Among the existing GDP forecasting methods, time series and regression model are two most commonly used methods. However, traditional macroeconomic models unable to accurately achieve optimal forecasts of highly complex nonlinear dynamic systems due influence multiple confounding factors. In order solve above problems, a economic based on an improved RBF neural network is proposed. First, main ...

As energy systems become increasingly complex, there is a growing need for sustainable and efficient management strategies that reduce greenhouse gas emissions. In this paper, multi-energy (MES) have emerged as promising solution integrates various sources enables sharing between different sectors. The proposed model based on using an Attractive Repulsive Shuffled Frog-Leaping (ARSFL) algorithm...

In order to minimize power losses caused by high current and improve the voltage profile in the network distribution, the introduction of dispersed generations also called productions decentralized in distribution network (DG) plays an important role. The installation of DGs directly affects the power requested or purchased. The sizing and placement of DGs in the system must be optimal because ...

This paper proposes Hybrid SFL-Bees Algorithm that combines strengths of Shuffled Frog Leaping Algorithm (SFLA) and Bees Algorithms (BA). While SFLA can find optimal solutions quickly because of directive searching and exchange of information, BA has higher random that make it easily escape local optima to find global solutions. Thus Hybrid SFL-Bees Algorithm is able to find optimal solutions q...

Reducing energy consumption is becoming very important in order to keep battery life and lower overall operational costs for heterogeneous real-time multiprocessor systems. In this paper, we first formulate this as a combinatorial optimization problem. Then, a successful meta-heuristic, called Shuffled Frog Leaping Algorithm (SFLA) is proposed to reduce the energy consumption. Precocity remissi...

In order to realize the fault diagnosis of the polyvinyl chloride (PVC) polymerization kettle reactor, a rough set (RS)–probabilistic neural networks (PNN) fault diagnosis strategy is proposed. Firstly, through analysing the technique of the PVC polymerization reactor, the mapping between the polymerization process data and the fault modes is established. Then, the rough set theory is used to t...