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A Hybrid Multi-objective Algorithm for Imbalanced Controller Placement in Software-Defined Networks

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Abstract

The Software-defined network (SDN) is a technique to design and manage a network that allows dynamic and programmatically functional configuration of network intending to improve the performance and monitor the system to make it comparable to the cloud computing than traditional types of network management. The SDNs comprise various switches and several controllers that lead the switches' data to a station or other controllers. One of the main challenges in the SDNs is seeking a fair number of controllers and optimal places for deploying them, known as controller placement problems. Depending on the network requirements, various criteria (e.g., installation cost, latency, load balancing, etc.) have been proposed to find the best places to install the controllers. The so-called problem that has attracted researchers' attention is formulated in the form of an optimization problem of multi-objective type. A novel multi-objective version of the Marine Predator Algorithm (MOMPA) was introduced in the current paper. The MOMPA was then hybridized with the Non-dominated Sorting Genetic Algorithm-II innovatively. Next, the proposed hybrid algorithm is discretized with mutation and crossover operators. Afterwards, the proposed hybrid discrete multi-objective algorithm was exploited to solve the controller placement problem. Henceforth, the proposed algorithm was applied to several real-world software-defined networks and was compared with some state-of-the-art algorithms regarding LC−S, LC−C, Imbalance, SP, and obtained Pareto members. The results of the comparisons demonstrated the superiority of the proposed controller placement algorithm.

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References

  1. Sen, S., Gupta, K.D., Ahsan, M.M.: Leveraging machine learning approach to setup software-defined network (SDN) controller rules during DDoS Attack. In: Proceedings of International Joint Conference on Computational Intelligence. Springer (2020)

  2. Messaoud, S., Bradai, A., Moulay, E.: Online GMM clustering and mini-batch gradient descent based optimization for industrial IoT 4.0. IEEE Trans. Ind. Inf. 16(2), 1427–1435 (2019)

    Article  Google Scholar 

  3. Masdari, M., Khezri, H.: Efficient VM migrations using forecasting techniques in cloud computing: a comprehensive review. Clust. Comput. 2020, 1–30 (2020)

    MATH  Google Scholar 

  4. Jafarian, T., et al.: A survey and classification of the security anomaly detection mechanisms in software defined networks. Clust. Comput. 24(2), 1235–1253 (2021)

    Article  Google Scholar 

  5. Jafarian, T., et al.: Security anomaly detection in software-defined networking based on a prediction technique. Int. J. Commun. Syst. 33(14), e4524 (2020)

    Article  Google Scholar 

  6. Eskca, E.B., et al.: Software defined networks security: an analysis of issues and solutions. Int. J. Sci. Eng. Res. 6(5), 1270–1275 (2015)

    Google Scholar 

  7. Wang, P., et al.: Data-driven software defined network attack detection: state-of-the-art and perspectives. Inf. Sci. 513, 65–83 (2020)

    Article  Google Scholar 

  8. Jafarian, T., et al.: SADM-SDNC: security anomaly detection and mitigation in software-defined networking using C-support vector classification. Computing 103(4), 641–673 (2021)

    Article  MathSciNet  Google Scholar 

  9. Sung, Y., et al.: FS-OpenSecurity: a taxonomic modeling of security threats in SDN for future sustainable computing. Sustainability 8(9), 919 (2016)

    Article  Google Scholar 

  10. Goto, Y., et al.: Queueing analysis of software defined network with realistic openflow–based switch model. Comput. Netw. 164, 106892 (2019)

    Article  Google Scholar 

  11. Priyadarsini, M., et al.: An adaptive load balancing scheme for software-defined network controllers. Comput. Netw. 164, 106918 (2019)

    Article  Google Scholar 

  12. Bhushan, K., Gupta, B.B.: Distributed denial of service (DDoS) attack mitigation in software defined network (SDN)-based cloud computing environment. J. Ambient. Intell. Humaniz. Comput. 10(5), 1985–1997 (2019)

