computational intelligence

Congestion of vehicles in metropolitan areas has increasingly raised concerns about the negative consequences for human health, the environment and the economy. The movement of public and private vehicles in the city to send goods, urban traffic or public transport, intensifies traffic and result in social anomalies, release of pollutants, increasing the psychological impact and cost of urban living, waste of fossil fuels, and ultimately degrading quality of urban life. In this regard, many researches have addressed this problem amongst, the Computational Intelligence (CI)-based algorithms are in the center of attention. In this paper, we have reviewed the most recent and influential CI-based methods presented for solving the Vehicle Transportation Routing System (VTRS) problem.

Nowadays, electricity load forecasting, as one of the most important areas, plays a crucial role in the economic process. What separates electricity from other commodities is the impossibility of storing it on a large scale and cost-effective construction of new power generation and distribution plants. Also, the existence of seasonality, nonlinear complexity, and ambiguity pattern in electricity data set makes it more difficult to forecast by using the traditional methods. Therefore, new models, computational intelligence and soft computing tools and combining models are the most accurate and widely used methods for modeling the complexity and uncertainty in the data set. In this paper, a parallel optimal hybrid model using computational intelligence tools and soft computations is proposed to forecast the electricity load forecasting. The main idea of this model is the use of the advantages of the individual models in the modeling of complex systems in a structure and elimination of  the limitations of them, simultaneously. The experimental results indicate that the proposed hybrid model has a higher performance accuracy in comparison to iterative suboptimal hybrid models and its computational cost is lower than the other hybrid models; also, the proposed model can achieve more accurate results, as compared with its component and some other seasonal hybrid models.