Cellular automata, learning automata, and cellular learning automata for optimization. (English) Zbl 1485.68218
Kordestani, Javidan Kazemi (ed.) et al., Advances in learning automata and intelligent optimization. Cham: Springer. Intell. Syst. Ref. Libr. 208, 75-125 (2021).
Summary: Since many real problems have several limitations and constraints for different environments, no standard optimization algorithms could work successfully for all kinds of problems. To enhance the abilities and improve the performance of a standard optimization algorithm for solving problems, several modifications or combinations with some techniques such as learning automata (LA), cellular automata (CA), and cellular learning automata (CLA) are presented by researchers. Thus, this chapter investigates new learning automata (LA) and cellular learning automata (CLA) models for solving optimization problems. Moreover, this chapter provides a summary of hybrid LA models for optimization problems from 2015 to 2021.
For the entire collection see [Zbl 1470.68021].
For the entire collection see [Zbl 1470.68021].
MSC:
| 68T05 | Learning and adaptive systems in artificial intelligence |
| 68Q45 | Formal languages and automata |
| 68Q80 | Cellular automata (computational aspects) |
| 90C59 | Approximation methods and heuristics in mathematical programming |
References:
| [1] | Bhattacharjee, K., Naskar, N., Roy, S., Das, S.: A survey of cellular automata: types, dynamics, non-uniformity and applications. Nat. Comput. (2018) https://doi.org/10.1007/s11047-018-9696-8 · Zbl 1530.68158 |
| [2] | Gasior, J., Seredynski, F., Hoffmann, R.: Towards self-organizing sensor networks: game-theoretic e-learning automata-based approach. In: Cellular Automata: Proceedings of the 13th International Conference on Cellular Automata for Research and Industry, ACRI 2018, Como, Italy, 17-21 September 2018, p. 125. Springer (2018) · Zbl 1515.68083 |
| [3] | Gheisari, S.: VLA-CR: a variable action-set learning automata-based cognitive routing protocol for IoT. Comput. Commun. 164, 162-176 (2020). https://doi.org/10.1016/j.comcom.2020.10.015 · doi:10.1016/j.comcom.2020.10.015 |
| [4] | Gheisari, S., Meybodi, M.R.: A new reasoning and learning model for Cognitive Wireless Sensor Networks based on Bayesian networks and learning automata cooperation. Comput. Netw. 124, 11-26 (2017) · doi:10.1016/j.comnet.2017.05.031 |
| [5] | Gheisari, S., Meybodi, M.R., Dehghan, M., Ebadzadeh, M.M.: Bayesian network structure training based on a game of learning automata. Int. J. Mach. Learn. Cybern. 8, 1093-1105 (2017) · doi:10.1007/s13042-015-0476-9 |
| [6] | Gheisari, S., Meybodi, M.R., Dehghan, M., Ebadzadeh, M.M.: BNC-VLA: Bayesian network structure learning using a team of variable-action set learning automata. Appl. Intell. 45, 135-151 (2016) · doi:10.1007/s10489-015-0743-1 |
| [7] | Guo, Y., Ge, H., Wang, F., Huang, Y., Li, S.: Function optimization via a continuous action-set reinforcement learning automata model. In: Liang, Q., Mu, J., Wang, W., Zhang, B. (eds.) Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems, pp. 981-989. Springer, Heidelberg (2016) |
| [8] | Guo, Y., Hao, G., Shenghong, L.: A set of novel continuous action-set reinforcement learning automata models to optimize continuous functions. Appl. Intell. 46, 845-864 (2017) · doi:10.1007/s10489-016-0853-4 |
| [9] | Hadavi, N., Nordin, Md.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) |
| [10] | Hao, S., Zhang, H., Wang, J.: A learning automata based stable and energy-efficient routing algorithm for discrete energy harvesting mobile wireless sensor network. Wirel. Pers. Commun. 107, 437-469 (2019) · doi:10.1007/s11277-019-06284-3 |
| [11] | Hariri, A., Rastegar, R., Navi, K., Zamani, M.S., Meybodi, M.R.: Cellular learning automata based evolutionary computing (CLA-EC) for intrinsic hardware evolution. In: 2005 NASA/DoD Conference on Evolvable Hardware (EH 2005), pp. 294-297. IEEE (2005a) |
| [12] | 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 (2005b) |
| [13] | 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 · doi:10.1016/j.aeue.2015.05.010 |
| [14] | Hashemi, A.B., Meybodi, M.R.: Cellular PSO: a PSO for dynamic environments. In: Cai, Z. (ed.) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 422-433. Springer, Heidelberg (2009) |
| [15] | Hashemi, S.M., He, J.: LA-based approach for IoT security. J. Robot. Network. Artif. Life 3, 240-248 (2017). https://doi.org/10.2991/jrnal.2017.3.4.7 · doi:10.2991/jrnal.2017.3.4.7 |
| [16] | 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) |
