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Genetic algorithm based bi-objective optimization of sigmoidal utility and throughput in ad-hoc wireless networks

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Abstract

With the rapid growth of ad-hoc wireless technology amidst the huge demands of high data-rates, the simultaneous optimization of overall networking throughput and utility-proportional resource allocation is of utmost significance. The design of infrastructure-less ad-hoc wireless network with highly dynamic operating conditions can be intelligently modeled by using the artificial intelligence algorithms and machine learning techniques. In this work, we employ the meta-heuristic computational method of genetic algorithm for multi-criterion optimization of sigmoidal utility maximization and throughput efficiency. The iterative implementation of the evolutionary genetic algorithm is effectively enforced by considering the comprehensive application of eight disparate combinations of individual creation, selection, mutation and crossover operations. Through the simulation experiments, the impact of execution of these feasible composite models on the derived two-dimensional Pareto-optimal frontier, the criteria satisfaction ratio and the average spread of individual solutions is extensively investigated. Furthermore, these diverse genetic operations for retrieving the optimal solution to the proposed problem formulation are compared in terms of the estimated precision and time complexity metrics. Finally, the efficacy of our optimization model is significantly compared with the previous works in terms of achievable throughput.

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References

  1. Haupt RL, Haupt SE (2004) Practical genetic algorithms, 2nd edn. Wiley, New York

    MATH  Google Scholar 

  2. Ali AH, Nazir MM (2016) QoS oriented multiobjective optimizer for radio resource management of LTE-A femtocells. Mob Inf Syst 2016:7964359. https://doi.org/10.1155/2016/7964359

  3. Grzyb S, Orlowski P (2020) Multi-objective optimization of two control strategies for congestion avoiding in computer network. In: 2020 16th international conference on control, automation, robotics and vision (ICARCV), pp 497–504. https://doi.org/10.1109/ICARCV50220.2020.9305358

  4. Muwonge BS, Pei T, Otim JS, Mayambala F (2020) A joint power, delay and rate optimization model for secondary users in cognitive radio sensor networks. Sensors (Basel, Switzerland) 20(17):4907. https://doi.org/10.3390/s20174907

    Article  Google Scholar 

  5. Han X, Sun Y (2018) Resource allocation algorithm based on multi-objective optimization in D2D communication. In: 3rd international conference on communications, information management and network security (CIMNS 2018), advances in computer science research, vol 65, pp 1–4. https://doi.org/10.2991/cimns-18.2018.1

  6. Sámano-Robles R (2018) On the throughput region of wireless random access protocols with multi-packet reception using multi-objective optimization. Technologies 6(4):117. https://doi.org/10.3390/technologies6040117

    Article  Google Scholar 

  7. Dong X, Cheng L, Zheng G, Wang T (2019) Multi-objective optimization method for spectrum allocation in cognitive heterogeneous wireless networks. AIP Adv 9(4):045130. https://doi.org/10.1063/1.5092211

    Article  Google Scholar 

  8. Nardelli PHJ, Kountouris M, Cardieri P, Latva-aho M (2014) Throughput optimization in wireless networks under stability and packet loss constraints. IEEE Trans Mob Comput 13(8):1883–1895. https://doi.org/10.1109/TMC.2013.49

    Article  Google Scholar 

  9. Ding X, Wang J, Zhao C et al (2021) The throughput optimization for wireless sensor networks adopting interference alignment and successive interference cancellation. Peer-to-Peer Netw Appl 14:1748–1764. https://doi.org/10.1007/s12083-020-00972-9

    Article  Google Scholar 

  10. Li Y, Yang D, Xu Y, **ao L, Chen H (2019) Throughput maximization for UAV-enabled relaying in wireless powered communication networks. Sensors (Basel, Switzerland) 19(13):2989. https://doi.org/10.3390/s19132989

    Article  Google Scholar 

  11. Taki M, Svensson T, Nezafati MB (2019) Delay constrained throughput optimization in multi-hop AF relay networks, using limited quantized CSI. EURASIP J Wirel Commun Netw Article No. 102. https://doi.org/10.1186/s13638-019-1423-3

  12. Cohen K, Leshem A (2013) Distributed throughput maximization for multi-channel ALOHA networks. In: 2013 5th IEEE international workshop on computational advances in multi-sensor adaptive processing (CAMSAP), pp 456–459. https://doi.org/10.1109/CAMSAP.2013.6714106

  13. Selvakumar G, Ramesh KS, Chaudhari S, Jain M (2019) Throughput optimization methods for TDMA-based tactical mobile ad hoc networks. Integr Intell Comput Commun Secur Stud Comput Intell 771:323–331. https://doi.org/10.1007/978-981-10-8797-4_34

    Article  Google Scholar 

  14. Hussain AS, Deka SK, Chauhan P, Karmakar A (2019) Throughput optimization for interference aware underlay CRN. Wirel Pers Commun 107:325–340. https://doi.org/10.1007/s11277-019-06257-6

    Article  Google Scholar 

  15. Chiu CC, Cheng YT, Yang CH (2019) Capacity optimization of multi-input/multi-output relay channel by SADDE algorithm. Ann Telecommun 74:365–372. https://doi.org/10.1007/s12243-019-00708-8

