automata Based Sentiment Analysis for recommender system on cloud. In: 2013 International Conference on Computer, Information and Telecommunication Systems (CITS), pp. 1–5. IEEE (2013)
Krishna, P.V., Misra, S., Joshi, D., Gupta, A., Obaidat, M.S.: Secure socket layer certificate verification: a learning automata approach. Secur. Commun. Netw. 7, 1712–1718 (2014). https://doi.org/10.1002/sec.867
Kumar, N., Lee, J.-H., Rodrigues, J.J.: 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 (2015). https://doi.org/10.1109/TITS.2014.2354372
Kumar, N., Misra, S., Obaidat, M.S.: Collaborative learning automata-based routing for rescue operations in dense urban regions using vehicular sensor networks. IEEE Syst. J. 9, 1081–1090 (2015). https://doi.org/10.1109/JSYST.2014.2335451
Lanctot, J.K., Oommen, B.J.: Discretized estimator learning automata. IEEE Trans. Syst. Man Cybern. 22, 1473–1483 (1992). https://doi.org/10.1109/21.199471
Li, W., Ozcan, E., John, R.: A learning automata based multiobjective hyper-heuristic. IEEE Trans. Evol. Comput. 1 (2018). https://doi.org/10.1109/TEVC.2017.2785346
Lingam, G., Rout, R.R., Somayajulu, D.: Learning automata-based trust model for user recommendations in online social networks. Comput. Electr. Eng. 66, 174–188 (2018). https://doi.org/10.1016/j.compeleceng.2017.10.017
Mahdaviani, M., Kordestani, J.K., Rezvanian, A., Meybodi, M.R.: LADE: learning automata based differential evolution. Int. J. Artif. Intell. Tools 24, 1550023 (2015). https://doi.org/10.1142/S0218213015500232
Mahdaviani, M., Kordestani, J.K., Rezvanian, A., Meybodi, M.R: LADE: learning automata based differential evolution. Int. J. Artif. Intell. Tools 24, 1550023 (2015). https://doi.org/10.1142/S0218213015500232
Mahmoudi, M., Faez, K., Ghasemi, A.: Defense against primary user emulation attackers based on adaptive Bayesian learning automata in cognitive radio networks. Ad Hoc Netw. 102, 102147 (2020). https://doi.org/10.1016/j.adhoc.2020.102147
Manshad, M.K., Meybodi, M.R., Salajegheh, A.: A new irregular cellular learning automata-based evolutionary computation for time series link prediction in social networks. Appl. Intell. 51, 71–84 (2021)
Manurung, R., Ritchie, G., Thompson, H.: Using genetic algorithms to create meaningful poetic text. J. Exp. Theor. Artif. Intell. 24, 43–64 (2012). https://doi.org/10.1080/0952813X.2010.539029
Meybodi, M.R., Lakshmivarahan, S.: ε-Optimality of a general class of learning algorithms. Inf. Sci. 28, 1–20 (1982). https://doi.org/10.1016/0020-0255(82)90029-9
Misra, S., Interior, B., Kumar, N., Misra, S., Obaidat, M., Rodrigues, J., Pati, B.: Networks of learning automata for the vehicular environment: a performance analysis study. IEEE Wirel. Commun. 21, 41–47 (2014). https://doi.org/10.1109/MWC.2014.7000970
Mollakhalili Meybodi, M.R., Meybodi, M.R.: Extended distributed learning automata: an automata-based framework for solving stochastic graph. Appl. Intell. 41, 923–940 (2014)
Mollakhalili Meybodi, M.R., Meybodi, M.R.: Extended distributed learning automata. Appl. Intell. 41, 923–940 (2014). https://doi.org/10.1007/s10489-014-0577-2
Montague, P.R.: Reinforcement learning: an introduction, by Sutton, R.S. and Barto, A.G. Trends Cogn. Sci. 3, 360 (1999). https://doi.org/10.1016/S1364-6613(99)01331-5
Moradabadi, B., Meybodi, M.R.: Link prediction based on temporal similarity metrics using continuous action set learning automata. Phys. A 460, 361–373 (2016). https://doi.org/10.1016/j.physa.2016.03.102
Moradabadi, B., Meybodi, M.R.: Link prediction in fuzzy social networks using distributed learning automata. Appl. Intell. 47, 837–849 (2017a). https://doi.org/10.1007/s10489-017-0933-0