| [17] | Jafarpour, B., Meybodi, M.R.: Recombinative CLA-EC. In: Proceedings of the International Conference on Tools with Artificial Intelligence, ICTAI, pp. 415-422. IEEE (2007) |
| [18] | Jahanshahi, M., Dehghan, M., Meybodi, M.R.: A cross-layer optimization framework for joint channel assignment and multicast routing in multi-channel multi-radio wireless mesh networks. Int. J. Comput. Math. 94, 1624-1652 (2017) · Zbl 1409.68033 · doi:10.1080/00207160.2016.1227431 |
| [19] | Jameii, S.M., Faez, K., Dehghan, M.: AMOF: adaptive multi-objective optimization framework for coverage and topology control in heterogeneous wireless sensor networks. Telecommun. Syst. 61, 515-530 (2016) · doi:10.1007/s11235-015-0009-6 |
| [20] | 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. Theor. Artif. Intell. 33, 1-26 (2021) · doi:10.1080/0952813X.2020.1721568 |
| [21] | Kazemitabar, S.J., Taghizadeh, N., Beigy, H.: A graph-theoretic approach toward autonomous skill acquisition in reinforcement learning. Evol. Syst. 9, 227-244 (2018) · doi:10.1007/s12530-017-9193-9 |
| [22] | Kennedy, J.: Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC 1999 (Cat. No. 99TH8406), pp. 1931-1938. IEEE (1999) |
| [23] | Khatamnejad, A., Meybodi, M.R.: A hybrid method for optimization (CLA-EC + extremal optimization). In: 13th Annual CSI Computer Conference of Iran, pp. 1-6 (2008) |
| [24] | 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) · doi:10.1016/j.jocs.2017.10.009 |
| [25] | 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 (2016) · doi:10.1142/S0217979216500429 |
| [26] | Khot, P.S., Naik, U.L.: Cellular automata-based optimised routing for secure data transmission in wireless sensor networks. J. Exp. Theor. Artif. Intell. 1-19 (2021). https://doi.org/10.1080/0952813X.2021.1882002 |
| [27] | Kiink, T., Vesterstroem, J.S., Riget, J.: Particle swarm optimization with spatial particle extension. In: IEEE Congress on Evolutionary Computation, pp. 1474-1479 (2002) |
| [28] | 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) · doi:10.1007/s10489-017-0963-7 |
| [29] | 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) · doi:10.1016/j.swevo.2018.09.002 |
| [30] | Kordestani, J.K., Rezvanian, A., Meybodi, M.R.: An efficient oscillating inertia weight of particle swarm optimisation for tracking optima in dynamic environments. J. Exp. Theor. Artif. Intell. 28, 137-149 (2016). https://doi.org/10.1080/0952813X.2015.1020521 · doi:10.1080/0952813X.2015.1020521 |
| [31] | Kordestani, J.K., Rezvanian, A., Meybodi, M.R.: CDEPSO: a bi-population hybrid approach for dynamic optimization problems. Appl. Intell. 40, 682-694 (2014). https://doi.org/10.1007/s10489-013-0483-z · doi:10.1007/s10489-013-0483-z |
| [32] | Kumar, S., Kumar, V., Kaiwartya, O., Dohare, U., Kumar, N., Lloret, J.: Towards green communication in wireless sensor network: GA enabled distributed zone approach. Ad Hoc Netw. 93, 101903 (2019) · doi:10.1016/j.adhoc.2019.101903 |
| [33] | Narendra, K.S., Thathachar, M.A.L.: Learning Automata: An Introduction. Prentice-Hall, Englewood Cliffs (1989) |
| [34] | Li, M., Miao, C., Leung, C.: A coral reef algorithm based on learning automata for the coverage control problem of heterogeneous directional sensor networks. Sensors 15, 30617-30635 (2015) · doi:10.3390/s151229820 |
| [35] | Li, W., Özcan, E., John, R.: A learning automata-based multiobjective hyper-heuristic. IEEE Trans. Evol. Comput. 23, 59-73 (2019). https://doi.org/10.1109/TEVC.2017.2785346 · doi:10.1109/TEVC.2017.2785346 |
| [36] | Lin, Y., Wang, L., Zhong, Y., Zhang, C.: Control scaling factor of cuckoo search algorithm using learning automata. Int. J. Comput. Sci. Math. 7, 476-484 (2016). https://doi.org/10.1504/IJCSM.2016.080088 · Zbl 1453.90203 · doi:10.1504/IJCSM.2016.080088 |
| [37] | Lin, Y., Wang, X., Hao, F., Wang, L., Zhang, L., Zhao, R.: An on-demand coverage based self-deployment algorithm for big data perception in mobile sensing networks. Future Gener. Comput. Syst. 82, 220-234 (2018) · doi:10.1016/j.future.2018.01.007 |
| [38] | Liu, W., Xu, Y., Qi, N., Yao, K., Zhang, Y., He, W.: Joint computation offloading and resource allocation in UAV swarms with multi-access edge computing. In: 2020 International Conference on Wireless Communications and Signal Processing (WCSP), pp. 280-285 (2020) |
| [39] | Mahdaviani, M., Kordestani, J.K., Rezvanian, A., Meybodi, M.R.: LADE: learning automata based differential evolution. Int. J. Artif. Intell. Tools 24, 1550023 (2015) · doi:10.1142/S0218213015500232 |
| [40] | Zhao, H., Zhang, C.: An online-learning-based evolutionary many-objective algorithm. Inf. Sci. 509, 1-21 (2020). https://doi.org/10.1016/j.ins.2019.08.069 · Zbl 1456.90153 · doi:10.1016/j.ins.2019.08.069 |
