Parameters estimation for a mechanistic model of high dose irradiation damages using nelder-mead simplex and particle swarm optimization
Radiotherapy is a treatment that utilizes the high energy waves to treat cancers and tumors. The high energy radiation released from the therapy might directly kill the cancer cell or creates charged particles on the targeted area which consecutively damage the DNA. The damage part of DNA resulted f...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://eprints.utm.my/102680/1/NabilHaazimMohdAmirrudinMFS2020.pdf.pdf |
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| Summary: | Radiotherapy is a treatment that utilizes the high energy waves to treat cancers and tumors. The high energy radiation released from the therapy might directly kill the cancer cell or creates charged particles on the targeted area which consecutively damage the DNA. The damage part of DNA resulted from the high energy radiation will disrupts the growth and division of the cancer cell. However, the high dose radiation may bring side effects as it also may damages the nearby normal cell. The main objective of radiotherapy treatment is to maximize the damage on the cancer cell and minimize its side effect on the surrounding normal cell. Over the years, many mechanistic models had been developed to study the dynamic behavior of the cell population after it had been irradiated by high dose ionizing radiation. Determination of set of parameters of the mechanistic model helps to understand the dynamic behavior of the cell population. The current study aims at estimating parameter for a mechanistic model of high dose irradiation damage using two optimization algorithms which are Nelder-Mead Simplex (NMS) and Particle Swarm Optimization (PSO). The performance and efficiency of both optimization algorithms are compared based on the minimum value of sum of squared error, computational time and number of iteration to compute the objective function. The analysis demonstrates that NMS has higher accuracy and requires shorter time to minimize the objective function. On the other hand, PSO show a quicker convergence to achieve the objective function as compared to NMS. |
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