Moradabadi, B., Meybodi, M.R.: A novel time series link prediction method: learning automata approach. Phys. A 482, 422–432 (2017b). https://doi.org/10.1016/j.physa.2017.04.019
Moradabadi, B., Meybodi, M.R.: Link prediction in stochastic social networks: learning automata approach. J. Comput. Sci. 24, 313–328 (2018a). https://doi.org/10.1016/j.jocs.2017.08.007
Moradabadi, B., Meybodi, M.R.: Link prediction in weighted social networks using learning automata. Eng. Appl. Artif. Intell. 70, 16–24 (2018b). https://doi.org/10.1016/j.engappai.2017.12.006
Moradabadi, B., Meybodi, M.R.: Wavefront cellular learning automata. Chaos 28, 21101 (2018c). https://doi.org/10.1063/1.5017852
Morshedlou, H., Meybodi, M.R.: Decreasing impact of SLA violations:a proactive resource allocation approachfor cloud computing environments. IEEE Trans. Cloud Comput. 2, 156–167 (2014). https://doi.org/10.1109/TCC.2014.2305151
Morshedlou, H., Meybodi, M.R.: A new local rule for convergence of ICLA to a compatible point. IEEE Trans. Syst. Man Cybern. Syst. 47, 3233–3244 (2017). https://doi.org/10.1109/TSMC.2016.2569464
Morshedlou, H., Meybodi, M.R.: A new learning automata based approach for increasing utility of service providers. Int. J. Commun. Syst. 31, e3459 (2018). https://doi.org/10.1002/dac.3459
Mostafaei, H.: Stochastic barrier coverage in wireless sensor networks based on distributed learning automata. Comput. Commun. 55, 51–61 (2015)
Mostafaei, H.: Energy-efficient algorithm for reliable routing of wireless sensor networks. IEEE Trans. Ind. Electron. 1 (2018). https://doi.org/10.1109/TIE.2018.2869345
Mostafaei, H., Obaidat, M.S.: A distributed efficient algorithm for self-protection of wireless sensor networks. In: 2018 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2018a)
Mostafaei, H., Obaidat, M.S.: Learning automaton-based self-protection algorithm for wireless sensor networks. IET Netw. 7, 353–361 (2018b). https://doi.org/10.1049/iet-net.2018.0005
Motiee, S., Meybodi, M.R.: Identification of web communities using cellular learning automata. In: 2009 14th International CSI Computer Conference, pp. 553–563. IEEE (2009)
Mousavian, A., Rezvanian, A., Meybodi, M.R.: Solving minimum vertex cover problem using learning automata. In: 13th Iranian Conference on Fuzzy Systems (IFSC 2013), pp. 1–5 (2013)
Mousavian, A., Rezvanian, A., Meybodi, M.R.: Cellular learning automata based algorithm for solving minimum vertex cover problem. In: 2014 22nd Iranian Conference on Electrical Engineering (ICEE), pp. 996–1000. IEEE (2014)
Mozafari, M., Shiri, M.E., Beigy, H.: A cooperative learning method based on cellular learning automata and its application in optimization problems. J. Comput. Sci. 11, 279–288 (2015). https://doi.org/10.1016/j.jocs.2015.08.002
Nabizadeh, S., Rezvanian, A., Meybodi, M.R.: Tracking extrema in dynamic environment using multi-swarm cellular PSO with local search. Int. J. Electron. Inform. 1, 29–37 (2012)
Kumpati, S., Narendra, M.A.L.T.: Learning Automata: An Introduction. Prentice-Hall (1989)
Narendra, K.S., Thathachar, M.A.L.: Learning automata - a survey. IEEE Trans. Syst. Man. Cybern. SMC-4, 323–334 (1974). https://doi.org/10.1109/TSMC.1974.5408453
Nesi, L.C., da Righi, R.R.: H2-SLAN: a hyper-heuristic based on stochastic learning automata network for obtaining, storing, and retrieving heuristic knowledge. Expert Syst. Appl. 153, 113426 (2020). https://doi.org/10.1016/j.eswa.2020.113426
Oommen, B.J., Ma, D.C.Y.: Deterministic learning automata solutions to the equipartitioning problem. IEEE Trans. Comput. 37, 2–13 (1988)
Papadimitriou, G.I., Vasilakos, A.V., Papadimitriou, G.I., Paximadis, C.T.: A new approach to the design of reinforcement schemes for learning automata: stochastic estimator learning algorithms. In: Conference Proceedings 1991 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1387–1392. IEEE (1991)