| [41] | 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 · doi:10.1007/s10489-011-0292-1 |
| [42] | Vafashoar, R., Meybodi, M.R.: Cellular learning automata based bare bones PSO with maximum likelihood rotated mutations. Swarm Evol. Comput. 44, 680-694 (2019c). https://doi.org/10.1016/j.swevo.2018.08.016 |
| [43] | Talabeigi, M., Forsati, R., Meybodi, M.R.: A hybrid web recommender system based on cellular learning automata. In: 2010 IEEE International Conference on Granular Computing, pp. 453-458. IEEE (2010) |
| [44] | Suma, R., Premasudha, B.G., Ram, V.R.: A novel machine learning-based attacker detection system to secure location aided routing in MANETs. Int. J. Network. Virtual Organ. 22, 17-41 (2020). https://doi.org/10.1504/IJNVO.2020.104968 |
| [45] | Rezvanian, A., Moradabadi, B., Ghavipour, M., Daliri Khomami, M.M., Meybodi, M.R.: Learning Automata Approach for Social Networks. Springer, Cham (2019a) |
| [46] | Khaksarmanshad, M., Meybodi, M.R.: Designing optimization algorithms based on CLA-EC for dynamic environments. In: The 4th Iran Data Mining Conference (IDMC 2010), pp. 1-6 (2010) |
| [47] | Khaksar Manshad, M., Meybodi, M.R., Salajegheh, A.: A new irregular cellular learning automata-based evolutionary computation for time series link prediction in social networks. Appl. Intell. (2020). https://doi.org/10.1007/s10489-020-01685-5 · doi:10.1007/s10489-020-01685-5 |
| [48] | Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995 - International Conference on Neural Networks, pp. 1942-1948. IEEE (1995) |
| [49] | 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. (2017). https://doi.org/10.1016/j.jocs.2017.08.005 |
| [50] | 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) · doi:10.3233/JIFS-17199 |
| [51] | 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 · doi:10.1016/j.asoc.2016.12.006 |
| [52] | Akbari Torkestani, J.: A learning approach to the bandwidth multicolouring problem. J. Exp. Theor. Artif. Intell. 28, 499-527 (2016) · doi:10.1080/0952813X.2015.1015218 |
| [53] | 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) |
| [54] | Alirezanejad, M., Enayatifar, R., Motameni, H., Nematzadeh, H.: GSA-LA: gravitational search algorithm based on learning automata. J. Exp. Theor. Artif. Intell. 33, 109-125 (2021) · doi:10.1080/0952813X.2020.1725650 |
| [55] | Alirezanejad, M., Enayatifar, R., Motameni, H., Nematzadeh, H.: GSA-LA: gravitational search algorithm based on learning automata. J. Exp. Theor. Artif. Intell. 1-17 (2020). https://doi.org/10.1080/0952813X.2020.1725650 |
| [56] | Anari, B., Torkestani, J.A., Rahmani, A.M.: Automatic data clustering using continuous action-set learning automata and its application in segmentation of images. Appl. Soft Comput. 51, 253-265 (2017) · doi:10.1016/j.asoc.2016.12.007 |
| [57] | Arora, S., Anand, P.: Learning automata-based butterfly optimization algorithm for engineering design problems. Int. J. Comput. Mater. Sci. Eng. 7, 1850021 (2018) |
| [58] | Babichenko, Y., Tamuz, O.: Graphical potential games. J. Econ. Theory 163, 889-899 (2016). https://doi.org/10.1016/j.jet.2016.03.010 · Zbl 1369.91033 · doi:10.1016/j.jet.2016.03.010 |
| [59] | Bak, P., Sneppen, K.: Punctuated equilibrium and criticality in a simple model of evolution. Phys. Rev. Lett. 71, 4083-4086 (1993). https://doi.org/10.1103/PhysRevLett.71.4083 · doi:10.1103/PhysRevLett.71.4083 |
| [60] | Bayessa, G.A., Sah Tyagi, S.K., Parashar, V., Gao, M., Shi, J.: Novel protected sub-frame selection based interference mitigation and resource assignment in heterogeneous multi-cloud radio access networks. Sustain. Comput. Inform. Syst. 20, 165-173 (2018). https://doi.org/10.1016/j.suscom.2018.02.009 · doi:10.1016/j.suscom.2018.02.009 |
| [61] | Beigy, H., Meybodi, M.R.: Asynchronous cellular learning automata. Automatica 44, 1350-1357 (2008) · Zbl 1283.68225 · doi:10.1016/j.automatica.2007.09.018 |
| [62] | 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 · Zbl 1098.68083 · doi:10.1142/S0219525904000202 |
| [63] | Beigy, H., Meybodi, M.R.: An iterative stochastic algorithm based on distributed learning automata for finding the stochastic shortest path in stochastic graphs. J. Supercomput. 76, 5540-5562 (2020). https://doi.org/10.1007/s11227-019-03085-0 · doi:10.1007/s11227-019-03085-0 |
| [64] | Beigy, H., Meybodi, M.R.R.: Cellular learning automata with multiple learning automata in each cell and its applications. IEEE Trans. Syst. Man Cybern. Part B (cybern.) 40, 54-65 (2010). https://doi.org/10.1109/TSMCB.2009.2030786 · doi:10.1109/TSMCB.2009.2030786 |
