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Application of nature inspired algorithms to multi-objective optimization of new generation network problem

Published:19 July 2022Publication History

ABSTRACT

The subject of the paper is the application of metaheuristic algorithms inspired by nature for multi-criteria optimization in new generation optical networks. In the considered optical network, criteria related to the structure and topology of the network and the equipment used were taken into account. Network criteria include the length of optical channels, optical fiber attenuation, and dispersion. On the other hand, the hardware criteria include the cost of transponders and a finite range of frequency slides in the optical spectrum of the optical fiber. Several nature-inspired metaheuristics were used for the multi-criteria optical network optimization problem. The proposed algorithm, based on the bee algorithm was compared with others taken from the literature. Simulation results of all algorithms were implemented and carried out using test networks with topology typical for telecommunication networks. The proposed and improved algorithm obtained good results that encourage further work and research.

References

  1. J. Arabas and Stanislaw Kozdrowski. 2001. Applying an evolutionary algorithm to telecommunication network design. Evolutionary Computation, IEEE Transactions on 5 (09 2001), 309 -- 322. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Charles Audet, J Bigeon, D Cartier, Sébastien Le Digabel, and Ludovic Salomon. 2021. Performance indicators in multiobjective optimization. European Journal of Operational Research 292, 2 (Feb. 2021), 397--422.Google ScholarGoogle ScholarCross RefCross Ref
  3. Misha Brodsky, Nicholas J. Frigo, Misha Boroditsky, and Moshe Tur. 2006. Polarization Mode Dispersion of Installed Fibers. Journal of Lightwave Technology 24, 12 (2006), 4584--4599. Google ScholarGoogle ScholarCross RefCross Ref
  4. David W. Corne, Nick R. Jerram, Joshua D. Knowles, and Martin J. Oates. 2001. PESA-II: Region-Based Selection in Evolutionary Multiobjective Optimization. In 3rd Annual Conference on Genetic and Evolutionary Computation.Google ScholarGoogle Scholar
  5. Peter E. Hart, Nils J. Nilsson, and Bertram Raphael. 1968. A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics 4, 2 (1968). Google ScholarGoogle ScholarCross RefCross Ref
  6. Lin Jiang, Lianshan Yan, Anlin Yi, Yan Pan, Ming Hao, Wei Pan, Bin Luo, and Yves Jaouën. 2018. Chromatic Dispersion, Nonlinear Parameter, and Modulation Format Monitoring Based on Godard's Error for Coherent Optical Trans. Sys. IEEE Phot. Jour. 10, 1 (2018), 1--12. Google ScholarGoogle ScholarCross RefCross Ref
  7. Stanisław Kozdrowski, Mateusz Zotkiewicz, and Sławomir Sujecki. 2020. Ultra-Wideband WDM Optical Network Optimization. Photonics 7, 1 (2020). Google ScholarGoogle ScholarCross RefCross Ref
  8. Miqing Li, Shengxiang Yang, Xiaohui Liu, and Kang Wang. 2013. IPESA-II: Improved Pareto Envelope-Based Selection Algorithm II. In Evolutionary Multi-Criterion Optimization. Springer, Berlin, Heidelberg, 143--155.Google ScholarGoogle Scholar
  9. Jiayi Liu, Zude Zhou, D. Pham, Wenjun Xu, Junwei Yan, Aiming Liu, Cuilian Ji, and Quan Liu. 2018. An improved multi-objective discrete bees algorithm for robotic disassembly line balancing problem in remanufacturing. (08 2018). Google ScholarGoogle ScholarCross RefCross Ref
  10. Hardik H. Maheta and Vipul K. Dabhi. 2014. An improved SPEA2 Multi objective algorithm with non dominated elitism and Generational Crossover. Google ScholarGoogle ScholarCross RefCross Ref
  11. Abbas Moradi, Amin Mirzakhani, and Afshin Ghanbarzadeh. 2015. Multi-objective optimization of truss structures using the bee algorithm. Scientia Iranica 22 (2015), 1789--1800.Google ScholarGoogle Scholar
  12. Hamid Reza Nasrinpour, Amir Massah Bavani, and Mohammad Teshnehlab. 2017. Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm. Computers 6, 1 (2017). Google ScholarGoogle ScholarCross RefCross Ref
  13. Claunir Pavan, Rui Manuel Morais, José R. Ferreira da Rocha, and Armando Nolasco Pinto. 2010. Generating Realistic Optical Transport Network Topologies. J. Opt. Commun. Netw. 2, 1 (Jan 2010), 80--90. Google ScholarGoogle ScholarCross RefCross Ref
  14. Wanxing Sheng, Yongmei Liu, Xiaoli Meng, and Tianshu Zhang. 2012. An Improved Strength Pareto Evolutionary Algorithm 2. (2012). Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. N. Srinivas and Kalyanmoy Deb. 2000. Multiobjective Function Optimization Using Nondominated Sorting Genetic Algorithms. 2 (06 2000).Google ScholarGoogle Scholar
  16. Kacper Wnuk and Stanisław Kozdrowski. 2021. Multi-objective Optimization in Optical Networks. In 2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM). 1--3. Google ScholarGoogle ScholarCross RefCross Ref
  17. Jin Y. Yen. 1971. Finding the K Shortest Loopless Paths in a Network. Management Science 17, 11 (Jul 1971).Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Xingyi Zhang, Ye Tian, Ran Cheng, and Yaochu Jin. 2015. An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization. IEEE Transactions on Evolutionary Computation (2015).Google ScholarGoogle Scholar

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      cover image ACM Conferences
      GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion
      July 2022
      2395 pages
      ISBN:9781450392686
      DOI:10.1145/3520304

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      Publication History

      • Published: 19 July 2022

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