Differential evolution algorithm

A novel approach to the nonlinear localization problem is the differential evolution algorithm by Ruzek and Kvasnicka [2001]. The differential evolution algorithm is stmctured like a genetic algorithm and works efficiently and reliably. It is a robust global optimizer which does not use the... 

Wisittipanich W, Kachitvichyanukul V (2011) Differential evolution algorithm for job shop scheduling problem. Int J Ind Eng Manag Syst 10(3) 203-208 Wisittipanich W, Kachitvichyanukul V (2012) Two enhanced differential evolution algorithms for job shop scheduling problems. Int J Prod Res 50(10) 2757-2773... 

Zio E., Golea L. Sansavini G. 2012. Optimizing protections against cascades in network systems A modified binary differential evolution algorithm. Reliability Engineering System Safety, 103 72-83. 

DE Differential evolution EDP Evolutionary distribution plot EPP Evolutionary progress plot ETP Evolutionary trajectory plot GA Genetic algorithm PGA Parallel genetic algorithm... 

A new evolutionary algorithm, called differential evolution DEf has been successfully applied to powder data crystal structure solution. DEis a simpler and more deterministic method with respect to GA, and is based on the generation of children from a unique parent. In particular, each member of the population creates a child having the chromosome ... 

Differential evolution (DE) is a branch of evolutionary algorithms developed by Storn and Price (1997) for optimization problems over continuous domains. DE is characterized by representing the variables by real numbers and by its three-parents crossover. At the selection stage,... 

Rahimpour MR, Parvasi P, Setoodeh P. Dynamic optimization of a novel radial-flow, spherical-bed methanol synthesis reactor in the presence of catalyst deactivation using differential evolution (DE) algorithm. International Journal of Hydrogen Energy 2009 34 6221-6230. 

SEPaT (Simple Evolutionary Parameter Tuning) is an implementation of the Meta-Optimization paradigm, introduced in [21]. In this case the algorithm used as Tuner-MH is Differential Evolution (DE [22]). 

Based upon the exposition in this section, a robust algorithm using the latest theory of differential evolution is constructed for the identification of differential hysteresis. As explained before, a two-stage procedure is adopted whereby a crude value of the vector pi in Eq. 15 is first obtained before the optimal value of p2 in Eq. 16 is computed. In simulations reported in this entry, the population size P is set to 100, while both the scaling factor F and the crossover constant CR are set to 0.5. The computing time is dependent on the amount of input data used for identification. The runtime can be reduced appreciably if some of the insensitive parameters are fixed and parallel computing is utilized. 

A robust identification algorithm based upon the generalized Bouc-Wen model and the theory of differential evolution can be used to generate practical models of hysteresis of degrading structures. Differential evolution is not sensitive to a moderate level of input noise. 

With the introduction of Gear s algorithm (25) for integration of stiff differential equations, the complete set of continuity equations describing the evolution of radical and molecular species can be solved even with a personal computer. Many models incorporating radical reactions have been pubHshed. 

These differential equations depend on the entire probability density function / (x, t) for x(t). The evolution with time of the probability density function can, in principle, be solved with Kolmogorov s forward equation (Jazwinski, 1970), although this equation has been solved only in a few simple cases (Bancha-Reid, 1960). The implementation of practical algorithms for the computation of the estimate and its error covariance requires methods that do not depend on knowing p(x, t). 

A systematic stepwise method for numerical integration of a rate expression [indeed, of any differential equation y = f(x,y) with an initial value y(Xo) = Vo] to determine the time evolution of the rate process. See also Numerical Computer Methods Numerical Integration Stiffness Gear Algorithm... 

A computational method was developed by Gillespie in the 1970s [381, 388] from premises that take explicit account of the fact that the time evolution of a spatially homogeneous process is a discrete, stochastic process instead of a continuous, deterministic process. This computational method, which is referred to as the stochastic simulation algorithm, offers an alternative to the Kolmogorov differential equations that is free of the difficulties mentioned above. The simulation algorithm is based on the reaction probability density function defined below. 

Consider an algorithm of the kinetic schemes of analysis in respect to stability of the stationary states for a general case when the system has more than one internal variable. Let us say that the system has two internal parameters, Y and Z, and the evolution of these parameters is described by a set of differential equations ... 

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