Solving random inverse heat conduction problems using PSO and genetic algorithms

Subject Areas : Statistics

I. Hossein Zade Shahbolaghi ¹ , R. Pourgholi ² , H. Dana Mazraeh ³ , S.H. Tabasi ⁴

1 - Student, School of Mathematics and Computer Science, Damghan University, P.O.Box 36715-364, Damghan, Iran.
2 - Associate Professor, School of Mathematics and Computer Science, Damghan University, P.O.Box 36715-364, Damghan, Iran. Web address:http://faculty.du.ac.ir/pourgholi/
3 - Lecturere, School of Mathematics and Computer Science, Damghan University, P.O.Box 36715-364, Damghan, Iran.
4 - chool of Mathematics and Computer Science, Damghan University, Damghan, Iran.

Received: 2018-06-25 Accepted : 2019-06-12 Published : 1970-01-01

Keywords: ﮔﺴﺴﺘﻪﺳﺎزی, ﻣﺴﺎﯾﻞ ﻣﻌﮑﻮس, روش ﺗﻔﺎﺿﻼت ﻣﺘﻨﺎﻫﯽ, ﻣﻌﺎدﻟﻪدﯾﻔﺮاﻧﺴﯿﻞﺟﺰﻳﯽ ﺗﺼﺎدﻓﯽ, اﻟﮕﻮرﯾﺘﻢ ژﻧﺘﯿﮏ, اﻟﮕﻮرﯾﺘﻢﺑﻬﯿﻨﻪﺳﺎزی ازدﺣﺎم ذرات,

Abstract :

The main purpose of this paper is to solve an inverse random differential equation problem using evolutionary algorithms. Particle Swarm Algorithm and Genetic Algorithm are two algorithms that are used in this paper. In this paper, we solve the inverse problem by solving the inverse random differential equation using Crank-Nicholson's method. Then, using the particle swarm optimization algorithm and the genetic algorithm, we solve them. The algorithms presented in this article have advantages over other old methods that have been presented so far. Implementing these algorithms is simpler, have less run time and produce better approximation. The numerical results obtained in this paper also show that the solutions obtained for the examples presented in the numerical results section are highly accurate and have less error. All of the algorithms in this paper to obtain the desired numeric results, have been implemented on the Pentium (R) Dual core E5700 processor at 3.00 GHz.

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