| [65] | Ben-Zvi, T.: Learning automata decision analysis for sensor placement. J. Oper. Res. Soc. 69, 1396-1405 (2018) · doi:10.1080/01605682.2017.1398205 |
| [66] | Betka, A., Terki, N., Toumi, A., Dahmani, H.: Grey wolf optimizer-based learning automata for solving block matching problem. Sig. Image Video Process. 14, 285-293 (2020) · doi:10.1007/s11760-019-01554-w |
| [67] | Blackwell, T.: Particle swarm optimization in dynamic environments, pp. 29-49 (2007) |
| [68] | Boettcher, S., Percus, A.G.: Optimization with extremal dynamics. Complexity 8, 57-62 (2002). https://doi.org/10.1002/cplx.10072 · doi:10.1002/cplx.10072 |
| [69] | Bohlool, M., Meybodi, M.R.: Edge detection using open and asynchronous cellular learning automata. In: 4th Iranian Conference on Machine Vision and Image Processing, pp. 1-6 (2007) |
| [70] | Bouhmala, N.: A multilevel learning automata for MAX-SAT. Int. J. Mach. Learn. Cyber. 6, 911-921 (2015). https://doi.org/10.1007/s13042-015-0355-4 · doi:10.1007/s13042-015-0355-4 |
| [71] | Bouhmala, N., Oseland, M., Brådland, Ø.: WalkSAT based-learning automata for MAX-SAT. In: International Conference on Soft Computing-MENDEL, pp. 98-110. Springer (2016) |
| [72] | Burkowski, F.J.: Shuffle crossover and mutual information. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC 1999 (Cat. No. 99TH8406), pp. 1574-1580. IEEE (1999) |
| [73] | Cao, H., Cai, J.: Distributed opportunistic spectrum access in an unknown and dynamic environment: a stochastic learning approach. IEEE Trans. Veh. Technol. 67, 4454-4465 (2018) · doi:10.1109/TVT.2018.2789344 |
| [74] | Chowdhury, A., Rakshit, P., Konar, A., Nagar, A.K.: A meta-heuristic approach to predict protein-protein interaction network. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 2137-2144. IEEE (2016) |
| [75] | Cook, M.: Universality in elementary cellular automata. Complex Syst. 15, 1-40 (2004) · Zbl 1167.68387 |
| [76] | Copeland, B.: The church-turing thesis (1997) |
| [77] | 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 · doi:10.1109/TCYB.2015.2503433 |
| [78] | 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) |
| [79] | 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) |
| [80] | Díaz-Cortés, M.-A., Cuevas, E., Rojas, R.: Multi-threshold segmentation using learning automata. In: Engineering Applications of Soft Computing, pp. 101-127. Springer (2017) |
| [81] | Duarte, P.B.F., Fadlullah, ZMd., Vasilakos, A.V., Kato, N.: On the partially overlapped channel assignment on wireless mesh network backbone: a game theoretic approach. IEEE J. Sel. Areas Commun. 30, 119-127 (2012). https://doi.org/10.1109/JSAC.2012.120111 · doi:10.1109/JSAC.2012.120111 |
| [82] | Eberhart, R.C., Shi, Y.: Tracking and optimizing dynamic systems with particle swarms. In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546), pp. 94-100 (2001) |
| [83] | 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 · Zbl 1344.68186 · doi:10.1016/j.cnsns.2013.04.028 |
| [84] | Esnaashari, M., Meybodi, M.R.: Dynamic point coverage problem in wireless sensor networks: a cellular learning automata approach. Ad Hoc Sens. Wirel. Netw. 10, 193-234 (2010) · Zbl 1208.68092 |
| [85] | Esnaashari, M., Meybodi, M.R.: A cellular learning automata based clustering algorithm for wireless sensor networks. Sens. Lett. 6, 723-735 (2008) · doi:10.1166/sl.2008.m146 |
| [86] | 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) · Zbl 1219.68042 · doi:10.1016/j.jpdc.2010.10.015 |
| [87] | Fakhrmoosavy, S.H., Setayeshi, S., Sharifi, A.: A modified brain emotional learning model for earthquake magnitude and fear prediction. Eng. Comput. 34, 261-276 (2018). https://doi.org/10.1007/s00366-017-0538-6 · doi:10.1007/s00366-017-0538-6 |
| [88] | 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) |
| [89] | 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 · doi:10.1016/j.engappai.2017.01.004 |
| [90] | Manshad, M.K., Manshad, A.K., Meybodi, M.R.: Memory/search RCLA-EC: a CLA-EC for moving parabola problem. In: 2011 6th International Conference on Computer Sciences and Convergence Information Technology (ICCIT), pp. 1-6 (2011) |
| [91] | Masoodifar, B., Meybodi, M.R., Hashemi, M.: Cooperative CLA-EC. In: 12th Annual CSI Computer Conference of Iran, pp. 558-559 (2007) |
| [92] | Mendes, R., Kennedy, J., Neves, J.: Watch thy neighbor or how the swarm can learn from its environment. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, SIS 2003 (Cat. No. 03EX706), pp. 88-94. IEEE (2003) |
| [93] | Mirsaleh, M.R., Meybodi, M.R.: Assignment of cells to switches in cellular mobile network: a learning automata-based memetic algorithm. Appl. Intell. 48, 3231-3247 (2018) · doi:10.1007/s10489-018-1136-z |
