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Summary: The present paper describes a new optimal design for an autonomous renewable energy‐based CHP system for a remote area in Zhidoi county, China. The configuration contains different parts of the electric heater (EH), photovoltaic‐thermal (PV/T), wind turbines (WTs), thermal energy storage (TES), and electrical energy storage (EES). The total annual cost (TAC) is utilized as a cost function of the system configuration and the idea is to minimize this function to access an optimal configuration. Due to the complicated nonlinear nature of this system, a metaheuristic‐based method, called Improved Marine Predators Algorithm (IMPA) has been introduced and designed. The reason for using this new algorithm is to cover the main drawbacks of most metaheuristics like better accuracy and higher convergence speed. To show the capability of the designed IMPA, it is validated by some different new metaheuristics from the literature. Afterward, the algorithm is used for system configuration optimization. Some sensitivity analysis is also investigated to shoe the method capability and the final results confirm the high ability of the proposed method for providing an optimal renewable energy‐based CHP system. The final results show a $56 307.74 value of TAC. The total efficiency of the system for the winter and the summers are 66% and 61%, respectively and the minimum total annual cost happens at AD = 0.8983. Finally, the minimum value of the total annual cost is achieved by the proposed method with 922.4 kWh. [ABSTRACT FROM AUTHOR]

In view of the miniaturization and decentralization characteristics of agricultural equipment factories in China, agricultural equipment manufacturing is well suited to the cloud manufacturing model, but there is no specific research on cloud services optimization for it. To fill the research gap, a cloud service optimization method is proposed in this paper. For the optimization model, the dynamic coefficient strategy and the reliability feedback update strategy are added to the mathematical model to strengthen the applicability of farming season. As optimization algorithm, a dynamic artificial ant-bee colony algorithm (DAABA) based on artificial ant colony algorithm and bee colony algorithm is presented. The optimal fusion evaluation strategy is used to save optimization time by reducing the useless iteration, and the iterative adjustment threshold strategy is adopted to improve the accuracy of cloud service by increasing the size of bee colony. Finally, the performance of DAABA is verified to be more superior by comparing with other algorithms. [ABSTRACT FROM AUTHOR]

Stock index forecasting is an important tool for both the investors and the government organizations. However, due to the inherent large volatility, high noise, and nonlinearity of the stock index, stock index forecasting has been a challenging task for a long time. This paper aims to develop a novel hybrid stock index forecasting model named BSO-GNN based on the brain storm optimization (BSO) approach and the grey neural network (GNN) model by taking full advantage of the grey model in dealing with data with small samples and the neural network in handling nonlinear fitting problems. Moreover, the new developed BSOGNN, which initializes the parameters in grey neural network with the BSO algorithm, has great capability in overcoming the deficiencies of the traditional GNN model with randomly initialized parameters through solving the local optimum and low forecasting accuracy problems. The performance of the proposed BSO-GNN model is evaluated under the normalization and nonnormalization preprocessing situations. Experimental results from the Shanghai Stock Exchange (SSE) Composite Index, the Shenzhen Composite Index, and the HuShen 300 Index opening price forecasting show that the proposed BSO-GNN model is effective and robust in the stock index forecasting and superior to the individual GNN model. [ABSTRACT FROM AUTHOR]