Papadimitriou, G.I., Pomportsis, A.S., Kiritsi, S., Talahoupi, E.: Absorbing stochastic estimator learning algorithms with high accuracy and rapid convergence. In: Proceedings ACS/IEEE International Conference on Computer Systems and Applications. IEEE Comput. Soc, pp. 45–51 (2002)
Parvanak, A.R., Jahanshahi, M., Dehghan, M.: A cross-layer learning automata based gateway selection method in multi-radio multi-channel wireless mesh networks. Computing (2018). https://doi.org/10.1007/s00607-018-0648-z
Qavami, H.R., Jamali, S., Akbari, M.K., Javadi, B.: A learning automata based dynamic resource provisioning in cloud computing environments. In: 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), pp. 502–509. IEEE (2017)
Qureshi, M.N., Tiwana, M.I., Haddad, M.: Distributed self optimization techniques for heterogeneous network environments using active antenna tilt systems. Telecommun. Syst. 70, 379–389 (2019). https://doi.org/10.1007/s11235-018-0494-5
Rahmani, P., Javadi, H.H.S., Bakhshi, H., Hosseinzadeh, M.: TCLAB: a new topology control protocol in cognitive MANETs based on learning automata. J. Network Syst. Manage. 26, 426–462 (2018). https://doi.org/10.1007/s10922-017-9422-3
Rahmanian, A.A., Ghobaei-Arani, M., Tofighy, S.: A learning automata-based ensemble resource usage prediction algorithm for cloud computing environment. Future Gener. Comput. Syst. 79, 54–71 (2018). https://doi.org/10.1016/j.future.2017.09.049
Rasouli, N., Razavi, R., Faragardi, H.R.: EPBLA: energy-efficient consolidation of virtual machines using learning automata in cloud data centers. Cluster Comput. (2020). https://doi.org/10.1007/s10586-020-03066-6
Rastegar, R., Meybodi, M.R.: A new evolutionary computing model based on cellular learning automata. In: IEEE Conference on Cybernetics and Intelligent Systems, 2004, pp. 433–438. IEEE (2004)
Rastegar, R., Rahmati, M., Meybodi, M.R.: A clustering algorithm using cellular learning automata based evolutionary algorithm. In: Adaptive and Natural Computing Algorithms, pp. 144–150. Springer, Vienna (2005)
Ren, J., Wu, G., Su, X., Cui, G., Xia, F., Obaidat, M.S.: Learning automata-based data aggregation tree construction framework for cyber-physical systems. IEEE Syst. J. 12, 1467–1479 (2018). https://doi.org/10.1109/JSYST.2015.2507577
Rezaee Jordehi, A., Jasni, J.: Parameter selection in particle swarm optimisation: a survey. J. Exp. Theoret. Artif. Intell. 25, 527–542 (2013)
Rezapoor Mirsaleh, M., Meybodi, M.R.: LA-MA: a new memetic model based on learning automata. In: 18th National Conference of Computer Society of Iran, pp 1–6 (2013)
Rezapoor Mirsaleh, M., Meybodi, M.R.: A learning automata-based memetic algorithm. Genet. Program. Evol. Mach. 16, 399–453 (2015). https://doi.org/10.1007/s10710-015-9241-9
Rezapoor Mirsaleh, M., Meybodi, M.R.: A new memetic algorithm based on cellular learning automata for solving the vertex coloring problem. Memetic Comput. 8, 211–222 (2016). https://doi.org/10.1007/s12293-016-0183-4
Rezapoor Mirsaleh, M., Meybodi, M.R.: Assignment of cells to switches in cellular mobile network: a learning automata-based memetic algorithm. Appl. Intell. 48, 3231–3247 (2018a). https://doi.org/10.1007/s10489-018-1136-z
Rezapoor Mirsaleh, M., Meybodi, M.R.: A Michigan memetic algorithm for solving the vertex coloring problem. J. Comput. Sci. 24, 389–401 (2018b). https://doi.org/10.1016/j.jocs.2017.10.005
Rezapoor Mirsaleh, M., Meybodi, M.R.: Balancing exploration and exploitation in memetic algorithms: a learning automata approach. Comput. Intell. 34, 282–309 (2018c). https://doi.org/10.1111/coin.12148
