A low carbon management model for regional energy economies based on blockchain technology

As the issue of sustainable energy development becomes more and more important in national economic construction, the potential dangers of climate change are gradually attracting widespread attention from countries around the world. In order to better carry out the low-carbon management of the regional energy economy, based on the analysis of the characteristics of blockchain technology, the present study utilized this technology to achieve intelligent and digital management of carbon emissions, and established a carbon emission prediction system. The cuckoo algorithm is used to improve the long-term memory network, and the improved algorithm is used in carbon emission prediction and management. The experimental results show that the improved Long Short Term Memory networks are close to the target precision in 240 iterations, and the convergence speed is fast. In the short-term regional carbon emission prediction, the average absolute error of the method is only 2%, which is highly consistent with the actual carbon emission. In the long-term carbon emission prediction, the average prediction accuracy of the upgraded long-term short-term memory networks can reach 97.26%, and the running time is only 19.46s. With high precision and running efficiency, the upgraded Long Short Term Memory networks can efficiently monitor regional carbon emission and provide a technical reference for the low-carbon management of the regional power industry.

Parameter setting of meta-heuristic algorithms: a new hybrid method based on DEA and RSM

The parameter setting of meta-heuristic algorithms is one of the most effective issues in the performance of meta-heuristic algorithms and is usually done experimentally which is very time-consuming. In this research, a new hybrid method for selecting the optimal parameters of meta-heuristic algorithms is presented. The proposed method is a combination of data envelopment analysis method and response surface methodology, called DSM. In addition to optimizing parameters, it also simultaneously maximizes efficiency. In this research, the hybrid DSM method has been used to set the parameters of the cuckoo optimization algorithm to optimize the standard and experimental functions of Ackley and Rastrigin. In addition to standard functions, in order to evaluate the performance of the proposed method in real problems, the parameter of reverse logistics problem for COVID-19 waste management has been adjusted using the DSM method, and the results show better performance of the DSM method in terms of solution time, number of iterations, efficiency, and accuracy of the objective function compared to other.

Trajectory tracking of a quadrotor using a robust adaptive type-2 fuzzy neural controller optimized by cuckoo algorithm

This paper proposes an adaptive and robust adaptive control strategy based on type-2 fuzzy neural network (T2FNN) for tracking the desired trajectories of a quadrotor. The designed methods can control both the position and the orientation of a quadrotor. Contrary to common sliding mode controllers (SMCs), the robust adaptive trajectory tracking scheme presented here is based on SMC with exponential reaching law; which helps reduce the phenomenon of chattering. Moreover, parameters including the gains of sliding surfaces, are optimized by cuckoo optimization algorithm (COA), and the results are compared with those obtained by genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO). The designed methods in this study are called adaptive T2FNN controller and the exponential SMC (ESMC)-T2FNN. The law for updating the T2FNN is obtained online by using the Lyapunov stability theory. Considering undesired factors such as uncertainties, external disturbances and control signal saturation, the results of our controllers are compared with those of the adaptive type-1 fuzzy neural network controller (T1FNN) and ESMC-T1FNN. The extensive simulations demonstrate the effectiveness of the proposed COA-based ESMC-AT2FNN approach compared to the other tested techniques (i.e. GA, PSO and ACO) in terms of the improved transient and steady-state trajectory-tracking performance. The mean and standard deviation values concerning the COA are obtained through statistical analyses at 0.00006173 and 0.000092, respectively. This paper also examines the complexity of COA in optimizing the trajectory tracking control of quadrotor and investigates the effects of COA parameters on optimization results. The stable performance of the cuckoo algorithm is demonstrated by varying its parameters and analyzing the obtained results. These results also show the convergence of COA for the considered problem.

Providing an imputation algorithm for missing values of longitudinal data using Cuckoo search algorithm: A case study on cervical dystonia

Background

Missing values in data are found in a large number of studies in the field of medical sciences, especially longitudinal ones, in which repeated measurements are taken from each person during the study. In this regard, several statistical endeavors have been performed on the concepts, issues, and theoretical methods during the past few decades.

Methods

Herein, we focused on the missing data related to patients excluded from longitudinal studies. To this end, two statistical parameters of similarity and correlation coefficient were employed. In addition, metaheuristic algorithms were applied to achieve an optimal solution. The selected metaheuristic algorithm, which has a great search functionality, was the Cuckoo search algorithm.

Results

Profiles of subjects with cervical dystonia (CD) were used to evaluate the proposed model after applying missingness. It was concluded that the algorithm used in this study had a higher accuracy (98.48%), compared with similar approaches.

Conclusion

Concomitant use of similar parameters and correlation coefficients led to a significant increase in accuracy of missing data imputation.