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A Novel System for Finding Shortest Path in a Network Routing Using Hybrid Evolutionary Algorithm

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Mobile Radio Communications and 5G Networks (MRCN 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 915))

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Abstract

It has been used as an inspiration for much scientific and technological advancement including improvements in the field of computer science such as the Shortest Path algorithms and Environment Machine Learning. These algorithms are used in a wide range of applications, such as finding the fastest route between two cities or the shortest route for a robot to navigate through an obstacle course. One way that the Big Bang Theory has improved Shortest Path algorithms is through the development of a new algorithm called the Big Bang Shortest Path algorithm. In addition, Environment Machine Learning is a branch of machine learning that focuses on learning from the environment. This approach to machine learning has been inspired by the way that the universe and the environment have evolved over time. By using the principles of the Big Bang Theory and Environment Machine Learning, researchers have been able to develop more efficient algorithms for a variety of tasks. For example, these techniques have been used to develop more accurate weather prediction models and to improve the performance of robotic systems in complex environments Overall, the Big Bang Theory has had a significant impact on the development of computer science, and has inspired many innovative ideas and technologies.

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References

  1. Aamodt A, Plaza E (1994) Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun 7:39–59

    Article  Google Scholar 

  2. Anwar MN, Pan S (2015) A new PID load frequency controller design method in frequency domain through direct synthesis approach. Int J Electr Power Energy Syst 67:560–569. https://doi.org/10.1016/j.ijepes.2014.12.024

    Article  Google Scholar 

  3. Benner P, Mehrmann V, Sorensen DC (2005) Dimension reduction of large-scale systems (Vol 45). Springer

    Google Scholar 

  4. Bilke S, Peterson C (2001) Topological properties of citation and metabolic networks. Rev E 64:036106

    Google Scholar 

  5. Biradar S, Hote YV, Saxena S (2016) Reduced-order modeling of linear time invariant systems using big bang big crunch optimization and time moment matching method. Appl Math Model 40(15–16):7225–7244. https://doi.org/10.1016/j.apm.2016.03.006

    Article  MathSciNet  Google Scholar 

  6. Can M. Le, Elizaveta Levina and Roman Vershynin (2017) Random structures & algorithms, 51

    Google Scholar 

  7. Chaudhuri K, Chung F, Tsiatas A (2012) Spectral clustering of graphs with general degrees in the extended planted partition model. Jpirnal of machine learning research workshop and conference proceedings

    Google Scholar 

  8. Das S, Pan I (2014) On the mixed H2, H∞ loop-shaping tradeoffs in fractional-order control of the AVR system on the mixed H2, H∞ loop-shaping tradeoffs in fractional-order control of the AVR system. IEEE Trans Industr Inf 10(4):1982–1991. https://doi.org/10.1109/TII.2014.2322812

    Article  Google Scholar 

  9. Desai S, Prasad R (2013) A novel order diminution of LTI systems using big bang big crunch optimization and routh approximation. Appl Math Model 37(16–17):8016–8028. https://doi.org/10.1016/j.apm.2013.02.052

    Article  MathSciNet  Google Scholar 

  10. Esmaeili M, Sedighizadeh M, Esmaili M (2016) Multiobjective optimal reconfiguration and DG (Distributed generation) power allocation in distribution networks using big bang-big crunch algorithm considering load uncertainty. Energy 103:86–99. https://doi.org/10.1016/j.energy.2016.02.152

    Article  Google Scholar 

  11. Gallehdari Z, Karrari M, Malik O (2009) Model order reduction using PSO algorithm and it’s application to power systems. In 2009 international conference on electric power and energy conversion systems, pp 1–5

    Google Scholar 

  12. Gugercin S, Antoulas AC, Beattie C (2008) H2 model reduction for large-scale linear dynamical systems H2 model reduction for large-scale linear dynamical systems. SIAM J Matrix Anal Appl 30(2):609–638. https://doi.org/10.1137/060666123

    Article  MathSciNet  Google Scholar 

  13. Gupta AK, Singh CN, Kumar D, Samuel P (2021) Modified Eigen permutation-based model simplification of LTI systems using evolutionary algorithm. In: Intelligent algorithms for analysis and control of dynamical systems, pp 41–49. Springer

    Google Scholar 

  14. Gutman P, Mannerfelt C, Molander P (1982) Contributions to the model reduction problem. IEEE Trans Autom Control 27(2):454–455. https://doi.org/10.1109/TAC.1982.1102930

    Article  Google Scholar 

  15. Goh KC, Ng RB, Wong YK, Ho NJ, Chua MC (2021) Aerial filming with synchronized drones using reinforcement learning. Multimed Tools Appl 80:1–26

    Article  Google Scholar 

  16. Hafez AT, Givigi SN, Yousefi S, Iskandarani M (2017) Multiuav tactic switching via model predictive control and fuzzy q-learning. J Eng Sci Mil Technol 1(2):44–57

    Article  Google Scholar 

  17. Kumbasar T, Eksin I, Guzelkaya M, Yesil E (2011) Adaptive fuzzy model based inverse controller design using BB–BC optimization algorithm. Expert Syst Appl 38(10):12356–12364. https://doi.org/10.1016/j.eswa.2011.04.015