    Article  Google Scholar 

  13. Indira, B., Valarmathi, K., Devaraj, D.: An approach to enhance packet classification performance of software-defined network using deep learning. Soft. Comput. 23(18), 8609–8619 (2019)

    Article  Google Scholar 

  14. Chakravarthy, V.D., Amutha, B.: A novel software-defined networking approach for load balancing in data center networks. Int. J. Commun. Syst. 35, e4213 (2019)

    Google Scholar 

  15. Lu, J., et al.: A survey of controller placement problem in software-defined networking. IEEE Access 7, 24290–24307 (2019)

    Article  Google Scholar 

  16. Guo, Z., et al.: STAR: preventing flow-table overflow in software-defined networks. Comput. Netw. 125, 15–25 (2017)

    Article  Google Scholar 

  17. Dvir, A., Haddad, Y., Zilberman, A.: The controller placement problem for wireless SDN. Wirel. Netw. 25(8), 4963–4978 (2019)

    Article  Google Scholar 

  18. Singh, A.K., Maurya, S., Srivastava, S.: Varna-based optimization: a novel method for capacitated controller placement problem in SDN. Front. Comput. Sci. 14(3), 143402 (2020)

    Article  Google Scholar 

  19. Iqbal, S., et al.: Minimize the delays in software defined network switch controller communication. Concurr. Comput. Pract. Exp. 2020, e5940 (2020)

    Google Scholar 

  20. Aoki, H., Shinomiya, N.: Controller placement problem to enhance performance in multi-domain SDN networks. In: Proceedings of the ICN (2016)

  21. Kanodia, K., et al.: HPSOSA: a hybrid approach in resilient controller placement in SDN. In: Proceedings of the 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). IEEE (2020)

  22. Moradi, A., Abdi Seyedkolaei, A., Hosseini, S.A.: Controller placement in software defined network using iterated local search. J. AI Data Min. 8(1), 55–65 (2020)

    Google Scholar 

  23. Yao, Z., Yan, Z.: A trust management framework for software-defined network applications. Concurr. Comput. Pract. Exp. 32(16), e4518 (2020)

    Article  Google Scholar 

  24. Aliyu, A.L., et al.: A trust management framework for software defined network (SDN) controller and network applications. Comput. Netw. 181, 107421 (2020)

    Article  Google Scholar 

  25. Singh, A.K., Kumar, N., Srivastava, S.: PSO and TLBO based reliable placement of controllers in SDN. IJ Comput. Netw. Inf. Secur. 2, 36–42 (2019)

    Google Scholar 

  26. Ruiz-Rivera, A., Chin, K.-W., Soh, S.: GreCo: An energy aware controller association algorithm for software defined networks. IEEE Commun. Lett. 19(4), 541–544 (2015)

    Article  Google Scholar 

  27. Singh, A.K., Srivastava, S.: A survey and classification of controller placement problem in SDN. Int. J. Netw. Manag. 28(3), e2018 (2018)

    Article  Google Scholar 

  28. Sallahi, A., St-Hilaire, M.: Expansion model for the controller placement problem in software defined networks. IEEE Commun. Lett. 21(2), 274–277 (2016)

    Article  Google Scholar 

  29. Sahoo, K.S., et al.: On the placement of controllers in software-defined-WAN using meta-heuristic approach. J. Syst. Softw. 145, 180–194 (2018)

    Article  Google Scholar 

  30. Kanodia, K., et al.: CCPGWO: a meta-heuristic strategy for link failure aware placement of controller in SDN. In: Proceedings of the 2020 International Conference on Inventive Computation Technologies (ICICT). IEEE (2020)

  31. Messaoud, S., et al.: A survey on machine learning in internet of things: algorithms, strategies, and applications. Intern. Things 12, 100314 (2020)