| [94] | Mirsaleh, M.R., Meybodi, M.R.: A Michigan memetic algorithm for solving the community detection problem in complex network. Neurocomputing 214, 535-545 (2016a) |
| [95] | Mirsaleh, M.R., Meybodi, M.R.: A new memetic algorithm based on cellular learning automata for solving the vertex coloring problem. Memet. Comput. 8, 211-222 (2016b) |
| [96] | Mohajer, A., Bavaghar, M., Farrokhi, H.: Reliability and mobility load balancing in next generation self-organized networks: using stochastic learning automata. Wirel. Pers. Commun. 114, 2389-2415 (2020a) |
| [97] | Mohajer, A., Bavaghar, M., Farrokhi, H.: Mobility-aware load balancing for reliable self-organization networks: multi-agent deep reinforcement learning. Reliab. Eng. Syst. Saf. 202, 107056 (2020b) |
| [98] | Moradabadi, B., Meybodi, M.R.: Link prediction based on temporal similarity metrics using continuous action set learning automata. Phys. A 460, 361-373 (2016) · Zbl 1400.91482 · doi:10.1016/j.physa.2016.03.102 |
| [99] | Moradabadi, B., Meybodi, M.R.: Wavefront cellular learning automata. Chaos 28, 021101 (2018). https://doi.org/10.1063/1.5017852 · Zbl 1390.68450 · doi:10.1063/1.5017852 |
| [100] | Moradabadi, B., Meybodi, M.R.: Link prediction in fuzzy social networks using distributed learning automata. Appl. Intell. 47, 837-849 (2017) · doi:10.1007/s10489-017-0933-0 |
| [101] | 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 · doi:10.1109/TSMC.2016.2569464 |
| [102] | 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) |
| [103] | Mozafari, M., Alizadeh, R.: A cellular learning automata model of investment behavior in the stock market. Neurocomputing 122, 470-479 (2013). https://doi.org/10.1016/j.neucom.2013.06.002 · doi:10.1016/j.neucom.2013.06.002 |
| [104] | 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 · doi:10.1016/j.jocs.2015.08.002 |
| [105] | Nabizadeh, S., Faez, K., Tavassoli, S., Rezvanian, A.: A novel method for multi-level image thresholding using Particle Swarm Optimization algorithms. In: ICCET 2010 - 2010 Proceedings of the International Conference on Computer Engineering and Technology, pp. V4-271-V4-275 (2010) |
| [106] | Nouri, E.: An unequal clustering-based topology control algorithm in wireless sensor networks using learning automata. In: Fundamental Research in Electrical Engineering, pp. 55-68. Springer (2019) |
| [107] | Ortiz, L.E.: Graphical potential games (2015) |
| [108] | Packard, N.H., Wolfram, S.: Two-dimensional cellular automata. J. Stat. Phys. 38, 901-946 (1985). https://doi.org/10.1007/BF01010423 · Zbl 0625.68038 · doi:10.1007/BF01010423 |
| [109] | Peer, E.S., van den Bergh, F., Engelbrecht, A.P.: Using neighbourhoods with the guaranteed convergence PSO. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, SIS 2003 (Cat. No. 03EX706), pp. 235-242. IEEE (2003) |
| [110] | Raharya, N., Hardjawana, W., Al-Khatib, O., Vucetic, B.: Multi-BS association and pilot allocation via pursuit learning. In: 2020 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1-6 (2020a) |
| [111] | Raharya, N., Hardjawana, W., Al-Khatib, O., Vucetic, B.: Pursuit learning-based joint pilot allocation and multi-base station association in a distributed massive MIMO network. IEEE Access 8, 58898-58911 (2020b). https://doi.org/10.1109/ACCESS.2020.2982974 |
| [112] | Rakshit, P.: Improved differential evolution for noisy optimization. Swarm Evol. Comput. 52, 100628 (2020). https://doi.org/10.1016/j.swevo.2019.100628 · Zbl 1456.90149 · doi:10.1016/j.swevo.2019.100628 |
| [113] | Rakshit, P., Konar, A.: Realization of learning induced self-adaptive sampling in noisy optimization. Appl. Soft Comput. 69, 288-315 (2018). https://doi.org/10.1016/j.asoc.2018.04.052 · doi:10.1016/j.asoc.2018.04.052 |
| [114] | Rakshit, P., Konar, A., Nagar, A.K.: Modified selection and search in learning automata based artificial bee colony in noisy environment. In: 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 3173-3180 (2019) |
| [115] | Rakshit, P., Konar, A., Nagar, A.K.: Learning automata induced artificial bee colony for noisy optimization. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 984-991 (2017) |
| [116] | Rastegar, R., Meybodi, M.R.: A new evolutionary computing model based on cellular learning automata. In: 2004 IEEE Conference on Cybernetics and Intelligent Systems, pp. 433-438. IEEE (2004) |
| [117] | Rastegar, R., Meybodi, M.R., Hariri, A.: A new fine-grained evolutionary algorithm based on cellular learning automata. Int. J. Hybrid Intell. Syst. 3, 83-98 (2006). https://doi.org/10.3233/HIS-2006-3202 · Zbl 1101.68568 · doi:10.3233/HIS-2006-3202 |
| [118] | 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) |