Rezvanian, A., Meybodi, M.R.: 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), pp. 479–483. IEEE (2010a)
Rezvanian, A., Meybodi, M.R.: Tracking extrema in dynamic environments using a learning automata-based immune algorithm. In: Communications in Computer and Information Science, pp. 216–225. Springer, Heidelberg (2010b)
Rezvanian, A., Meybodi, M.R.: LACAIS: Learning automata based cooperative artificial immune system for function optimization. In: Communications in Computer and Information Science, pp. 64–75. Springer, Heidelberg (2010c)
Rezvanian, A., Meybodi, M.R.: 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), pp. 479–483. IEEE (2010d)
Rezvanian, A., Meybodi, M.R.: Finding maximum clique in stochastic graphs using distributed learning automata. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 23, 1–31 (2015a). https://doi.org/10.1142/S0218488515500014
Rezvanian, A., Meybodi, M.R.: Finding minimum vertex covering in stochastic graphs: a learning automata approach. Cyber. Syst. 46, 698–727 (2015b). https://doi.org/10.1080/01969722.2015.1082407
Rezvanian, A., Meybodi, M.R.: Stochastic Social Networks: Measures and Algorithms. LAP LAMBERT Academic Publishing (2016a)
Rezvanian, A., Meybodi, M.R.: Stochastic graph as a model for social networks. Comput. Hum. Behav. 64, 621–640 (2016b). https://doi.org/10.1016/j.chb.2016.07.032
Rezvanian, A., Meybodi, M.R.: Sampling algorithms for stochastic graphs: a learning automata approach. Knowl. Based Syst. 127, 126–144 (2017a). https://doi.org/10.1016/j.knosys.2017.04.012
Rezvanian, A., Meybodi, M.R.: A new learning automata-based sampling algorithm for social networks. Int. J. Commun. Syst. 30, e3091 (2017b). https://doi.org/10.1002/dac.3091
Rezvanian, A., Rahmati, M., Meybodi, M.R.: Sampling from complex networks using distributed learning automata. Physica A 396, 224–234 (2014). https://doi.org/10.1016/j.physa.2013.11.015
Rezvanian, A., Saghiri, A.M., Vahidipour, S.M., Esnaashari, M., Meybodi, M.R.: Learning automata theory. In: Recent Advances in Learning Automata, pp. 3–19. Springer (2018a)
Rezvanian, A., Saghiri, A.M., Vahidipour, S.M., Esnaashari, M., Meybodi, M.R.: Recent Advances in Learning Automata. Springer (2018b)
Rezvanian, A., Saghiri, A.M., Vahidipour, S.M., Esnaashari, M., Meybodi, M.R.: Cellular Learning Automata. pp 21–88 (2018c)
Rezvanian, A., Vahidipour, S.M., Esnaashari, M.: New applications of learning automata-based techniques in real-world environments. J. Comput. Sci. 24, 287–289 (2018d). https://doi.org/10.1016/j.jocs.2017.11.012
Rezvanian, A., Saghiri, A.M., Vahidipour, S.M., Esnaashari, M., Meybodi, M.R.: Learning automata for cognitive peer-to-peer networks. In: Recent Advances in Learning Automata, pp. 221–278 (2018e)
Rezvanian, A., Saghiri, A.M., Vahidipour, S.M., Esnaashari, M., Meybodi, M.R.: Learning automata for wireless sensor networks. In: Recent Advances in Learning Automata, pp. 91–219 (2018f)
Rezvanian, A., Moradabadi, B., Ghavipour, M., Daliri Khomami, M.M., Meybodi, M.R.: Social recommender systems. In: Learning Automata Approach for Social Networks, pp. 281–313. Springer (2019a)
Rezvanian, A., Moradabadi, B., Ghavipour, M., Daliri Khomami, M.M., Meybodi, M.R.: Wavefront cellular learning automata: a new learning paradigm. In: Learning Automata Approach for Social Networks, pp. 51–74. Springer (2019b)
Rezvanian, A., Moradabadi, B., Ghavipour, M., Daliri Khomami, M.M., Meybodi, M.R.: Social networks and learning systems: a bibliometric analysis. In: Learning Automata Approach for Social Networks, pp. 75–89. Springer (2019c)
