Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Binary Particle Swarm Optimization Algorithm for Kidney Exchanges Acceleration using Parallel MATLAB

Version 1 : Received: 21 December 2020 / Approved: 22 December 2020 / Online: 22 December 2020 (12:57:56 CET)
Version 2 : Received: 11 May 2021 / Approved: 14 May 2021 / Online: 14 May 2021 (11:03:30 CEST)

How to cite: Abdel-Rehim, W.M.F. Binary Particle Swarm Optimization Algorithm for Kidney Exchanges Acceleration using Parallel MATLAB. Preprints 2020, 2020120563 (doi: 10.20944/preprints202012.0563.v2). Abdel-Rehim, W.M.F. Binary Particle Swarm Optimization Algorithm for Kidney Exchanges Acceleration using Parallel MATLAB. Preprints 2020, 2020120563 (doi: 10.20944/preprints202012.0563.v2).

Abstract

In this paper, we implement a new method binary Particle Swarm Optimization (PSO) for solving the kidney exchange problem, which will improve the future decisions of kidney exchange programs. Because using a kidney exchange, we can help incompatible patient-donor couples to swap donors to receive a compatible kidney. Kidney paired donation programs provide an innovative approach for increasing the number of available kidneys. Further, we implementing binary particle swarm optimization in parallel with MATLAB with one, two, three and four threads and from the computations point of view, the authors compare the performance to reduce the running time for kidney exchange to match patients as fast as possible to help clinicians. Moreover, implementing binary particle swarm optimization in solving the kidney exchange problem is an effective method. The obtained results indicate that binary PSO outperforms other stochastic-based methods such as genetic algorithm, ant lion optimization, and efficient the number of resulting exchanges.

Subject Areas

kidney exchange; Particle Swarm; Meta-heuristics; Optimization

Comments (1)

Comment 1
Received: 14 May 2021
Commenter: Wael Abdel-Rehim
Commenter's Conflict of Interests: Author
Comment: Extended version of the research, acceleration using Parallel MATLAB.
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