New Energetic Selection Principle in Differential Evolution

Feoktistov, Vitaliy; Janaqi, Stefan

doi:10.1007/1-4020-3675-2_18

Cited by 17 publications

(30 citation statements)

References 7 publications

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“…Differential Evolution (DE) [7][8][9] is a population based metaheuristic method which has become more and more popular for problems that either classical continuous and combinatorial methods fail to solve. Its virtues are that a) they do not require special conditions for the properties of the objective functions and the constrains, b) they can be applied in both continuous and combinatorial problems and c) they are extensible on multimodal and multiobjective optimization.…”

Section: Differential Evolutionmentioning

confidence: 99%

“…The difference vector can be formed either randomly or by using the values of the objective function to determine a direction which can be viewed as an imitation of the gradient function. Combination of these selections in the difference vector formation has been proposed too [9]. Thee most popular strategies are the following:…”

Section: Differential Evolutionmentioning

confidence: 99%

“…The algorithm iteration terminates when a stopping criterion, such as a maximum number of generations is reached or VT R criterion is satisfied [9].…”

Section: Differential Evolutionmentioning

confidence: 99%

See 2 more Smart Citations

On the modification of Differential Evolution strategy for the construction of Runge Kutta pairs

Famelis

Tsitouras²

2016

MATEC Web of Conferences

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Abstract. Among the most popular methods for the solution of the Initial Value Problem are the Runge-Kutta (RK) pairs. These methods can be derived by solving a system of nonlinear equations after admitting various simplifying assumptions. The more simplifying assumptions we consider the more we narrow the solution space. In [1] Tsitouras presented an algorithm for the construction of Runge-Kutta pairs of orders 5 and 4 satisfying only the so called "first column simplifying assumption". In [2] Famelis and Tsitouras have studied the ability of Differential Evolution techniques to find solutions satisfying all the order conditions needed for the derivation of orders 5 and 4 pairs. In this work we propose an modification on the Differential Evolution strategy for the same problem. The current study will be a guide to the construction of other classes of RK that have not been presented in the literature.

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Section: Differential Evolutionmentioning

confidence: 99%

Section: Differential Evolutionmentioning

confidence: 99%

See 1 more Smart Citation

On the modification of Differential Evolution strategy for the construction of Runge Kutta pairs

Famelis

Tsitouras²

2016

MATEC Web of Conferences

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“…A trial and error algorithm was performed to obtain these unknown parameters [6]. In [7][8], the unknown parameters of the PV module were obtained using differential evolution (DE) algorithm. The values for I o , I Ph and R sh are analytically computed while a and R s are computed by using DE.…”

Section: Introductionmentioning

confidence: 99%

Determination of Unknown Parameters of Photovoltaic Module Using Genetic Algorithm

Abdelqawee

Yousef

Hasaneen

et al. 2016

IJEECS

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In this paper, the unknown parameters of the photovoltaic (PV) Keywords: genetic algorithm, photovoltaic, unknown parametersCopyright © 2016 Institute of Advanced Engineering and Science. All rights reserved. IntroductionThe basic device of a photovoltaic (PV) system is the PV cell. PV cell is a semiconductor diode whose p-n junction is effected by light [1]. Cells may be grouped in series and parallel to form a module. For large power applications, modules are grouped in series and parallel to form an array.Manufacturer datasheets of the PV modules does not give all the required parameters to obtain the current-voltage (I-V) characteristics of the module analytically. These unknown parameters are the module intrinsic series resistance, R s , parallel resistance, R sh , the ideality factor, a, the diode reverse-bias saturation current, I o , and the module photo current, I ph . These parameters were obtained, in many previous studies by minimizing the absolute error between the PV module characteristics datasheet and the mathematical model of the PV module [2][3][4][5][6][7]. This minimizing technique was processed by different methods.Curve fitting method was used to obtain these unknown parameters [2]. This method proposed more sophisticated model using an extra diode (double-exponential model) [3]. The parameters Rs and Rsh were obtained using the iterative solution to minimize the error between the experimental maximum power given in the data sheet and that obtained from the mathematical model [3][4]. The accuracy of this method depends on the proper assumption of Rs and Rsh. The parameters "R s , R sh and a" for the module were obtained by using NewtonRaphson method [5]. The solution obtained from this method depended on the correct assumption of the initial values of the unknown parameters to insure convergence of the solution. A trial and error algorithm was performed to obtain these unknown parameters [6]. In [7][8], the unknown parameters of the PV module were obtained using differential evolution (DE) algorithm. The values for I o , I Ph and R sh are analytically computed while a and R s are computed by using DE.In this paper, the unknown parameters, Rs, Rsh, a, Io and Iph are determined using GA method at standard test condition (STC) [3]. Also, I-V and power-voltage (P-V) characteristics are obtained at various cell temperature and sunlight irradiance conditions. The characteristics obtained from this proposed method are compared with those given in the module datasheet.

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“…Differential evolution (DE), proposed by Storn and Price, is a population-based optimization technique [1] used to obtain a global minimum solution in continuous design variable problems [2][3][4]. DE, as well as other population-based optimization techniques, is also considered as a stochastic optimization technique.…”

Section: Introductionmentioning

confidence: 99%

Discrete Differential Evolution for Mixed Discrete Non-Linear Problems

Kitayama¹,

Arakawa²,

Yamazaki³

2012

JCEA

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Differential evolution (DE) is a global optimizer for continuous design variables. To enhance DE, it is necessary to handle discrete design variables. In this paper, a discrete differential evolution (DDE) algorithm is proposed to handle discrete design variables. The proposed DDE is based on the DE/1/rand/bin method. In the proposed DDE, the mutation ratio is regarded as the exchange probability, and thus, no modifications of DE/1/rand/bin are required. In addition, in order to maintain diversity through the search process, we initialize all search points. By introducing the initialization of all search points, global or quasi-optimum solution can be found. We validate the proposed DDE by applying it to several benchmark problems.

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New Energetic Selection Principle in Differential Evolution

Cited by 17 publications

References 7 publications

On the modification of Differential Evolution strategy for the construction of Runge Kutta pairs

On the modification of Differential Evolution strategy for the construction of Runge Kutta pairs

Determination of Unknown Parameters of Photovoltaic Module Using Genetic Algorithm

Discrete Differential Evolution for Mixed Discrete Non-Linear Problems

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