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Dynamic kidney paired exchange using modified multiverse optimization

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Abstract

Kidney exchange is among the effective methods that may permanently supply an important platform for incompatible donor-candidate pairs to exchange organs to achieve mutual benefit and guarantee treatment to people with kidney failure. However, building a dynamic model for Kidney Paired Exchange has become an increasingly urgent issue for augmenting the number of available kidneys in the field of organ transplantation. There has not been made a lot of research on the kidney exchange problem in a dynamic situation. Mathematically, maximizing the possible kidney exchanges for a given pool can be considered as an optimization problem and has attracted the attention of the community of researchers in the past few years. Thus, optimization approaches, like natural-inspired algorithms, can help Kidney paired exchange in defining which transplants should be made among all incompatible pairs according to some objectives. In this paper, a new natural stochastic-based algorithm called a Multiverse Optimizer is introduced to develop advanced dynamic approaches for kidney paired exchange. The objective of the proposed approach is to maximize the number of feasible cycles and chains among the pool pairs over time. The effectiveness of the proposed method is confirmed by providing the best performance compared to the results of genetic and antlion algorithms which are the only stochastic-based optimization algorithms applied to the kidney exchange. Furthermore, we applied more stochastic-based optimization algorithms for Kidney paired exchange to confirm the overall performance superiority of our proposed method. The performance of the proposed method in a dynamic situation demonstrates the competitiveness of the proposed method.

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References

  1. Abraham DJ, Avrim B, Tuomas S (2007) Clearing algorithms for barter exchange markets: enabling nationwide kidney exchanges. In: Proceedings of the 8th ACM conference on electronic commerce. ACM, pp 295–304

  2. Agarwal N, Ashlagi I, Azevedo E, Featherstone CR, Karaduman Ö (2019) Market failure in kidney exchange. Am Econ Rev 109(11):4026–70

    Article  Google Scholar 

  3. Akbarpour M, Li S, Oveis GS (2014) Dynamic matching market design. arXiv:1402.3643

  4. Anderson R, Ashlagi I, Gamarnik D, Kanoria Y (2015) A dynamic model of barter exchange. In: Proceedings of the twenty-sixth annual ACM-SIAM symposium on discrete algorithms. Society for Industrial and Applied Mathematics, pp 1925–1933

  5. Anderson R, Ashlagi I, Gamarnik D, Roth AE (2015) Finding long chains in kidney exchange using the traveling salesman problem. Proc Natl Acad Sci 112(3):663–668

    Article  Google Scholar 

  6. Andersson T (2015) Pairwise kidney exchange with blood-group incompatibility. Lund University Department of Economics working paper

  7. Benmessahel I, Xie K, Chellal M (2018) A new evolutionary neural networks based on intrusion detection systems using multiverse optimization. Appl Intell 48(8):2315–2327

    Article  Google Scholar 

  8. Burnapp L (2014) National living donor kidney sharing schemes. HTA Independent Assessor Conference. https://www.hta.gov.uk/sites/default/files/Lisa_Burnapp_-_The_national_picture_-_2014_IA_Conference.pdf

  9. Dababneh D, Truc Doan LT, Amer Y, My Tran DT (2019) A proposed genetic algorithm approach for the kidney exchange problem. In: 2019 International conference on system science and engineering (ICSSE). pp 383–390

  10. Dickerson P John, MF David, PB, Sandholm T, Trimble J (2016) Position-indexed formulations for kidney exchange. In: Proceedings of the 2016 ACM conference on economics and computation. ACM, pp 25–42

  11. Dickerson JP, Procaccia A, Sandholm T (2012) Dynamic matching via weighted myopia with application to kidney exchange. In: Twenty-sixth AAAI conference on artificial intelligence

  12. Dickerson PJ, Sandholm T (2015) Futurematch: combining human value judgments and machine learning to match in dynamic environments. In: Twenty-ninth AAAI conference on artificial intelligence

  13. Ellison B (2014) A systematic review of kidney paired donation. Applying lessons from historic and contemporary case studies to improve the us model

  14. Goezinne S, Bekker R, Glorie K (2016) A genetic algorithm for kidney transplantation matching. Business Analytics Master program at VU University, Amsterdam

