Previous studies have shown that differential evolution is an efficient, effective and robust evolutionary optimization method. However, the convergence rate of differential evolution in optimizing a computationally expensive objective function still does not meet all our requirements, and attempting to speed up DE is considered necessary. In this paper, a new local search operation, trigonomet...

Renewable energy power generation could increase the uncertainties of power system with intermittent and stochastic performance, which has significant effect on Available Transfer Capability (ATC). With the rapid development of renewable energy power generation technology as well as the gradual maturity of power market, it has become essential to research the effect specially. The wind generato...

The objective of the paper is to demonstrate the calculation of micromorphic continuum stress measures based directly on three-dimensional (3D) ellipsoidal discrete element (DE) simulations over representative volume elements (RVE) within the DE domain representing granular media. We will demonstrate these calculations for two simulations: (1) pile penetration, and (2) cavity expansion. All cal...

Nonlinear constrained optimization problems are encountered in many scientific fields. To utilize the huge calculation power of current computers, many mathematic models are also rebuilt as optimization problems. Most of them have constrained conditions which need to be handled. Borrowing biological concepts, a study is accomplished for dealing with the constraints in the synthesis of a four-ba...

In this update to the 2012 year's study, the authors examine statutes that regulate, license, and enforce investigative functions in each US state. As before, the authors find that very few state statutes explicitly differentiate between Private Investigators and Digital Examiners. However, there is a small trend in which some states are changing definitions or moving to exempt DE from PI licen...

From a learning perspective, the mutation scheme in differential evolution (DE) can be regarded as a learning strategy. When mutating, three random individuals are selected and placed in a random order. This strategy, however, probably suffers some drawbacks which can slow down the convergence rate. To improve the efficiency of classic DE, this paper proposes a differential evolution based on i...

Over the years, several metaheuristics have been developed to solve hard constrained and unconstrained optimization problems. In general, a metaheuristic is proposed and following researches are made to improve the original algorithm. In this paper, we evaluate a not so new metaheuristic called differential evolution (DE) to solve constrained engineering design problems and compare the results ...

In constrained optimisation, the augmented Lagrangian method is considered as one of the most effective and efficient methods. This paper studies the behaviour of augmented Lagrangian function (ALF) in the solution space and then proposes an improved augmented Lagrangian method. We have shown that our proposed method can overcome some of the drawbacks of the conventional augmented Lagrangian me...

In this paper a new method for recognition of 2D occluded shapes based on neural networks using generalized differential evolution training algorithm is proposed. Firstly, a generalization strategy of differential evolution algorithm is introduced. And this global optimization algorithm is applied to train the multilayer perceptron neural networks. The proposed algorithms are evaluated through ...