Rezvanian, A., Moradabadi, B., Ghavipour, M., Khomami, M.M.D., Meybodi, M.R.: Social link prediction. In: Learning Automata Approach for Social Networks, pp. 169–239. Springer (2019d)
Rezvanian, A., Moradabadi, B., Ghavipour, M., Daliri Khomami, M.M., Meybodi, M.R.: Social trust management. In: Learning Automata Approach for Social Networks, pp. 241–279. Springer (2019e)
Rezvanian, A., Moradabadi, B., Ghavipour, M., Daliri Khomami, M.M., Meybodi, M.R.: Learning Automata Approach for Social Networks. Springer International Publishing (2019f)
Rezvanian, A., Moradabadi, B., Ghavipour, M., Daliri Khomami, M.M., Meybodi, M.R.: Introduction to learning automata models. In: Learning Automata Approach for Social Networks, pp. 1–49. Springer (2019g)
Willianms, R.J.: Toward a Theory of Reinforcement-Learning Connectionist Systems. Northeastern University (1988)
Roohollahi, S., Bardsiri, A.K., Keynia, F.: Using an evaluator fixed structure learning automata in sampling of social networks. J AI Data Min. 8, 127–148 (2020). https://doi.org/10.22044/JADM.2019.7145.1842
Ruan, X., Jin, Z., Tu, H., Li, Y.: Dynamic cellular learning automata for evacuation simulation. IEEE Intell. Transp. Syst. Mag. 11, 129–142 (2019). https://doi.org/10.1109/MITS.2019.2919523
Rummery, G.A.A., Niranjan, M.: On-line Q-learning using connectionist systems. University of Cambridge, Department of Engineering (1994)
Safara, F., Souri, A., Deiman, S.F.: Super peer selection strategy in peer-to-peer networks based on learning automata. Int. J. Commun. Syst. 33, e4296 (2020). https://doi.org/10.1002/dac.4296
Saghiri, A.M., Meybodi, M.R.: An approach for designing cognitive engines in cognitive peer-to-peer networks. J. Netw. Comput. Appl. 70, 17–40 (2016). https://doi.org/10.1016/j.jnca.2016.05.012
Saghiri, A.M., Meybodi, M.R.: A closed asynchronous dynamic model of cellular learning automata and its application to peer-to-peer networks. Genet. Program. Evol. Mach. 18, 313–349 (2017a). https://doi.org/10.1007/s10710-017-9299-7
Saghiri, A.M., Meybodi, M.R.: 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 (2017b). https://doi.org/10.1002/dac.2977
Saghiri, A.M., Meybodi, M.R.: An adaptive super-peer selection algorithm considering peers capacity utilizing asynchronous dynamic cellular learning automata. Appl. Intell. 48, 271–299 (2018a). https://doi.org/10.1007/s10489-017-0946-8
Saghiri, A.M., Meybodi, M.R.: Open asynchronous dynamic cellular learning automata and its application to allocation hub location problem. Knowl. Based Syst. 139, 149–169 (2018b). https://doi.org/10.1016/j.knosys.2017.10.021
Saleem, A., Afzal, M.K., Ateeq, M., Kim, S.W., Bin, Z.Y.: Intelligent learning automata-based objective function in RPL for IoT. Sustain. Cities Soc. 59, 102234 (2020). https://doi.org/10.1016/j.scs.2020.102234
Samma, H., Lim, C.P., Mohamad Saleh, J.: A new reinforcement learning-based memetic particle swarm optimizer. Appl. Soft Comput. 43, 276–297 (2016). https://doi.org/10.1016/j.asoc.2016.01.006
Santoso, J., Riyanto, B., Adiprawita, W.: Dynamic path planning for mobile robots with cellular learning automata. J. ICT Res. Appl. 10, 1–14 (2016). https://doi.org/10.5614/itbj.ict.res.appl.2016.10.1.1
Saraeian, S., Shirazi, B., Motameni, H.: Optimal autonomous architecture for uncertain processes management. Inf. Sci. 501, 84–99 (2019). https://doi.org/10.1016/j.ins.2019.05.095
Savargiv, M., Masoumi, B., Keyvanpour, M.R.: A new ensemble learning method based on learning automata. J. Ambient Intell. Human. Comput. (2020). https://doi.org/10.1007/s12652-020-01882-7
Schwartz, A.: A reinforcement learning method for maximizing undiscounted rewards. In: Machine Learning Proceedings 1993, pp. 298–305 (1993)