| [119] | Rauniyar, A., Yazidi, A., Engelstad, P., Østerbo, O.N.: A reinforcement learning based game theoretic approach for distributed power control in downlink NOMA. In: 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA), pp. 1-10 (2020) |
| [120] | Rezapoor Mirsaleh, M., Meybodi, M.R.: Balancing exploration and exploitation in memetic algorithms: a learning automata approach. Comput. Intell. 34, 282-309 (2018) · doi:10.1111/coin.12148 |
| [121] | Rezvanian, A., Meybodi, M.R.: Finding minimum vertex covering in stochastic graphs: a learning automata approach. Cybern. Syst. 46, 698-727 (2015) · Zbl 1323.05113 · doi:10.1080/01969722.2015.1082407 |
| [122] | 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 (2019b) |
| [123] | 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 (2019c) |
| [124] | Rezvanian, A., Moradabadi, B., Ghavipour, M., Daliri Khomami, M.M., Meybodi, M.R.: Wavefront cellular learning automata: a new learning paradigm. In: Rezvanian, A., Moradabadi, B., Ghavipour, M., Daliri Khomami, M.M., Meybodi, M.R. (eds.) Learning Automata Approach for Social Networks, pp. 51-74. Springer, Cham (2019d) |
| [125] | Rezvanian, A., Saghiri, A.M., Vahidipour, S.M., Esnaashari, M., Meybodi, M.R.: Recent Advances in Learning Automata. Springer, Cham (2018a) · Zbl 1402.68012 |
| [126] | Rezvanian, A., Saghiri, A.M., Vahidipour, S.M., Esnaashari, M., Meybodi, M.R.: Learning automata for complex social networks. In: Recent Advances in Learning Automata, pp. 279-334 (2018b) · Zbl 1402.68012 |
| [127] | 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 (2018c) · Zbl 1402.68012 |
| [128] | 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 (2019d) |
| [129] | 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 · doi:10.1109/MITS.2019.2919523 |
| [130] | Saghiri, A.M., Meybodi, M.R.: Open asynchronous dynamic cellular learning automata and its application to allocation hub location problem. Know.-Based Syst. 139, 149-169 (2018a). https://doi.org/10.1016/j.knosys.2017.10.021 |
| [131] | 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 Evolvable Mach. 18, 313-349 (2017). https://doi.org/10.1007/s10710-017-9299-7 · doi:10.1007/s10710-017-9299-7 |
| [132] | 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 (2018b). https://doi.org/10.1007/s10489-017-0946-8 |
| [133] | Salehi, F., Majidi, M.-H., Neda, N.: Channel estimation based on learning automata for OFDM systems. Int. J. Commun. Syst. 31, e3707 (2018) · doi:10.1002/dac.3707 |
| [134] | Saritha, V., Krishna, P.V., Misra, S., Obaidat, M.S.: Learning automata based optimized multipath routingusing leapfrog algorithm for VANETs. In: 2017 IEEE International Conference on Communications (ICC), pp. 1-5 (2017) |
| [135] | Sayyadi Shahraki, N., Zahiri, S.H.: Multi-objective learning automata for design and optimization a two-stage CMOS operational amplifier. Iran. J. Electr. Electron. Eng. 16, 201-214 (2020a). https://doi.org/10.22068/IJEEE.16.2.201 |
| [136] | Sayyadi Shahraki, N., Zahiri, S.H.: An improved multi-objective learning automata and its application in VLSI circuit design. Memet. Comput. 12, 115-128 (2020b). https://doi.org/10.1007/s12293-020-00303-8 |
| [137] | Sayyadi Shahraki, N., Zahiri, S.H.: DRLA: dimensionality ranking in learning automata and its application on designing analog active filters. Knowl.-Based Syst. 219, 106886 (2021). https://doi.org/10.1016/j.knosys.2021.106886 · doi:10.1016/j.knosys.2021.106886 |
| [138] | Sinaie, S., Ghanizadeh, A., Majd, E.M., Shamsuddin, S.M.: A hybrid edge detection method based on fuzzy set theory and cellular learning automata. In: 2009 International Conference on Computational Science and Its Applications, pp. 208-214. IEEE (2009) |
| [139] | Singh, S., Dwivedi, A.K., Sharma, A.K., Mehra, P.S.: Learning automata based heuristics for target Q-coverage. In: 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), pp. 170-173. IEEE (2020) |
| [140] | 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 |
| [141] | 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 (2012) |
| [142] | Storn, R.M., Price, K.V.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341-359 (1997). https://doi.org/10.1023/A:1008202821328 · Zbl 0888.90135 |
| [143] | Su, X., Han, W., Wu, Y., Zhang, Y., Liu, J.: A proactive robust scheduling method for aircraft carrier flight deck operations with stochastic durations. Complexity 2018, e6932985 (2018). https://doi.org/10.1155/2018/6932985 |
| [144] | Subha, C.P., Malarkkan, S.: Optimisation of energy efficient cellular learning automata algorithm for heterogeneous wireless sensor networks. Int. J. Network. Virtual Organ. 17, 170-183 (2017) · doi:10.1504/IJNVO.2017.085526 |
