Hybrid optimal control-swarm intelligence for optimization of selected cancer chemotherapy model / Omar Ali Mohammad Shindi
Date of Award
1-1-2018
Thesis Type
masters
Document Type
Thesis (Restricted Access)
Divisions
eng
Department
Faculty of Engineering
Institution
Universiti Malaya
Abstract
Cancer chemotherapy optimization problem one of the critical cases until now, the researchers still working on it, to find the optimal amount of the drug, that reduce the toxicity and the tumor size. That caused increasing in the number of objectives and constraints, so increasing in the complexity of the optimization problem. This research project proposes two hybrid techniques that’s combined between the optimal control theory (OCT) with the swarm intelligence (SI) and evolutionary algorithms (EA), and check the performance of this techniques, with the popular method that used purely SI and EA algorithms, such M-MOPSO, MOPOS, MOEAD, MODE. The comparison between these methods, is done by solved a constraints multi-objectives optimization problem CMOOP, for the optimization problem of cancer chemotherapy treatment. The results of the hybrid techniques appear more efficient than that discovered by the purely SI and EA method. That’s improve the ability of the hybrid methods for solving the CMOOP with a high performance more than used a purely swarm intelligence. This will be very helpful for the clinicians and oncologist to discover and find the optimum dose schedule of the chemotherapy that’s reduce the tumor cells and save the patients’ health at a safe level.
Note
Research Report (M.A.) - Faculty of Engineering, Universiti Malaya, 2018.
Recommended Citation
Omar Ali, Mohammad Shindi, "Hybrid optimal control-swarm intelligence for optimization of selected cancer chemotherapy model / Omar Ali Mohammad Shindi" (2018). Student Works (2010-2019). 5253.
https://knova.um.edu.my/student_works_2010s/5253