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An optimization approach for transportation problem of transplant organ using self-adaptive bear hibernation algorithm

Abstract In this paper, we propose a new algorithm for transportation problem considering the multiple organ transplant scenario which is based on the concept of hibernation in bears. Hibernation is a state of inactivity based on localized adaptation instead of migrating out. Organ transplant is a major achievement of modern medicine,and it is helping in savingmany lives every day. In our proposed work, we consider a donor bank where all the required organs are brought for their transportation to the different organ transplant patients around the city and nearby areas. Also, each organ has its own preservation time before which the organ must be transplanted. Inspired from hibernation in bears, the proposed algorithm optimizes the time duration, and the priority of the organs to be transplanted. Here, we migrate or hibernate the organs according to the factors that affect the scenario the most. The factors include time constraints of the organs, risk of navigation in the wrong direction, etc. It has been tested on some benchmark functions and compared with other migration algorithms like AMO. The proposed algorithm removes the limitations of the optimization algorithms based on migrating models.