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Considering DC motor's non-linear characteristics, design challenges and mechanical variation due to the operation conditions, the precise controller cannot be performed using the traditional control alone. This study presents an innovative method to optimize the direct current motor speed by decreasing the transient response characteristics such as overshoot, settling time and rise time. In this study, several PID's configurations and ANFIS controllers have been performed for direct current motor speed control. The suggested techniques were compared to each other to validate the superiority of the highest controller performance. Training data for ANFIS was generated using standard configuration, cascade and dual configuration. The dual configuration is a hybrid of two controller's data manipulated using a special algorithm. The results obtained using Matlab Simulink tools demonstrated the efficiency of ANFIS based dual PID and ANFIS based PD methods for controlling the motor speed. The overall simulation results showed that the models have a rapid response, small overshoot, superior dynamics and robustness, and static performance. The techniques presented herein outperform the traditional control methods for obtaining highly accurate control.

Gorilla troops optimizer (GTO) is a newly developed meta-heuristic algorithm, which is inspired by the collective lifestyle and social intelligence of gorillas. Similar to other metaheuristics, the convergence accuracy and stability of GTO will deteriorate when the optimization problems to be solved become more complex and flexible. To overcome these defects and achieve better performance, this paper proposes an improved gorilla troops optimizer (IGTO). First, Circle chaotic mapping is introduced to initialize the positions of gorillas, which facilitates the population diversity and establishes a good foundation for global search. Then, in order to avoid getting trapped in the local optimum, the lens opposition-based learning mechanism is adopted to expand the search ranges. Besides, a novel local search-based algorithm, namely adaptive β-hill climbing, is amalgamated with GTO to increase the final solution precision. Attributed to three improvements, the exploration and exploitation capabilities of the basic GTO are greatly enhanced. The performance of the proposed algorithm is comprehensively evaluated and analyzed on 19 classical benchmark functions. The numerical and statistical results demonstrate that IGTO can provide better solution quality, local optimum avoidance, and robustness compared with the basic GTO and five other wellknown algorithms. Moreover, the applicability of IGTO is further proved through resolving four engineering design problems and training multilayer perceptron. The experimental results suggest that IGTO exhibits remarkable competitive performance and promising prospects in real-world tasks.

Wind speed forecasting is of great importance for wind farm management and plays an important role in grid integration. Wind speed is volatile in nature and therefore it is difficult to predict with a single model. In this study, three hybrid multi-step wind speed forecasting models are developed and comparedwith each other and with earlier proposed wind speed forecasting models. The three models are based on wavelet decomposition (WD), the Cuckoo search (CS) optimization algorithm, and a wavelet neural network (WNN). They are referred to as CS-WD-ANN (artificial neural network), CS-WNN, and CS-WD-WNN, respectively. Wind speed data from two wind farms located in Shandong, eastern China, are used in this study. The simulation result indicates that CS-WD-WNN outperforms the other two models, with minimum statistical errors. Comparison with earlier models shows that CS-WD-WNN still performs best, with the smallest statistical errors. The employment of the CS optimization algorithm in the models shows improvement compared with the earlier models.

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