    Article  Google Scholar 

  32. Barshandeh, S., Piri, F., Sangani, S.R.: HMPA: an innovative hybrid multi-population algorithm based on artificial ecosystem-based and Harris Hawks optimization algorithms for engineering problems. Eng. Comput. 2020, 1–45 (2020)

    Google Scholar 

  33. Barshandeh, S., Haghzadeh, M.: A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems. Eng. Comput. 37, 1–44 (2020)

    Google Scholar 

  34. Barshandeh, S., et al.: A range‐free localization algorithm for IoT networks. Int J Intell Syst (2021) https://doi.org/10.1002/int.22524

  35. Masdari, M., Zangakani, M.: Efficient task and workflow scheduling in inter-cloud environments: challenges and opportunities. J. Supercomput. 76(1), 499–535 (2020)

    Article  Google Scholar 

  36. Mohammadzadeh, A., et al.: A hybrid multi-objective metaheuristic optimization algorithm for scientific workflow scheduling. Clust. Comput. 24(2), 1479–1503 (2021)

    Article  Google Scholar 

  37. Messaoud, S., et al.: Deep federated q-learning-based network slicing for industrial iot. IEEE Trans. Ind. Inf. 17(8), 5572–5582 (2020)

    Article  Google Scholar 

  38. Masdari, M., Khoshnevis, A.: A survey and classification of the workload forecasting methods in cloud computing. Clust. Comput. 23(4), 2399–2424 (2020)

    Article  Google Scholar 

  39. Mirjalili, S., et al.: Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst. Appl. 47, 106–119 (2016)

    Article  Google Scholar 

  40. Mirjalili, S., Jangir, P., Saremi, S.: Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Appl. Intell. 46(1), 79–95 (2017)

    Article  Google Scholar 

  41. Dhiman, G., Kumar, V.: Multi-objective spotted hyena optimizer: a multi-objective optimization algorithm for engineering problems. Knowl.-Based Syst. 150, 175–197 (2018)

    Article  Google Scholar 

  42. Xue, J., et al.: Brain storm optimization algorithm for multi-objective optimization problems. In: Proceedings of the International Conference in Swarm Intelligence. Springer (2012)

  43. Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)

    Article  Google Scholar 

  44. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm. TIK-Rep. 103, 9016 (2001)

    Google Scholar 

  45. Mirjalili, S.: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27(4), 1053–1073 (2016)

    Article  MathSciNet  Google Scholar 

  46. Fan, Y., Wang, L., Yuan, X.: Controller placements for latency minimization of both primary and backup paths in SDNs. Comput. Commun. 163, 35–50 (2020)

    Article  Google Scholar 

  47. Torkamani-Azar, S., Jahanshahi, M.: A new GSO based method for SDN controller placement. Comput. Commun. 163, 91 (2020)

    Article  Google Scholar 

  48. Lange, S., et al.: Heuristic approaches to the controller placement problem in large scale SDN networks. IEEE Trans. Netw. Serv. Manag. 12(1), 4–17 (2015)

    Article  Google Scholar 

  49. Jalili, A., Keshtgari, M., Akbari, R.: A new framework for reliable control placement in software-defined networks based on multi-criteria clustering approach. Soft Comput. 24, 1–20 (2020)

    Article  Google Scholar 

  50. Jalili, A., Keshtgari, M., Akbari, R.: Optimal controller placement in large scale software defined networks based on modified NSGA-II. Appl. Intell. 48(9), 2809–2823 (2018)

    Article  Google Scholar 

  51. Zhang, B., Wang, X., Huang, M.: Multi-objective optimization controller placement problem in Internet-oriented software defined network. Comput. Commun. 123, 24–35 (2018)

    Article  Google Scholar 

  52. Ramya, G., Manoharan, R.: Enhanced optimal placements of multi‑controllers in SDN

  53. Ahmadi, V., Khorramizadeh, M.: An adaptive heuristic for multi-objective controller placement in software-defined networks. Comput. Electr. Eng. 66, 204–228 (2018)