Sengupta, A., Chakraborti, T., Konar, A., Kim, E., Nagar, A.K.: An adaptive memetic algorithm using a synergy of differential evolution and learning automata. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2012)
Seyyedi, S.H., Minaei-Bidgoli, B.: Estimator learning automata for feature subset selection in high-dimensional spaces, case study: email spam detection. Int. J. Commun. Syst. 31, e3541 (2018). https://doi.org/10.1002/dac.3541
Shen, X.-N., Minku, L.L., Marturi, N., Guo, Y.-N., Han, Y.: A Q-learning-based memetic algorithm for multi-objective dynamic software project scheduling. Inf. Sci. 428, 1–29 (2018). https://doi.org/10.1016/j.ins.2017.10.041
Sheng, X., Xu, W.: Solving the economic dispatch problem with q-learning quantum-behaved particle swarm optimization method. In: 2015 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), pp. 98–101. IEEE (2015)
Sheybani, M., Meybodi, M.R.: PSO-LA: a new model for optimization. In: 12th Annual International Computer Society of Iran Computer Conference CSICC2007, Iran, pp. 1162–1169 (2007a)
Sheybani, M., Meybodi, M.R.: CLA-PSO: a new model for optimization. In: Proceedings of the 15th Conference on Electrical Engineering, Volume on Computer, Telecommunication Research Center, Tehran, Iran, pp. 1–8 (2007b)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98TH8360), pp. 69–73. IEEE (1998)
Shyu, S.J., Yin, P.-Y., Lin, B.M., Haouari, M.: Ant-tree: an ant colony optimization approach to the generalized minimum spanning tree problem. J. Exp. Theoret. Artif. Intell. 15, 103–112 (2003)
Sikeridis, D., Tsiropoulou, E.E., Devetsikiotis, M., Papavassiliou, S.: Socio-physical energy-efficient operation in the internet of multipurpose things. In: 2018 IEEE International Conference on Communications (ICC), pp. 1–7. IEEE (2018)
Simha, R., Kurose, J.F.: Relative reward strength algorithms for learning automata. IEEE Trans. Syst. Man Cybern. 19, 388–398 (1989). https://doi.org/10.1109/21.31041
Sohrabi, M.K., Roshani, R.: Frequent itemset mining using cellular learning automata. Comput. Hum. Behav. 68, 244–253 (2017). https://doi.org/10.1016/j.chb.2016.11.036
Soleimani-Pouri, M., Rezvanian, A., Meybodi, M.R.: Solving maximum clique problem in stochastic graphs using learning automata. In: 2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN), pp. 115–119. IEEE (2012)
Soleimani-pouri, M., Rezvanian, A., Meybodi, M.R.: An ant based particle swarm optimization algorithm for maximum clique problem in social networks. In: Can, F., Özyer, T., Polat, F. (eds.) State of the Art Applications of Social Network Analysis, pp. 295–304. Springer (2014)
Stuart, R., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Printice-Hall (2002)
Su, Y., Qi, K., Di, C., Ma, Y., Li, S.: Learning automata based feature selection for network traffic intrusion detection. In: 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC), pp. 622–627. IEEE (2018)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Thakur, D., Khatua, M.: Cellular Learning Automata-Based Virtual Network Embedding in Software-Defined Networks, pp. 173–182 (2019)
Thathachar, M.A.L., Harita, B.R.: Learning automata with changing number of actions. IEEE Trans. Syst. Man Cybern. 17, 1095–1100 (1987). https://doi.org/10.1109/TSMC.1987.6499323
Thathachar, M.A.L., Ramachandran, K.M.: Asymptotic behaviour of a learning algorithm. Int. J. Control 39, 827–838 (1984). https://doi.org/10.1080/00207178408933209
Thathachar, M.A.L., Sastry, P.S.: A new approach to the design of reinforcement schemes for learning automata. IEEE Trans. Syst. Man Cybern. SMC-15, 168–175 (1985a). https://doi.org/10.1109/TSMC.1985.6313407
Thathachar, M.A.L., Sastry, P.S.: A class of rapidly converging algorithms for learning automata. IEEE Trans. Syst. Man Cybern. SMC-15, 168–175 (1985b)
