in this paper, we study the numerical solution of hybrid fuzzy differential equations by using differential transformation method (dtm). this is powerful method which consider the approximate solution of a nonlinear equation as an infinite series usually converging to the accurate solution. several numerical examples are given and by comparing the numerical results obtained from dtm  and  predi...

We study the wave propagation in a curved chain of spherical particles constrained by elastic guides under the axial impact of a falling mass. We characterize the force transmission properties of the chain by varying the striker's mass and the chain's curvature. Experimental tests demonstrate amplitude-dependent attenuation of compressive waves propagated through the curved chain. We also show ...

This paper describes the impact of the distance and the size of the corona ring, on the magnitude anddistribution of electrical potential across the polymer insulators. The procedure is based on finiteelement method numerical analysis and stochastic optimization algorithm of differential evolution(DE). The optimal selection of corona ring has a significant impact on the control of potential and...

Differential Evolution (DE) is a simple and efficient optimizer, especially for continuous global optimization. Over the last few decades, DE has often been employed for solving various engineering problems. At the same time, the DE structure has some limitations in the complicated problems. This fact has inspired many researchers to improve on DE by proposing modifications to the original algo...

The general purpose optimization method known as Differential Evolution (DE) has a number of parameters that determine its behaviour and efficacy in optimizing a given problem. This paper gives a list of good choices of parameters for various optimization scenarios which should help the practitioner achieve better results with little effort.

Created in 1994, Differential Evolution (DE) is relatively new but already has been proven to perform well for optimisation in static environments. But with many realworld optimisation problems having non-static environments, how well can DE perform in these situations? What changes can be done to allow DE to perform better in dynamic environments?

Differential Evolution (DE) is a simple and efficient optimizer, especially for continuous global optimization. Over the last few decades, DE has often been employed for solving various engineering problems. At the same time, the DE structure has some limitations in the complicated problems. This fact has inspired many researchers to improve on DE by proposing modifications to the original algo...

in this article differential transformation method (dtms) has been used to solve neutral functional-differential equations with proportional delays. the method can simply be applied to many linear and nonlinear problems and is capable of reducing the size of computational work while still providing the series solution with fast convergence rate. exact solutions can also be obtained from the kno...

with the rapid development of the internet, the amount of information and data which are produced, are extremely massive. hence, client will be confused with huge amount of data, and it is difficult to understand which ones are useful. data mining can overcome this problem. while data mining is using on cloud computing, it is reducing time of processing, energy usage and costs. as the speed of ...