    Article  Google Scholar 

  54. Ran, J., Chen, Y., Zhao, S.: Controller placement optimization strategy based on multi-objective bat algorithm. In: Proceedings of the 2019 International Conference on Intelligent Computing, Automation and Systems (ICICAS). IEEE (2019)

  55. Liao, L., Leung, V.C.: Genetic algorithms with particle swarm optimization based mutation for distributed controller placement in SDNs. In: 2017 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). IEEE (2017)

  56. Faramarzi, A., et al.: Marine predators algorithm: A nature-inspired Metaheuristic. Expert Syst. Appl. 152, 113377 (2020)

    Article  Google Scholar 

  57. Humphries, N.E., et al.: Environmental context explains Lévy and Brownian movement patterns of marine predators. Nature 465(7301), 1066–1069 (2010)

    Article  Google Scholar 

  58. Mantegna, R.N.: Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Phys. Rev. E 49(5), 4677 (1994)

    Article  Google Scholar 

  59. Yang, X.-S.: Engineering Optimization: An Introduction with Metaheuristic Applications. John Wiley & Sons, Hoboken (2010)

    Book  Google Scholar 

  60. Filmalter, J.D., et al.: First descriptions of the behavior of silky sharks, Carcharhinus falciformis, around drifting fish aggregating devices in the Indian Ocean. Bull. Mar. Sci. 87(3), 325–337 (2011)

    Article  Google Scholar 

  61. Parouha, R.P., Das, K.N.: A memory based differential evolution algorithm for unconstrained optimization. Appl. Soft Comput. 38, 501–517 (2016)

    Article  Google Scholar 

  62. Shah, S.A., Koltun, V.: Robust continuous clustering. Proc. Natl. Acad. Sci. 114(37), 9814–9819 (2017)

    Article  Google Scholar 

  63. Baraldi, A., Alpaydin, E.: Constructive feedforward ART clustering networks. IEEE Trans. Neural Netw. 13(3), 645–661 (2002)

    Article  Google Scholar 

  64. Backer, E., Jain, A.K.: A clustering performance measure based on fuzzy set decomposition. IEEE Trans. Pattern Anal. Mach. Intell. 1, 66–75 (1981)

    Article  MATH  Google Scholar 

  65. Zhang, X., Wang, D., Chen, H.: Improved biogeography-based optimization algorithm and its application to clustering optimization and medical image segmentation. IEEE Access 7, 28810–28825 (2019)

    Article  Google Scholar 

  66. Jiang, Y., et al.: A novel distributed multitask fuzzy clustering algorithm for automatic MR brain image segmentation. J. Med. Syst. 43(5), 118 (2019)

    Article  Google Scholar 

  67. Kalra, M., et al.: Online variational learning for medical image data clustering. In: Mixture Models and Applications, pp. 235–269. Springer, New York (2020)

    Chapter  Google Scholar 

  68. Masdari, M., Barshande, S., Ozdemir, S.: CDABC: chaotic discrete artificial bee colony algorithm for multi-level clustering in large-scale WSNs. J. Supercomput. 75(11), 7174–7208 (2019)

    Article  Google Scholar 

  69. Masdari, M., Barshandeh, S.: Discrete teaching–learning-based optimization algorithm for clustering in wireless sensor networks. J. Ambient Intell. Hum. Comput. 11, 5459 (2020)

    Article  Google Scholar 

  70. Shukla, A.K., Muhuri, P.K.: Big-data clustering with interval type-2 fuzzy uncertainty modeling in gene expression datasets. Eng. Appl. Artif. Intell. 77, 268–282 (2019)

    Article  Google Scholar 

  71. Alguliyev, R.M., Aliguliyev, R.M., Sukhostat, L.V.: Efficient algorithm for big data clustering on single machine. CAAI Trans. Intell. Technol. 5(1), 9–14 (2020)