| [145] | Thathachar, M.A.L., Sastry, P.S.: Varieties of learning automata: an overview. IEEE Trans. Syst. Man Cybern. B Cybern. 32, 711-722 (2002). https://doi.org/10.1109/TSMCB.2002.1049606 · doi:10.1109/TSMCB.2002.1049606 |
| [146] | 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 · Zbl 1391.90095 · doi:10.1016/j.cor.2017.08.002 |
| [147] | 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 · doi:10.1016/j.ygeno.2016.05.001 |
| [148] | 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 |
| [149] | 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) · doi:10.1016/j.asoc.2019.106009 |
| [150] | 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 · doi:10.1007/s10489-017-1039-4 |
| [151] | Vafashoar, R., Meybodi, M.R.: Multi swarm bare bones particle swarm optimization with distribution adaption. Appl. Soft Comput. 47, 534-552 (2016) · doi:10.1016/j.asoc.2016.06.028 |
| [152] | Vafashoar, R., Morshedlou, H., Rezvanian, A., Meybodi, M.R.: Cellular Learning Automata: Theory and Applications. Springer, Cham (2021a) |
| [153] | Vafashoar, R., Morshedlou, H., Rezvanian, A., Meybodi, M.R.: Applications of cellular learning automata and reinforcement learning in global optimization. In: Cellular Learning Automata: Theory and Applications, pp. 157-224. Springer (2021b) |
| [154] | Vafashoar, R., Morshedlou, H., Rezvanian, A., Meybodi, M.R.: Learning from multiple reinforcements in cellular learning automata. In: Cellular Learning Automata: Theory and Applications, pp. 111-156. Springer (2021c) |
| [155] | Vafashoar, R., Morshedlou, H., Rezvanian, A., Meybodi, M.R.: Applications of multi-reinforcement cellular learning automata in channel assignment. In: Cellular Learning Automata: Theory and Applications, pp. 225-254. Springer (2021d) |
| [156] | Vafashoar, R., Morshedlou, H., Rezvanian, A., Meybodi, M.R.: Cellular learning automata: a bibliometric analysis. In: Cellular Learning Automata: Theory and Applications, pp. 83-109. Springer (2021e) |
| [157] | 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 |
| [158] | 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 (2017a) · Zbl 1377.68153 |
| [159] | Vahidipour, S.M., Meybodi, M.R., Esnaashari, M.: Cellular adaptive Petri net based on learning automata and its application to the vertex coloring problem. Discrete Event Dyn. Syst. 27, 609-640 (2017b). https://doi.org/10.1007/s10626-017-0251-z · Zbl 1386.93196 |
| [160] | Velusamy, G., Lent, R.: Evaluating an adaptive web traffic routing method for the cloud. In: 2019 IEEE ComSoc International Communications Quality and Reliability Workshop (CQR), pp. 1-6. IEEE (2019) |
| [161] | Velusamy, G., Lent, R.: Dynamic cost-aware routing of web requests. Future Internet 10, 57 (2018). https://doi.org/10.3390/fi10070057 · doi:10.3390/fi10070057 |
| [162] | Wauters, T., Verbeeck, K., De Causmaecker, P., Vanden Berghe, G.: A learning-based optimization approach to multi-project scheduling. J. Sched. 18, 61-74 (2015). https://doi.org/10.1007/s10951-014-0401-1 · Zbl 1310.90058 · doi:10.1007/s10951-014-0401-1 |
| [163] | Wolfram, S.: Theory and applications of cellular automata. World Scientific Publication (1986) · Zbl 0609.68043 |
| [164] | Wolfram, S.: Cellular automata as simple self-organizing systems. Caltech preprint CALT-68-938 5 (1982) |
| [165] | Xiao, G., Liu, H., Guo, W., Wang, L.: A hybrid training method of convolution neural networks using adaptive cooperative particle swarm optimiser. Int. J. Wirel. Mob. Comput. 16, 18-26 (2019). https://doi.org/10.1504/IJWMC.2019.097418 · doi:10.1504/IJWMC.2019.097418 |
| [166] | Xu, Y., Ma, L., Shi, M.: Adaptive brain storm optimization based on learning automata. In: Pan, L., Liang, J., Qu, B. (eds.) Bio-Inspired Computing: Theories and Applications, pp. 98-108. Springer, Singapore (2020) |
| [167] | Yang, Z., Liu, Y., Chen, Y.: Distributed reinforcement learning for NOMA-enabled mobile edge computing. In: 2020 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1-6 (2020a) |
| [168] | Yang, Z., Liu, Y., Chen, Y., Al-Dhahir, N.: Cache-aided NOMA mobile edge computing: a reinforcement learning approach. IEEE Trans. Wirel. Commun. 19, 6899-6915 (2020b). https://doi.org/10.1109/TWC.2020.3006922 |
| [169] | Yazidi, A., Bouhmala, N., Goodwin, M.: A team of pursuit learning automata for solving deterministic optimization problems. Appl. Intell. 50, 2916-2931 (2020) · doi:10.1007/s10489-020-01657-9 |
| [170] | Yazidi, A., Oommen, B.J.: The theory and applications of the stochastic point location problem. In: 2017 International Conference on New Trends in Computing Sciences (ICTCS). pp. 333-341 (2017) |
