Energy consumption in China is continuously increasing. Accordingly, the present paper aims to develop a hybrid procedure for energy demand forecasting in China with higher precision. The mechanism of the affecting factors of China's energy demand is investigated via path-coefficient analysis. The main affecting factors include gross domestic product, population, economic structure, urbanization rate, and energy structure. These factors are the inputs of the model with three forms: linear, exponential, and quadratic. To obtain better parameters, an improved hybrid algorithm called PSO-GA (particle swarm optimization-genetic algorithm) is proposed. This proposed algorithm differs from previous hybrids in the two ways. First, the GA and PSO approaches produce a hybrid hierarchy. Second, two information transfers are accomplished in the process. Results of this study show that China's energy demand will be 4.70 billion tons coal equivalent in 2015. Furthermore, the proposed forecast method shows its superiority compared with single optimization methods, such as GA, PSO or ant colony optimization, and multiple linear regressions. (C) 2011 Elsevier Ltd. All rights reserved.