System sizing of hybrid<scp>solar‐fuel cell battery</scp>energy system using artificial bee colony algorithm with predator effect

Singh, Shakti; Singh, Nirbhowjap; Gupta, Aditi

doi:10.1002/er.7526

Cited by 12 publications

(3 citation statements)

References 51 publications

(73 reference statements)

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“…Different bees can adapt well to the environment, and this mechanism of division of labor and cooperation organization does not require special information about the problem; By comparing the advantages and disadvantages of the problem, the ABC algorithm has an excellent global search ability, and the algorithm has a fast Rate of convergence speed, which is widely used in different fields such as traveling salesman problem, signal deployment problem, power and water conservancy scheduling problem, parameter optimization, image segmentation, etc. [16][17].…”

Section: A Fuzzy Pid Control In View Of Abc Algorithmmentioning

confidence: 99%

Permanent Magnet Motor Control System Based on Fuzzy PID Control

Sha,

Chen

2024

IJACSA

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Although the traditional permanent magnet synchronous motor control system is simple and convenient, the control of speed and accuracy is often affected by external interference, which impacts the dynamic and static performance requirements. Therefore, this study attempts to introduce fuzzy rules to improve the proportional integral differential control method, and further integrate intelligent optimization algorithms into the fuzzy proportional integral differential control method to construct an efficient and feasible permanent magnet synchronous motor control method. The simulation experiment demonstrates that under fuzzy proportional integral differential control, there is no overshoot in the waveform when facing changes in load, and the tuning time increases from 0.01 seconds to 0.12 seconds. The steady-state error of speed control is small, and there is no obvious oscillation in the waveform. Fuzzy control enhances the control system. After the optimization of the artificial bee colony algorithm, the control system has a faster speed response, with the overshoot diminished from 11.2% to 3.1%, and the adjustment time reduced from 0.27 seconds to 0.19 seconds, enhancing its adaptability. Under load regulation, the optimized control system speed response curve responds in a timely manner without obvious overshooting and oscillating changes. Optimizing variable universe fuzzy proportional integral differential control enables the control system for having better static and dynamic performance, and enhances the adaptability and follow-up of the control system. The current curve starts to stabilize at 0.04s, overcoming the control system oscillations early. The speed response curve and the motor torque curve are improved by the optimized variable domain theory, and the amount of overshoot is significantly reduced. The research and design of a permanent magnet motor control system has practical significance for improving the application performance and adaptability of permanent magnet motors.

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Section: A Fuzzy Pid Control In View Of Abc Algorithmmentioning

confidence: 99%

Permanent Magnet Motor Control System Based on Fuzzy PID Control

Sha,

Chen

2024

IJACSA

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“…The artificial bee colony (ABC) algorithm is an imitation of the honey harvesting process of a bee colony [21]. It is assumed that the nectar source location is: 𝑋 𝑖 = (𝑋 𝑖1 , 𝑋 𝑖2 , ⋯ , 𝑋 𝑖𝐷 ), then the initial solution is obtained:…”

Section: ) Artificial Bee Colony Algorithmmentioning

confidence: 99%

The Prediction of Douyin Live Sales based on Neural Network Algorithms

Sun,

De,

Zheng

2023

HighTech. Innov. J.

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This paper aims to optimize neural network algorithms in order to improve their predictive performance and meet the demand for Douyin live sales forecasting. The Douyin live data of Florasis between January 2022 and June 2022 were collected from Huitun data and preprocessed. Using the Pearson correlation coefficient, six factors that are highly correlated with live sales were selected for subsequent prediction. This paper briefly introduces the back-propagation neural network (BPNN) algorithm and analyzes its parameter optimization methods, including particle swarm optimization (PSO), the artificial bee colony (ABC) algorithm, and the beetle antler search (BAS) algorithm. Then, an improved beetle antennae search (IBAS) algorithm was developed by introducing inertia weight and used to construct an IBAS-BPNN model for predicting sales volume in Douyin live streaming. The results showed that compared with the BPNN, PSO-BPNN, and ABC-BPNN algorithms, the IBAS-BPNN algorithm had better prediction performance, with a root-mean-square error of 335.6694, a mean absolute percentage error of 0.0532%, an equilibrium coefficient of 0.9889, and a shorter training time of 90.07 s. The experimental results demonstrate the reliability of the IBAS-BPNN algorithm for predicting sales volume in Douyin live streaming, providing new insights into parameter optimization of BPNN and offering references for further research on BPNN parameter optimization. It also provides an effective method with both timeliness and high accuracy for predicting sales volume in Douyin live streaming in practical applications. Doi: 10.28991/HIJ-2023-04-02-09 Full Text: PDF

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“…-Artificial bee colony (ABC) algorithm [20]: It finds the optimal food source, which is the optimal solution, through the bees' cyclic search. The population size was set to 100, and the maximum number of iterations was 200.…”

Section: Mutation Probabilitymentioning

confidence: 99%

Optimization of the supply chain network planning problem using an improved genetic algorithm

Zhao,

Xie

2023

Int. J. Simul. Multidisci. Des. Optim.

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The planning problem of supply chain network is highly related to logistics cost and product quality. In this paper, for the optimization of supply chain network planning problem, an agricultural product supply chain network under the direct docking model between farmers and supermarkets was taken as an example to establish an agricultural product supply chain network planning model with the lowest cost as the objective. Then, an improved genetic algorithm (GA) was designed to solve the model. The analysis of the arithmetic example showed that compared with the traditional GA, the total cost obtained by the improved GA was lower, at 39,004.48 $, which was 6.5% less than that of the traditional GA; the solution result of the improved GA was also superior to that of other heuristic algorithms, such as particle swarm optimization and ant colony optimization. The experimental results demonstrate the optimization effectiveness of the improved GA for the supply chain network planning problem, and it can be applied in practice.

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System sizing of hybridsolar‐fuel cell batteryenergy system using artificial bee colony algorithm with predator effect

Cited by 12 publications

References 51 publications

Permanent Magnet Motor Control System Based on Fuzzy PID Control

Permanent Magnet Motor Control System Based on Fuzzy PID Control

The Prediction of Douyin Live Sales based on Neural Network Algorithms

Optimization of the supply chain network planning problem using an improved genetic algorithm

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