Dynamic kidney paired exchange using modified multiverse optimization

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.