Thathachar, M., Sastry, P.: Estimator algorithms for learning automata. In: Proceedings of the Platinum Jubilee Conference on Systems and Signal Processing, Bengalore, India (1986)
Thathachar, M.A.L., Sastry, P.S.: Varieties of learning automata: an overview. IEEE Trans. Syst. Man Cybern. Part B Cybern. 32, 711–722 (2002). https://doi.org/10.1109/TSMCB.2002.1049606
Thathachar, M.A.L., Sastry, P.S.: Networks of Learning Automata. Springer, Boston (2004)
Toffolo, T.A.M., Christiaens, J., Van Malderen, S., Wauters, T., Vanden Berghe, G.: Stochastic local search with learning automaton for the swap-body vehicle routing problem. Comput. Oper. Res. 89, 68–81 (2018). https://doi.org/10.1016/j.cor.2017.08.002
Toozandehjani, H., Zare-Mirakabad, M.-R., Derhami, V.: Improvement of recommendation systems based on cellular learning automata. In: 2014 4th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 592–597. IEEE (2014)
Tsetlin, M.L.: On the behavior of finite automata in random media. Autom. Remote Control 22, 1210–1219 (1962)
Vafaee Sharbaf, F., Mosafer, S., Moattar, M.H.: A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization. Genomics 107, 231–238 (2016). https://doi.org/10.1016/j.ygeno.2016.05.001
Vafashoar, R., Meybodi, M.R.: Multi swarm bare bones particle swarm optimization with distribution adaption. Appl. Soft Comput. J. 47, 534–552 (2016). https://doi.org/10.1016/j.asoc.2016.06.028
Vafashoar, R., Meybodi, M.R.: Multi swarm optimization algorithm with adaptive connectivity degree. Appl. Intell. 48, 909–941 (2018). https://doi.org/10.1007/s10489-017-1039-4
Vafashoar, R., Meybodi, M.R.: Reinforcement learning in learning automata and cellular learning automata via multiple reinforcement signals. Knowl. Based Syst. 169, 1–27 (2019a). https://doi.org/10.1016/j.knosys.2019.01.021
Vafashoar, R., Meybodi, M.R.: Cellular learning automata based bare bones PSO with maximum likelihood rotated mutations. Swarm Evol. Comput. 44, 680–694 (2019b). https://doi.org/10.1016/j.swevo.2018.08.016
Vafashoar, R., Meybodi, M.R.: A multi-population differential evolution algorithm based on cellular learning automata and evolutionary context information for optimization in dynamic environments. Appl. Soft Comput. 88, 106009 (2020). https://doi.org/10.1016/j.asoc.2019.106009
Vafashoar, R., Meybodi, M.R., Momeni Azandaryani, A.H.: CLA-DE: a hybrid model based on cellular learning automata for numerical optimization. Appl. Intell. 36, 735–748 (2012). https://doi.org/10.1007/s10489-011-0292-1
Vahidipour, S.M., Meybodi, M.R., Esnaashari, M.: Finding the shortest path in stochastic graphs using learning automata and adaptive stochastic petri nets. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 25, 427–455 (2017b). https://doi.org/10.1142/S0218488517500180
Vahidipour, S.M., Meybodi, M.R., Esnaashari, M.: Adaptive Petri net based on irregular cellular learning automata with an application to vertex coloring problem. Appl. Intell. 46, 272–284 (2017a). https://doi.org/10.1007/s10489-016-0831-x
Vahidipour, S.M., Esnaashari, M., Rezvanian, A., Meybodi, M.R.: GAPN-LA: a framework for solving graph problems using Petri nets and learning automata. Eng. Appl. Artif. Intell. 77, 255–267 (2019). https://doi.org/10.1016/j.engappai.2018.10.013
Vasilakos, A.V., Paximadis, C.T.: Faulttolerant routing algorithms using estimator discretized learning automata for high-speed packet-switched networks. IEEE Trans. Reliab. 43, 582–593 (1994). https://doi.org/10.1109/24.370222
Velusamy, G., Lent, R.: Dynamic cost-aware routing of web requests. Future Internet 10, 57 (2018). https://doi.org/10.3390/fi10070057
