In the present paper, dynamic identification problem of a FE structure with unknown parameters is solved by global search method. Response surface methodology is introduced in Differential Evolution algorithm to improve the performance of the algorithm. Differential evolution (DE) is an evolutionary algorithm where N different vectors collecting the parameters of the system are chosen randomly ...

Back propagation neural network is successfully used in various fields, particularly in pattern recognition. Despite numerous applications, back propagation neural network`s design and optimization are developed by trial-and-error process, which is time-consuming. On the other hand, although a dataset may contain many features, these features may not be useful in a back propagation neural netwo...

Differential Evolution (DE) has been proven to be an efficient and robust algorithm for many real optimization problems. However, it still may converge toward local optimum solutions, need to manually adjust the parameters, and finding the best values for the control parameters is a consuming task. In this paper that proposed a dimension perturb strategy and self-adaptive F value in original DE...

This paper presents an efficient power conditioning system for PV power system generation. The proposed Photovoltaic Power Conditioning System (PVPCS) used a single-stage single-phase Z-Source Inverter (ZSI) integrate with a relatively new evolutionary optimization algorithm known as the Differential Evolution (DE) as the Maximum Power Point (MPP) Tracker. Utilization of single-stage power cond...

This paper presents an improved Differential Evolution algorithm (IDE). It is aimed at improving its performance in estimating the relevant parameters for metabolic pathway data to simulate glycolysis pathway for yeast. Metabolic pathway data are expected to be of significant help in the development of efficient tools in kinetic modeling and parameter estimation platforms. Nonetheless, due to t...

Differential evolution (DE) is a powerful and simple algorithm for singleand multi-objective optimization. However, its performance is highly dependent on the right choice of parameters. To mitigate this problem, mechanisms have been developed to automatically control the parameters during the algorithm run. These mechanisms are usually a part of a unified DE algorithm, which makes it difficult...

Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems. Several extensions of differential evolution have been adopted in solving constrained and nonconstrained multiobjective optimization problems, but in this study, the third version of generalized differential evolution (GDE) is used for solving practical engineer...

This paper presents the application of differential evolution algorithm to obtain optimal switching angles for a single-phase seven-level to improve AC voltage quality. The proposed inverter in this article is composed of two H-bridge cells with non-equal DC voltage sources in order to generate multiple voltage levels. Selective harmonic elimination pulse width modulation (SHPWM) strategy is us...

Design of an optimal controller requires the optimization of differential evolution performance measures that are often no commensurable and competing with each other. Being a population based approach; Differential Evolution (DE) is well suited to solve designing problem of TCSC – based controller. This paper investigates the application of DE-based multi-objective optimization technique for t...

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding infor...