    Article  Google Scholar 

  16. Bhaumick D, Ghosh SC (2019) Throughput optimization for multirate multicasting through association control in IEEE 802.11 WLAN. Quality, reliability, security and robustness in heterogeneous systems, lecture notes of the institute for computer sciences, social informatics and telecommunications engineering, vol 272, pp 27–47. https://doi.org/10.1007/978-3-030-14413-5_3

  17. Ding C, Shen L, Liu D et al (2017) A game theoretic learning solution for distributed relay selection on throughput optimization. Wirel Netw 23:1757–1766. https://doi.org/10.1007/s11276-016-1250-y

    Article  Google Scholar 

  18. Sun X, Gao Y (2018) Distributed throughput optimization for heterogeneous IEEE 802.11 DCF networks. Wirel Netw 24:1205–1215. https://doi.org/10.1007/s11276-016-1392-y

    Article  Google Scholar 

  19. Mehta R, Lobiyal DK (2017) Bi-objective cross-layer design using different optimization methods in multi-flow ad-hoc networks. In: International conference on information, communication and computing technology (ICICCT 2017), pp 57–67. https://doi.org/10.1007/978-981-10-6544-6_6

  20. Mehta R (2021) Trade-off between spectral efficiency and normalized energy in ad-hoc wireless networks. Wirel Netw 27(4):2615–2627. https://doi.org/10.1007/s11276-021-02610-5

    Article  Google Scholar 

  21. Mehta R (2020) Throughput and resource optimization for adaptive coding-based random access networks with correlated sources. Int J Commun Syst 34(1):e4673. https://doi.org/10.1002/dac.4673

    Article  Google Scholar 

  22. Alnwaimi G, Boujemaa H, Arshad K (2021) Throughput optimization of cooperative non orthogonal multiple access. Telecommun Syst 76:359–370. https://doi.org/10.1007/s11235-020-00726-1

    Article  Google Scholar 

  23. Patri SR, Nithyanandan L (2022) Optimization of relay-based network throughput for NB-CR-IoT networks. Soft Comput Signal Process Adv Intell Syst Comput 1340:457–464. https://doi.org/10.1007/978-981-16-1249-7_43

    Article  Google Scholar 

  24. Abualigah L (2018) Feature selection and enhanced krill herd algorithm for text document clustering. Stud Comput Intell. https://doi.org/10.1007/978-3-030-10674-4

    Article  Google Scholar 

  25. Abualigah L, Diabat A, Mirjalili S, Elaziz MA, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609. https://doi.org/10.1016/j.cma.2020.113609

    Article  MathSciNet  MATH  Google Scholar 

  26. Abualigah L, Elaziz MA, Sumari P, Geem ZW, Gandomi AH (2022) Reptile Search Algorithm (RSA): a nature-inspired meta-heuristic optimizer. Expert Syst Appl 191:116158. https://doi.org/10.1016/j.eswa.2021.116158

    Article  Google Scholar 

  27. Abualigah L, Yousri D, Elaziz MA, Ewees AA, Al-qaness MAA, Gandomi AH (2021) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250. https://doi.org/10.1016/j.cie.2021.107250

    Article  Google Scholar 

  28. Azizi A (2020) Applications of artificial intelligence techniques to enhance sustainability of industry 4.0: design of an artificial neural network model as dynamic behavior optimizer of robotic arms. Complexity 2020:8564140. https://doi.org/10.1155/2020/8564140

  29. Azizi A (2019) Hybrid artificial intelligence optimization technique. In: Applications of artificial intelligence techniques in industry 4.0. https://doi.org/10.1007/978-981-13-2640-0_4

  30. Azizi A (2017) Introducing a novel hybrid artificial intelligence algorithm to optimize network of industrial applications in modern manufacturing. Complexity 2017:8728209. https://doi.org/10.1155/2017/8728209

  31. Azizi A, Entessari F, Osgouie KG, Rashnoodi AR (2013) Introducing neural networks as a computational intelligent technique. Appl Mech Mater 464:369–374. https://doi.org/10.4028/www.scientific.net/amm.464.369

    Article  Google Scholar 

  32. Ashkzari A, Azizi A (2014) Introducing genetic algorithm as an intelligent optimization technique. Appl Mech Mater 568–570:793–797. https://doi.org/10.4028/www.scientific.net/amm.568-570.793

    Article  Google Scholar 

  33. MATLAB [Online]. http://www.mathworks.com/products/matlab/description1.html

  34. Murthy CSR, Manoj BS (2007) Ad hoc wireless networks, architectures and protocols, 2nd edn. Pearson Education, Low price Edition, London

    Google Scholar 

  35. Rappaport TS (1996) Wireless communications: principles and practice. Prentice Hall Inc, Upper Saddle River

    MATH  Google Scholar 

  36. Grieco LA et al (2020) Ad-hoc, mobile, and wireless networks. In: 19th international conference on ad-hoc networks and wireless, ADHOC-NOW 2020, Bari, Italy, October 19–21, proceedings, vol 12338, Springer Nature

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  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India

    Ridhima Mehta

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Mehta, R. Genetic algorithm based bi-objective optimization of sigmoidal utility and throughput in ad-hoc wireless networks. Evol. Intel. 16, 1259–1269 (2023). https://doi.org/10.1007/s12065-022-00735-w

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