Verbeeck, K., Nowé, A., Nowe, A.: Colonies of learning automata. IEEE Trans. Syst. Man Cybern. Part B Cybern. 32, 772–780 (2002). https://doi.org/10.1109/TSMCB.2002.1049611
Watkins, C.C.J.H.: Learning from Delayed Rewards (1989)
Wolfram, S.: Theory and applications of cellular automata. World Scientific Publication (1986)
Wu, G., Mallipeddi, R., Suganthan, P.N.: Ensemble strategies for population-based optimization algorithms – a survey. Swarm Evol. Comput. 44, 695–711 (2019). https://doi.org/10.1016/j.swevo.2018.08.015
Xue, L., Sun, C., Wunsch, D.C.: A game-theoretical approach for a finite-time consensus of second-order multi-agent system. Int. J. Control Autom. Syst. 17, 1071–1083 (2019). https://doi.org/10.1007/s12555-017-0716-8
Yas, M.H., Kamarian, S., Pourasghar, A.: Application of imperialist competitive algorithm and neural networks to optimise the volume fraction of three-parameter functionally graded beams. J. Exp. Theoret. Artif. Intell. 26, 1–12 (2014)
Yazdani, D., Golyari, S., Meybodi, M.R.: A new hybrid algorithm for optimization based on artificial fish swarm algorithm and cellular learning automata. In: In: Proceedings of 2010 5th International Symposium on Telecommunications (IST), Tehran, Iran, pp. 932–937 (2010)
Yazidi, A., Bouhmala, N., Goodwin, M.: A team of pursuit learning automata for solving deterministic optimization problems. Appl. Intell. (2020). https://doi.org/10.1007/s10489-020-01657-9
Yu, X., Gen, M.: Introduction to Evolutionary Algorithms. Springer, London (2010)
Zamani, M.S., Mehdipour, F., Meybodi, M.R.: Implementation of cellular learning automata on reconfigurable computing systems. In: CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No. 03CH37436), pp. 1139–1142. IEEE (2003)
Zanganeh, S., Meybodi, M.R., Sedehi, M.H.: Continuous CLA-EC. In: 2010 Fourth International Conference on Genetic and Evolutionary Computing, pp. 186–189. IEEE (2010)
Zarei, B., Meybodi, M.R.: Improving learning ability of learning automata using chaos theory. J. Supercomputing (2020). https://doi.org/10.1007/s11227-020-03293-z
Zhang, J., Xu, L., Li, J., Kang, Q., Zhou, M.: Integrating particle swarm optimization with learning automata to solve optimization problems in noisy environment. In: 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1432–1437. IEEE (2014)
Zhang, J., Xu, L., Ma, J., Zhou, M.: A learning automata-based particle swarm optimization algorithm for noisy environment. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 141–147 (2015)
Zhang, F., Wang, X., Li, P., Zhang, L.: An energy aware cellular learning automata based routing algorithm for opportunistic networks. Int. J. Grid Distrib. Comput. 9, 255–272 (2016). https://doi.org/10.14257/ijgdc.2016.9.2.22
Zhang, J., Zhu, X., Zhou, M.: Learning Automata-based particle swarm optimizer. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–6. IEEE (2018)
Zhao, Y., Jiang, W., Li, S., Ma, Y., Su, G., Lin, X.: A cellular learning automata based algorithm for detecting community structure in complex networks. Neurocomputing 151, 1216–1226 (2015). https://doi.org/10.1016/j.neucom.2014.04.087
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Kazemi Kordestani, J., Razapoor Mirsaleh, M., Rezvanian, A., Meybodi, M.R. (2021). An Introduction to Learning Automata and Optimization. In: Kazemi Kordestani, J., Mirsaleh, M.R., Rezvanian, A., Meybodi, M.R. (eds) Advances in Learning Automata and Intelligent Optimization. Intelligent Systems Reference Library, vol 208. Springer, Cham. https://doi.org/10.1007/978-3-030-76291-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-76291-9_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-76290-2
Online ISBN: 978-3-030-76291-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)