Stellar population analysis of galaxies based on improved flower pollination algorithm

Baset, Mohamed Abdel; Selim, I. M.; Zhou, Yongquan; Hezam, Ibrahim M.

doi:10.1504/ijmmno.2018.088995

Cited by 2 publications

(1 citation statement)

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“…And the operation performance of the seeder is improved. In view of the shortcomings of the basic genetic algorithm, such as slow convergence speed and premature convergence, Lei proposed an improved adaptive genetic algorithm, which automatically adjusted the crossover probability and mutation probability according to the size of the fitness value in the evolution process, so that the algorithm could jump out of the local optimal solution [11]. The simulation experiment of mobile robot path planning shows that.…”

Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Computer Mathematical Modeling Based on the Improved Genetic Algorithm and Mobile Computing

Wei

Liu

2021

Wireless Communications and Mobile Computing

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In order to change the problem of data redundancy in a genetic algorithm, this paper proposes a computer mathematical model based on the combination of an improved genetic algorithm and mobile computing. Combined with the least square method, MATLAB software is used to solve the equations, determine the range of parameters, and solve the estimation parameter range and identification problems. The improved genetic algorithm combined with mobile computing and least square method to establish a mathematical model greatly increased the individual search space and increased the operation rate of 90% compared to the basic genetic algorithm or mobile computing. The results show that the improved genetic algorithm and mobile computing have a certain ability to identify the optimal solution and greatly improve the work efficiency.

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Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Computer Mathematical Modeling Based on the Improved Genetic Algorithm and Mobile Computing

Wei

Liu

2021

Wireless Communications and Mobile Computing

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Modeling and Optimization of a Liquid Flow Process using an Artificial Neural Network-Based Flower Pollination Algorithm

Dutta

Kumar²

2018

Journal of Intelligent Systems

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Abstract Controlling liquid flow is one of the most important parameters in the process control industry. It is challenging to optimize the liquid flow rate for its highly nonlinear nature. This paper proposes a model of liquid flow processes using an artificial neural network (NN) and optimizes it using a flower pollination algorithm (FPA) to avoid local minima and improve the accuracy and convergence speed. In the first phase, the NN model was trained by the dataset obtained from the experiments, which were carried out. In these conditions, the liquid flow rate was measured at different sensor output voltages, pipe diameter and liquid conductivity. The model response was cross-verified with the experimental results and found to be satisfactory. In the second phase of work, the optimized conditions of sensor output voltages, pipe diameter and liquid conductivity were found to give the minimum flow rate of the process using FPA. After cross-validation and testing subdatasets, the accuracy was nearly 94.17% and 99.25%, respectively.

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Stellar population analysis of galaxies based on improved flower pollination algorithm

Cited by 2 publications

References 0 publications

[Retracted] Computer Mathematical Modeling Based on the Improved Genetic Algorithm and Mobile Computing

[Retracted] Computer Mathematical Modeling Based on the Improved Genetic Algorithm and Mobile Computing

Modeling and Optimization of a Liquid Flow Process using an Artificial Neural Network-Based Flower Pollination Algorithm

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