| [171] | Yazidi, A., Oommen, B.J.: Solving stochastic root-finding with adaptive d-ary search. In: 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp. 1-8 (2015) |
| [172] | Zhang, D., Du, J., Zhang, T., Yang, P., Fan, H.: New algorithm of QoS constrained routing for node energy optimization of edge computing. In: 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), pp. 482-490. IEEE (2020) |
| [173] | 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) |
| [174] | Zhang, Y., Liu, S., Han, L: Optimization of node deployment in wireless sensor networks based on learning automata. In: International Conference on Human Centered Computing, pp. 79-85. Springer (2018) |
| [175] | Zhao, H., Wang, J., Wang, Q., Liu, F.: Queue-based and learning-based dynamic resources allocation for virtual streaming media server cluster of multi-version VoD system. Multimed. Tools Appl. 78, 21827-21852 (2019). https://doi.org/10.1007/s11042-019-7457-z · doi:10.1007/s11042-019-7457-z |
| [176] | 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 · doi:10.1016/j.neucom.2014.04.087 |
| [177] | Zhou, B., Song, Q., Zhao, Z., Liu, T.: A reinforcement learning scheme for the equilibrium of the in-vehicle route choice problem based on congestion game. Appl. Math. Comput. 371, 124895 (2020). https://doi.org/10.1016/j.amc.2019.124895 · Zbl 1433.90044 · doi:10.1016/j.amc.2019.124895 |
| [178] | Zhu, J., Gu, W., Lou, G., Wang, L., Xu, B., Wu, M., Sheng, W.: Learning automata-based methodology for optimal allocation of renewable distributed generation considering network reconfiguration. IEEE Access 5, 14275-14288 (2017). https://doi.org/10.1109/ACCESS.2017.2730850 · doi:10.1109/ACCESS.2017.2730850 |
| [179] | Zojaji, M., Meybodi, M.R.M., Mirzaie, K.: A rapid learning automata-based approach for generalized minimum spanning tree problem. J. Comb. Optim. 40, 636-659 (2020). https://doi.org/10.1007/s10878-020-00605-0 · Zbl 1466.90094 · doi:10.1007/s10878-020-00605-0 |
| [180] | Abbas, Z., Li, J., Yadav, N., Tariq, I.: Computational task offloading in mobile edge computing using learning automata. In: 2018 IEEE/CIC International Conference on Communications in China (ICCC), pp. 57-61. IEEE (2018) |
| [181] | Abbasi-ghalehtaki, R., Khotanlou, H., Esmaeilpour, M.: Fuzzy evolutionary cellular learning automata model for text summarization. Swarm Evol. Comput. 30, 11-26 (2016). https://doi.org/10.1016/j.swevo.2016.03.004 · doi:10.1016/j.swevo.2016.03.004 |
| [182] | Abedi Firouzjaee, H., Kordestani, J.K., Meybodi, M.R.: Cuckoo search with composite flight operator for numerical optimization problems and its application in tunnelling. Eng. Optim. 49, 597-616 (2017) · doi:10.1080/0305215X.2016.1206535 |
| [183] | Abin, A.A., Fotouhi, M., Kasaei, S.: Skin segmentation based on cellular learning automata. In: Proceedings of the 6th International Conference on Advances in Mobile Computing and Multimedia - MoMM 2008, Austria, p. 254. ACM (2008) |
| [184] | Abin, A.A., Fotouhi, M., Kasaei, S.: A new dynamic cellular learning automata-based skin detector. Multimed. Syst. 15, 309-323 (2009). https://doi.org/10.1007/s00530-009-0165-1 · doi:10.1007/s00530-009-0165-1 |
| [185] | Rezvanian, A., Saghiri, A.M., Vahidipour, S.M., Esnaashari, M., Meybodi, M.R.: Cellular learning automata, pp. 21-88 (2018c) · Zbl 1402.68012 |
| [186] | Mirsaleh, M.R., Meybodi, M.R.: A Michigan memetic algorithm for solving the vertex coloring problem. J. Comput. Sci. (2017). https://doi.org/10.1016/j.jocs.2017.10.005 |
| [187] | Lindsay, J., Gigivi, S.: A novel way of training a neural network with reinforcement learning and without back propagation. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1-6 (2020) |
| [188] | 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 · doi:10.1016/j.jocs.2017.09.008 |
| [189] | 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 · doi:10.1016/j.jocs.2017.08.012 |
| [190] | de Sousa, F.L., Ramos, F.M.: Function optimization using extremal dynamics. In: ICIPE 2002, Brazil, pp. 115-119 (2002) |
| [191] | Cota, L.P., Guimarães, F.G., Ribeiro, R.G., Meneghini, I.R., de Oliveira, F.B., Souza, M.J.F., Siarry, P.: An adaptive multi-objective algorithm based on decomposition and large neighborhood search for a green machine scheduling problem. Swarm Evol. Comput. 51, 100601 (2019). https://doi.org/10.1016/j.swevo.2019.100601 · doi:10.1016/j.swevo.2019.100601 |
| [192] | Beigy, H., Meybodi, M.R.: Open synchronous cellular learning automata. Adv. Complex Syst. 10, 527-556 (2007) · Zbl 1182.68128 · doi:10.1142/S0219525907001264 |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
