We study evolution semigroups associated with nonautonomous functional differential equations. In fact, we convert a given functional differential equation to an abstract autonomous evolution equation and then derive a representation theorem for the solutions of the underlying functional differential equation. The representation theorem is then used to study the boundedness and almost periodici...

Fuzzy time series approaches, which do not require the strict assumptions of traditional time series approaches, generally consist of three stages. These stages are called as the fuzzification of crisp time series observations, the identification of fuzzy relationships and the defuzzification, respectively. All of these stages play an important role on the forecasting performance of the model. ...

This paper presents a differential evolution algorithm to solve continuous function optimization problems. The algorithm was tested using 14 newly proposed benchmark instances in Congress on Evolutionary Computation 2005. For these benchmark problems, the problem definition files, codes and evaluation criteria are available in http://www.ntu.edu.sg/home/EPNSugan. Since these benchmarks are newl...

This paper proposed a modified differential evolution algorithm (MCDE) to solve constrained optimization problems. The methods incorporated the center point of the population into the DE algorithm, and modified the crossover factor of DE algorithm and used three simple selection criteria based on feasibility to guide the search in the feasible region. The proposed approach can be easily applied...

In some cases, the matrix available to the analyst/decision-maker is complete, but it is an invalid (not positive semi-definite) correlation matrix. There could be many reasons that give rise to such invalid matrices (Mishra, 2004). In such cases, the problem is to obtain a positive semi-definite approximate correlation matrix, which, in some sense, is closest to the given invalid matrix. A num...

Differential evolution, termed DE, is a novel and rapidly developed evolution computation in recent years. There are some advantages of DE, including simple structure, easy use and rapid convergence speed. Besides, DE can be also applied on the complex optimization problem. However, there are some issues, such as premature convergence and stagnation, remaining in DE algorithm. To overcome those...

As wind power is a mature and important renewable energy, wind power capacity forecasting plays an important role in renewable energy generation’s plan, investment and operation. Combined model is an effective load forecasting method; however, how to determine the weights is a hot issue. This paper proposed a combined model with differential evolution optimizing weights. The proposed model can ...

Distribution networks transport electric energy to the end user from distribution substations. Power utilities are looking for improved power delivery performance. The performance of the delivery system is measured by the power loss of the system. The increase in power loss increases the operating cost of the distribution system. This paper presents an algorithm to minimizing the power loss of ...

In this paper, we propose a new scale factor in differential evolution for optimization of numerical data (low dimensional data) that is both seen in algebraic and exponential form in real world scenarios. With the present work we improve the optimization of DE with real world numerical data set of the Lahi crop production of Pantnagar farm, G. B. Pant University of Agriculture and Technology, ...

The use of evolutionary strategies (ESs) to solve problems with multiple objectives (known as Vector Optimization Problems (VOPs)) has attracted much attention recently. Being population based approaches, ESs offer a means to find a set of Pareto-optimal solutions in a single run. Differential Evolution (DE) is an ES that was developed to handle optimization problems over continuous domains. Th...