    Article  Google Scholar 

  18. Kandoi G, Acencio ML, Lemke N (2015) Prediction of druggable proteins using machine learning and systems biology: a mini-review. Front Physiol 6:366

    Article  Google Scholar 

  19. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. NIPS’12 Proc 25th Int Conf Neural Inf Process Syst 1:1097–1105

    Google Scholar 

  20. Li D, Liu L, Jin Q, Hirasawa K (2015) Maximum sensitivity based fractional IMC-PID controller design for non-integer order system with time delay. J Process Control 31:17–29. https://doi.org/10.1016/j.jprocont.2015.04.001

    Article  Google Scholar 

  21. Li H, Luo Y, Chen Y (2009) A fractional order proportional and derivative (FOPD) motion controller: tuning rule and experiments. IEEE Trans Control Syst Technol 18(2):516–520. https://doi.org/10.1109/TCST.2009.2019120

    Article  Google Scholar 

  22. Schilders WH, Van der Vorst HA, Rommes J (2008) Model order reduction: theory, research aspects and applications (Vol 13). Springer

    Google Scholar 

  23. Li W, Han J, Pei J (2001) CMAR: Accurate and efficient classification based on multiple class-association rules. In: Proceedings 2001 IEEE international conference on data mining (ICDM) 369–376. IEEE

    Google Scholar 

  24. Lin J, Zhong C, Hu D, Rudin C, Seltzer M (2020) Generalized and scalable optimal sparse decision trees. In Proceedings of international conference on machine learning (ICML) 6150–6160

    Google Scholar 

  25. O’ Mahony N, Murphy T, Panduru K et al (2016) Adaptive process control and sensor fusion for process analytical technology. In: 2016 27th Irish signals and systems conference (ISSC). IEEE, pp 1–6

    Google Scholar 

  26. San KT, Lee EY, Chang YS (2016). The delivery assignment solution for swarms of uavs dealing with multi-dimensional chromosome representation of genetic algorithm. In: 2016 IEEE 7th annual ubiquitous computing, electronics and mobile communication conference (UEMCON). IEEE, pp 1–7

    Google Scholar 

  27. Tarca AL, Carey VJ, Chen X-W, Romero R, Drăghici S (2007) Machine learning and its applications to biology. This is an introduction to machine learning concepts and applications in biology with a focus on traditional machine learning methods. PLoS Comput Biol 3:e116

    Google Scholar 

  28. Venturini F, Mason F, Pase F, Chiariotti F, Testolin A, Zanella A, Zorzi M (2020) Distributed reinforcement learning for flexible uav swarm control with transfer learning capabilities. In: Proceedings of the 6th ACM workshop on micro aerial vehicle networks, systems, and applications, pp 1–6

    Google Scholar 

  29. Vijayakumari DM, Kim S, Suk J, Mo H (2019) Recedinghorizon trajectory planning for multiple uavs using particle swarm optimization. In: AIAA Scitech 2019 forum, p 1165

    Google Scholar 

  30. Ye F, Chen J, Tian Y, Jiang T (2020) Cooperative multiple task assignment of heterogeneous uavs using a modified genetic algorithm with multi-type-gene chromosome encoding strategy. J Intell Robot Syst 100:615–627

    Article  Google Scholar 

  31. Zhao D, Wang H, Shao K, Zhu Y (2016). Deep reinforcement learning with experience replay based on sarsa. In: 2016 IEEE symposium series on computational intelligence (SSCI). IEEE, pp 1–6

    Google Scholar 

  32. Zhao H, Pei Z, Jiang J, Guan R, Wang C, Shi X (2010) A hybrid swarm intelligent method based on genetic algorithm and artificial bee colony. In: International conference in swarm intelligence. Springer, pp 558–565

    Google Scholar 

  33. Zhao W, Qiu W, Zhou T, Shao X, Wang X (2019) Sarsa-based trajectory planning of multi-uavs in dense mesh router networks. In: 2019 international conference on wireless and mobile computing, networking and communications (WiMob). IEEE, pp 1–5

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science Engineering, CT University, Ludhiana, 142024, Punjab, India

    Tejinder Kaur & Jimmy Singla

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  1. Tejinder Kaur
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  2. Jimmy Singla
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Correspondence to Tejinder Kaur .

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Editors and Affiliations

  1. University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India

    Nikhil Kumar Marriwala

  2. University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, India

    Sunil Dhingra

  3. Electronics and Communication Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Shruti Jain

  4. Electrical and Computer System Engineering, RMIT University, Melbourne, VIC, Australia

    Dinesh Kumar

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Kaur, T., Singla, J. (2024). A Novel System for Finding Shortest Path in a Network Routing Using Hybrid Evolutionary Algorithm. In: Marriwala, N.K., Dhingra, S., Jain, S., Kumar, D. (eds) Mobile Radio Communications and 5G Networks. MRCN 2023. Lecture Notes in Networks and Systems, vol 915. Springer, Singapore. https://doi.org/10.1007/978-981-97-0700-3_4

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  • DOI: https://doi.org/10.1007/978-981-97-0700-3_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0699-0

  • Online ISBN: 978-981-97-0700-3

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