    Google Scholar 

  15. Hamouda E, El-Metwally S, Tarek M (2018) Ant lion optimization algorithm for kidney exchanges. PLoS ONE 13(5):e0196707

    Article  Google Scholar 

  16. Jia H, Peng X, Song W, Lang C, Xing Z, Sun K (2019) Multiverse optimization algorithm based on lévy flight improvement for multithreshold color image segmentation. IEEE Access 7:32805–32844

    Article  Google Scholar 

  17. Kahng A (2016) Timing objectives in dynamic kidney exchange. Ph.D thesis

  18. Kwon OJ, Kwak JY, Lee KS, Kang CM, Park HY (1999) Exchange-donor program in renal transplantation: a single center experience. J Korean Surg Soc 57(6):789–796

    Google Scholar 

  19. Li J, Liu Y, Huang L, Tang P (2014) Egalitarian pairwise kidney exchange: fast algorithms vialinear programming and parametric flow. In: Proceedings of the 2014 international conference on autonomous agents and multi-agent systems. International Foundation for Autonomous Agents and Multiagent Systems, pp 445–452

  20. Lucan M (2007) Five years of single-center experience with paired kidney exchange transplantation. In: Transplantation proceedings, vol 39. Elsevier, pp 1371–1375

  21. Mak-Hau V (2017) On the kidney exchange problem: cardinality constrained cycle and chain problems on directed graphs: a survey of integer programming approaches. J Combin Optim 33(1):35–59

    Article  MathSciNet  Google Scholar 

  22. Malik S, Cole E (2014) State of the art practices and policies in kidney paired donation. Curr Transplant Rep 1(1):10–17

    Article  Google Scholar 

  23. Manlove FD, O’Malley G (2012) Paired and altruistic kidney donation in the UK: algorithms and experimentation. In: International symposium on experimental algorithms. Springer, pp 271–282

  24. Manlove DF, Gregg O’malley. (2015) Paired and altruistic kidney donation in the UK: algorithms and experimentation. J Exp Algorithmics 19:2–6

    Article  MathSciNet  Google Scholar 

  25. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513

    Article  Google Scholar 

  26. Roth AE, Sönmez T et al (2005) A kidney exchange clearinghouse in New England. Am Econ Rev 95(2):376–380

    Article  Google Scholar 

  27. Roth AE, Sönmez T, Ünver MU (2004) Kidney exchange. Q J Econ 119(2):457–488

    Article  Google Scholar 

  28. Roth AE, Sönmez T, Utku Ünver M (2005) Pairwise kidney exchange. J Econ Theory 125(2):151–188

    Article  MathSciNet  Google Scholar 

  29. Roth AE, Sönmez T, Ünver MU (2007) Efficient kidney exchange: coincidence of wants in markets with compatibility-based preferences. Am Econ Rev 97(3):828–851

    Article  Google Scholar 

  30. Sakthivel S, Manimaran S (2013) An optimized kidney transplantation based on genetic algorithm. Int J Adv Res 3(4)

  31. Singla S (2018) Combinatorial optimization under uncertainty: probing and stopping-time algorithms. Ph.D. thesis, Carnegie Mellon University

  32. Sönmez T, Utku UM, Bumin YM (2017) Incentivized kidney exchange. Technical report, working paper

  33. Thiel PG, Vogelbach LG, Gasser T, Lehmann K, Voegele T, Kiss A, Kirste G (2001) Crossover renal transplantation: hurdles to be cleared!. Transplant Proc 1:811–816

    Article  Google Scholar 

  34. Ünver MU (2010) Dynamic kidney exchange. Rev Econ Stud 77(1):372–414

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, China

    Mouna Chellal, JianXin Wang & Abdelaziz Galoul

  2. College of Computer Science and Electronics Engineering, Hunan University, Changsha, China

    Ilyas Benmessahel

Authors
  1. Mouna Chellal
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  2. JianXin Wang
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  3. Ilyas Benmessahel
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  4. Abdelaziz Galoul
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Correspondence to Mouna Chellal.

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Appendix

Appendix

See Table 5.

Table 5 Patient and living donor distributions in simulations
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Chellal, M., Wang, J., Benmessahel, I. et al. Dynamic kidney paired exchange using modified multiverse optimization. Evol. Intel. 15, 397–406 (2022). https://doi.org/10.1007/s12065-020-00516-3

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