Evolutionary feature selection methods face the challenge of “curse of dimensionality" when dealing with high-dimensional data. Focusing on this challenge, this paper studies a variable-size cooperative coevolutionary particle swarm optimization algorithm (VS-CCPSO) for feature selection. The proposed algorithm employs the idea of “divide and conquer" in cooperative coevolutionary approach, but several new developed problem-guided operators/strategies make it more suitable for feature selection problems. Firstly, a space division strategy based on the feature importance is presented, which can classify relevant features into the same subspace with a low computational cost. Following that, an adaptive adjustment mechanism of sub-swarm size is developed to maintain an appropriate size for each sub-swarm, with the purpose of saving computational cost on evaluating particles. Moreover, a particle deletion strategy based on fitness-guided binary clustering, and a particle generation strategy based on feature importance and crossover both are designed to ensure the quality of particles in the sub-swarms. We apply VS-CCPSO to 12 typical datasets, and compare it with six state-of-the-art methods. Experimental results show that VS-CCPSO has the capability of obtaining good feature subsets, suggesting its competitiveness for tackling feature selection problems with high dimensionality.

With the reformation of energy market and the development of energy intelligent technology, regional integrated energy system, as an important direction of energy system development, is tending to play an important role in the field of energy supply and demand response program. This paper presents an optimal operation model of integrated energy system considering the response of energy price. In this paper, the typical structure of the integrated energy system and the model of each system module are firstly introduced in detail, and the system model presented includes three subsystems: power supply, heating and cooling. Secondly, a complete scheduling scheme is built based on the energy consumption characteristics and system operation characteristic of the integrated energy system. The model presented could reduce the cost of system without causing a significant amount of environmental pollutions, and improve the energy efficiency of the system efficiently. Besides, the scheduling strategy proposed could also provide support for the operation strategy of integrated energy system under the energy development.

With the application of distributed generation and the development of smart grid technology, micro-grid, an economic and stable power grid, tends to play an important role in the demand side management. Because micro-grid technology and demand response have been widely applied, what Demand Response actions can realize the economic operation of micro-grid has become an important issue for utilities. In this proposed work, operation optimization modeling for micro-grid is done considering distributed generation, environmental factors and demand response. The main contribution of this model is to optimize the cost in the context of considering demand response and system operation. The presented optimization model can reduce the operation cost of micro-grid without bringing discomfort to the users, thus increasing the consumption of clean energy effectively. Then, to solve this operational optimization problem, genetic algorithm is used to implement objective function and DR scheduling strategy. In addition, to validate the proposed model, it is employed on a smart micro-grid from Tianjin. The obtained numerical results clearly indicate the impact of demand response on economic operation of micro-grid and development of distributed generation. Besides, a sensitivity analysis on the natural gas price is implemented according to the situation of China, and the result shows that the natural gas price has a great influence on the operation cost of the micro-grid and effect of demand response.