    Article  Google Scholar 

  72. Benabdellah, A.C., Benghabrit, A., Bouhaddou, I.: A survey of clustering algorithms for an industrial context. Procedia Comput. Sci. 148, 291–302 (2019)

    Article  Google Scholar 

  73. Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)

    Article  Google Scholar 

  74. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Oakland (1967)

  75. Park, H.-S., Jun, C.-H.: A simple and fast algorithm for K-medoids clustering. Expert Syst. Appl. 36(2), 3336–3341 (2009)

    Article  Google Scholar 

  76. Zhang, L.S., Yang, M.J., Lei, D.J.: An improved PAM clustering algorithm based on initial clustering centers. Appl. Mech. Mater. 135–136, 244 (2012)

    Google Scholar 

  77. Zhao, G.-F., Qu, G.-Q.: Analysis and implementation of CLARA algorithm on clustering. J. Shandong Univ. Technol. 2, 45–48 (2006)

    Google Scholar 

  78. Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: a new data clustering algorithm and its applications. Data Min. Knowl. Disc. 1(2), 141–182 (1997)

    Article  Google Scholar 

  79. Guha, S., Rastogi, R., Shim, K.: Cure: an efficient clustering algorithm for large databases. Inf. Syst. 26(1), 35–58 (2001)

    Article  MATH  Google Scholar 

  80. Guha, S., Rastogi, R., Shim, K.: ROCK: a robust clustering algorithm for categorical attributes. Inf. Syst. 25(5), 345–366 (2000)

    Article  Google Scholar 

  81. Kriegel, H.P., et al.: Density-based clustering. Wiley Interdiscip. Rev. 1(3), 231–240 (2011)

    Google Scholar 

  82. Kumar, K.M., Reddy, A.R.M.: A fast DBSCAN clustering algorithm by accelerating neighbor searching using Groups method. Pattern Recogn. 58, 39–48 (2016)

    Article  Google Scholar 

  83. Ankerst, M., et al.: OPTICS: ordering points to identify the clustering structure. ACM SIGMOD Rec. 28(2), 49–60 (1999)

    Article  Google Scholar 

  84. Rehioui, H., et al.: DENCLUE-IM: a new approach for big data clustering. Procedia Comput. Sci. 83, 560–567 (2016)

    Article  Google Scholar 

  85. Saini, S., Rani, P.: A survey on STING and CLIQUE grid based clustering methods. Int. J. Adv. Res. Comput. Sci., 2017. 8(5).

  86. Duan, D., et al.: Incremental K-clique clustering in dynamic social networks. Artif. Intell. Rev. 38(2), 129–147 (2012)

    Article  Google Scholar 

  87. Hinneburg, A. Keim, D.A.: Optimal grid-clustering: towards breaking the curse of dimensionality in high-dimensional clustering (1999)

  88. Yang, M.-S., Lai, C.-Y., Lin, C.-Y.: A robust EM clustering algorithm for Gaussian mixture models. Pattern Recogn. 45(11), 3950–3961 (2012)

    Article  MATH  Google Scholar 

  89. Li, M., Holmes, G., Pfahringer, B.: Clustering large datasets using Cobweb and K-Means in tandem. In: Proceedings of the Australasian Joint Conference on Artificial Intelligence. Springer (2014)

  90. Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Trans. Neural Netw. 11(3), 586–600 (2000)

    Article  Google Scholar 

  91. Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat.-Theory Methods 3(1), 1–27 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  92. Hubert, L.J., Levin, J.R.: A general statistical framework for assessing categorical clustering in free recall. Psychol. Bull. 83(6), 1072 (1976)

    Article  Google Scholar 

  93. Xing, G., et al.: Integrated coverage and connectivity configuration for energy conservation in sensor networks. ACM Trans. Sens. Netw. (TOSN) 1(1), 36–72 (2005)

