As the penetration rate of solar energy in the grid continues to enhance, solar power photovoltaic generation forecasts have become an indispensable aspect of mechanism mobilization and maintenance of the stability of...
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As the penetration rate of solar energy in the grid continues to enhance, solar power photovoltaic generation forecasts have become an indispensable aspect of mechanism mobilization and maintenance of the stability of the power system. In this regard, many researchers have done a lot of study, and put forward some predictive models. However, many individual prediction systems only consider the prediction accuracy rate without further considering the prediction utility and stability. To fill this gap, a comprehensive system is designed in this paper, which is on the basis of automatic optimization of variational mode decomposition mechanism, and the weight of system is determined by multiobjective intelligent optimizationalgorithm. In particular, it can be proved theoretically that the developed predictive system can achieve the pareto optimal solution. And the designed system is shown to be very effective in forecasting the 2021 photovoltaic power data obtained from Belgium. The empirical study reports that the combination of variational mode decomposition strategy based on genetic algorithm and multiobjective grasshopper optimizationalgorithm is found to be the satisfactory strategy to optimize the predictive system compared with other common mechanism. And the results of several numerical studies show that the designed predictive system achieves the superior performance as compared to the control systems, and in multi-step forecasting, the designed system has better stability than the comparison systems.
In order to comprehensively measure these two indicators and make reasonable portfolio investment decisions, the author proposes using swarm intelligence optimizationalgorithm- artificial fish swarm algorithm to solv...
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In order to comprehensively measure these two indicators and make reasonable portfolio investment decisions, the author proposes using swarm intelligence optimizationalgorithm- artificial fish swarm algorithm to solve multi-objective investment portfolio problems, and has achieved good results. In order to verify the effectiveness and superiority of the artificial fish school algorithm, the author used MATLAB programming to conduct simulation experiments using AFSA algorithm and genetic algorithm (GA), and compared the results obtained. The results show that compared to the GA algorithm, the artificial fish school algorithm can obtain better investment portfolio decision-making solutions for investing in five types of assets, making investment returns as large as possible while minimizing risks, indicating the efficiency and superiority of the algorithm in solving multi-objective investment portfolio problems.
Hardened AISI 4340 steel is extensively utilised in engineering industries. Owing to the fact that thermo-mechanical loads are applied to surface layers during the hard machining process, severe changes occur at the s...
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Hardened AISI 4340 steel is extensively utilised in engineering industries. Owing to the fact that thermo-mechanical loads are applied to surface layers during the hard machining process, severe changes occur at the surface of the machined workpiece. These changes can affect some mechanical properties of the material, including microhardness, and residual stresses generation in material. Therefore, it is significantly influenced by process conditions and there is not any study in literature to extensively evaluate and optimise different indications of microhardness alteration after hard machining of AISI 4340 Steel. In this study, the influence of machining parameters was firstly studied on the microhardness distribution at machining of hardened AISI 4340 steel by experimental tests. Then, the optimal surface microhardness and depth of the affected layer were assessed using the intelligent techniques. In this regard, the optimal process conditions were simultaneously determined using the combination of the Artificial Neural Networks (ANNs) and Non-dominated Sorting Genetic algorithm (NSGA-II). The results indicated that, both depth of the affected layer and surface microhardness were obtained in optimal state when the cutting speed changes from 720 to 600 (Rpm) and feed rate and depth of cut are 0.05 (mm/rev) and 0.4 (mm), respectively.
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