Computational Research Progress in Applied Science & Engineering



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Volume 04,Issue 01
   Volume & Issue: Volume 04,Issue 01



Presenting a New Method to Solve Partial Differential Equations using a Group Search Optimizer Method (GSO)

  Volume 04,Issue 01

 

Author :

Akram Javadi, Nasser Mikaeilvand, Hassan Hosseinzde

 

Abstract :

The analytical methods are not able to solve some of the differential equations. For this reason, the approximate methods like intelligent algorithms are used to reach an answer with the error as low as possible. In this article, a new method has been presented to solve ordinary and partial differential equations (PDEs) which is based on a hierarchical method and the group search optimizer (GSO)-based graph theory. GSO algorithm is an optimization method inspired from the searching behavior of animals and has been implemented based on the producer-scrounger model. It should be noted that according to the aforementioned pattern, the scan mechanisms of animals are symbolically considered here to solve optimization problems. This algorithm has the ability to expand the search space of the genetic algorithm (GA) due to the presence of ranger operator and on the other hand, it has a high convergence speed to find the best answers due to use scrounger model. Therefore, in this article, the best possible answer for PDEs is provided from all possible answers with the least error.



 

Keywords :

Partial differential equations (PDE), GSO algorithm, Dependent error minimization, Graph theory.

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