    Article  Google Scholar 

  94. Baker, F.B., Hubert, L.J.: Measuring the power of hierarchical cluster analysis. J. Am. Stat. Assoc. 70(349), 31–38 (1975)

    Article  MATH  Google Scholar 

  95. Shieh, G.S.: A weighted Kendall’s tau statistic. Stat. Probab. Lett. 39(1), 17–24 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  96. Cleland, J., et al.: Effect of Ramipril on Morbidity and Mode of Death Among Survivors of Acute Myocardial Infarction with Clinical Evidence of Heart Failure: A Report from the AIRE Study Investigators. Oxford University Press, Oxford (1997)

    Book  Google Scholar 

  97. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 2, 224–227 (1979)

    Article  Google Scholar 

  98. Pakhira, M.K., Bandyopadhyay, S., Maulik, U.: Validity index for crisp and fuzzy clusters. Pattern Recogn. 37(3), 487–501 (2004)

    Article  MATH  Google Scholar 

  99. Lilliefors, H.W.: On the Kolmogorov-Smirnov test for normality with mean and variance unknown. J. Am. Stat. Assoc. 62(318), 399–402 (1967)

    Article  Google Scholar 

  100. Cheng, T.Y., Wang, M., Jia, X.: QoS-guaranteed controller placement in SDN. In: Proceedings of the 2015 IEEE Global Communications Conference (GLOBECOM). IEEE (2015)

  101. Liu, J., Liu, J., Xie, R.: Reliability-based controller placement algorithm in software defined networking. Comput. Sci. Inf. Syst. 13(2), 547–560 (2016)

    Article  Google Scholar 

  102. Cheng, G., et al.: Dynamic switch migration towards a scalable SDN control plane. Int. J. Commun. Syst. 29(9), 1482–1499 (2016)

    Article  Google Scholar 

  103. Wang, G., et al.: A K-means-based network partition algorithm for controller placement in software defined network. In: Proceedings of the 2016 IEEE International Conference on Communications (ICC). IEEE (2016)

  104. Deb, K., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  105. Ishibuchi, H., et al.: Evolutionary many-objective optimization by NSGA-II and MOEA/D with large populations. In: Proceedings of the 2009 IEEE International Conference on Systems, Man and Cybernetics. IEEE (2009)

  106. Elarbi, M., et al.: A new decomposition-based NSGA-II for many-objective optimization. IEEE Trans. Syst. Man Cybern. Syst. 48(7), 1191–1210 (2017)

    Article  Google Scholar 

  107. Rabiee, M., Zandieh, M., Ramezani, P.: Bi-objective partial flexible job shop scheduling problem: NSGA-II, NRGA, MOGA and PAES approaches. Int. J. Prod. Res. 50(24), 7327–7342 (2012)

    Article  Google Scholar 

  108. Zou, J., et al.: A knee-point-based evolutionary algorithm using weighted subpopulation for many-objective optimization. Swarm Evol. Comput. 47, 33–43 (2019)

    Article  Google Scholar 

  109. Dhiman, G., Kumar, V.: KnRVEA: a hybrid evolutionary algorithm based on knee points and reference vector adaptation strategies for many-objective optimization. Appl. Intell. 49(7), 2434–2460 (2019)

    Article  Google Scholar 

  110. Ye, X., et al.: User-oriented many-objective cloud workflow scheduling based on an improved knee point driven evolutionary algorithm. Knowl.-Based Syst. 135, 113–124 (2017)

    Article  Google Scholar 

  111. Liao, J., et al.: Density cluster based approach for controller placement problem in large-scale software defined networkings. Comput. Netw. 112, 24–35 (2017)

    Article  Google Scholar 

  112. Firouz, N., et al.: A novel controller placement algorithm based on network portioning concept and a hybrid discrete optimization algorithm for multi-controller software-defined networks. Clust. Comput. 24, 1–34 (2021)

    Article  Google Scholar 

  113. Deb, K., et al. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Proceedings of the International Conference on Parallel Problem Solving from Nature. Springer (2000)

