Automatic tuning of liver tissue model using simulated annealing and genetic algorithm heuristic approaches
Author(s) -
Salina Sulaiman,
Abdullah Bade,
Rechard Lee,
Siti Hasnah Tanalol
Publication year - 2014
Publication title -
aip conference proceedings
Language(s) - English
Resource type - Conference proceedings
eISSN - 1551-7616
pISSN - 0094-243X
DOI - 10.1063/1.4887608
Subject(s) - simulated annealing , genetic algorithm , computer science , algorithm , stiffness , heuristic , benchmark (surveying) , mathematical optimization , mathematics , artificial intelligence , engineering , structural engineering , machine learning , geography , geodesy
Mass Spring Model (MSM) is a highly efficient model in terms of calculations and easy implementation. Mass, spring stiffness coefficient and damping constant are three major components of MSM. This paper focuses on identifying the coefficients of spring stiffness and damping constant using automated tuning method by optimization in generating human liver model capable of responding quickly. To achieve the objective two heuristic approaches are used, namely Simulated Annealing (SA) and Genetic Algorithm (GA) on the human liver model data set. The properties of the mechanical heart, which are taken into consideration, are anisotropy and viscoelasticity. Optimization results from SA and GA are then implemented into the MSM to model two human hearts, each with its SA or GA construction parameters. These techniques are implemented while making FEM construction parameters as benchmark. Step size response of both models are obtained after MSMs were solved using Fourth Order Runge-Kutta (RK4) to compare the elast...
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