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Title

Evaluation of the performance of constrainted optimization methods using PSO and DE with application to the drug therapy of cancer

Year: 1396
COI: ICRSIE03_085
Language: EnglishView: 257
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Authors

f Heydarpoor - Department of Applied Mathematics, Yazd University, Yazd, Iran
S. M. Karbassi - Department of Applied Mathematics, Yazd University, Yazd, Iran
N. Bidabadi - Department of Applied Mathematics, Yazd University, Yazd, Iran

Abstract:

We present an optimal control strategy for nonlinear systems with application to the drug therapy of cancer. The tumour growth model is represented by a system of equations from population dynamics which is based on the competition between normal cells and tumour cells. There are quite a number of modern optimization algorithms proposed in the last two decades to solve optimization problems. Particle Swarm Optimization (PSO) and differential evolution (DE) are among the well-known modern optimization algorithms. This paper presents a comparative study for min-max constrained optimization using PSO and DE. The comparison is performed on eight benchmark functions f1-f8[22]. New findings have been discovered for the PSO algorithm and the comparison results in this report show that DE generally is better than PSO in term of solution accuracy and robustness in almost all the problems. Generally, from the numerical results and graphic illustrations, we can demonstrate that DE is more efficient and robust compare to PSO, although PSO gives good results in some cases.

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This Paper COI Code is ICRSIE03_085. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

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Heydarpoor, f and Karbassi, S. M. and Bidabadi, N.,1396,Evaluation of the performance of constrainted optimization methods using PSO and DE with application to the drug therapy of cancer,3rd.International Conference on Researches in Science & Engineering,https://civilica.com/doc/677422

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Type of center: دانشگاه دولتی
Paper count: 14,661
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