  114. Coello, C.C., Lechuga, M.S.: MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600). IEEE (2002)

  115. Mirjalili, S., et al.: Optimization of problems with multiple objectives using the multi-verse optimization algorithm. Knowl.-Based Syst. 134, 50–71 (2017)

    Article  Google Scholar 

  116. Mirjalili, S.Z., et al.: Grasshopper optimization algorithm for multi-objective optimization problems. Appl. Intell. 48(4), 805–820 (2018)

    Article  Google Scholar 

  117. Liang, J., et al.: Performance analysis on knee point selection methods for multi-objective sparse optimization problems. In: Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC). IEEE (2018)

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Authors and Affiliations

  1. Department of Computer Engineering, Islamic Azad University, Urmia Branch, Urmia, Iran

    Nasrin firouz, Mohammad Masdari, Amin Babazadeh Sangar & Kambiz Majidzadeh

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  1. Nasrin firouz
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  2. Mohammad Masdari
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Correspondence to Mohammad Masdari.

Appendix A The Acronyms and Their Description

Appendix A The Acronyms and Their Description

Table A: The Nomenclatures

Acronym

Description

SDN

Software-defined network

CPP

Controller placement problem

MPA

Marine predator algorithm

DMPA

Discrete marine predator algorithm

MOMPA

Multi-objective marine predator algorithm

GA

Genetic algorithm

NSGA-II

Non-dominated sorting genetic algorithm-II

\(lb\)

The lower bound of the problem space

\(ub\)

The upper bound of the problem space

\(X\)

The population of preys

\(n\)

The number of preys in MPA

\(d\)

The dimension of the problem

\({X}^{I}\)

The predator (the best prey)

\({f}_{B}\)

The Brownian motion function

\({f}_{L}\)

The Levy flight function

\(a\)

The power-law exponent

\(\Gamma \)

The Gamma function

\(Iter\)

Current iteration

\(MaxIter\)

Maximum number of iterations

\(FADs\)

Fish Aggregating Devices

\(Mp\)

Mutation probability

\(Cp\)

Crossover probability

\(S\)

The set of switches

\({S}_{i}\)

The \(i\) th switch

\(W\)

The number of switches

\(({X}_{n}, {Y}_{n})\)

The location of \(n\) th switch in the network

\({l}_{i,j}\)

The communication link between \(i\) th and \(jt\) h switches

\({F}_{j}\)

The \(j\) th objective function

\(|O|\)

The number of objectives

\({L}^{C-S}\)

Switch to controller latency

\({L}^{C-C}\)

Controller to controller latency

\({L}_{u,v}\)

The latency between switch \(u\) and controller \(v\)

\(P\)

The set of controllers

\(Q\)

The number of controllers

\({n}_{p}\)

The total number of assigned switches to the controller \(p\)

\({C}_{j}\)

The crowding distance

\(KP\)

The Knee Point strategy

\({z}_{ij}\)

The normalized of \(i\) th objective value of \(j\) th solution

\({\omega }_{i}\)

The entropy weight of the \(i\) th objective

\({H}_{i}\)

The entropy value of the \(i\) th objective

\({dist}_{i,j}\)

The distance between \(i\) th and \(j\) th switches

\({dist}_{c}\)

The threshold distance

\({\rho }_{i}\)

The local density of \(i\) th switch

\({\delta }\)

The distance between each switch and switches with higher local density

\(DEP\)

Distance to the Extreme Plate

\(|OT|\)

The number of solutions in the obtained Pareto Front

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firouz, N., Masdari, M., Sangar, A.B. et al. A Hybrid Multi-objective Algorithm for Imbalanced Controller Placement in Software-Defined Networks. J Netw Syst Manage 30, 51 (2022). https://doi.org/10.1007/s10922-022-09650-y

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  • DOI: https://doi.org/10.1007/s10922-022-09650-y

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