Firefly algorithm: an optimization solution in big data processing for the healthcare and engineering sector

Rahul, Kumar; Banyal, Rohitash Kumar

doi:10.1007/s10772-020-09783-y

Cited by 11 publications

(10 citation statements)

References 69 publications

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“…Nature-derived algorithms are the most powerful algorithms for optimization. Recently, swarm intelligence [26], the biologically inspired algorithms, have been studied dramatically, such as firefly swarm optimization algorithm [14], ant colony optimization technique [27], particle swarm optimization (PSO) algorithm [28], artificial fish swarm optimization algorithm [29], and swallow swarm optimization (SSO) [30]. They have been applied to solve various problems, such as healthcare, finance, energy, image thresholding, and others.…”

Section: Swarm Intelligence Optimization Algorithmmentioning

confidence: 99%

“…Besides, the confusion matrix is an effective performance metric to evaluate the binary classification problem, where four basic metrics true positive (TP), false positive (FP), true negative (TN), and false negative (FN) are used to measure the output. Based on the above basic metrics, some other evaluation indices, Accuracy, Precision, Recall, F1-score, and ROC_AUC Score, are calculated by ( 24)− (30).…”

Section: Dataset and Settingsmentioning

confidence: 99%

See 1 more Smart Citation

Product Quality Prediction Based on RBF Optimized by Firefly Algorithm

Han,

Wang,

Chen

et al. 2024

J. of Syst. Eng. Electron.

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With the development of information technology, a large number of product quality data in the entire manufacturing process is accumulated, but it is not explored and used effectively. The traditional product quality prediction models have many disadvantages, such as high complexity and low accuracy. To overcome the above problems, we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model: radial basis function model optimized by the firefly algorithm with Levy flight mechanism (RBFFALM). First, the new data equalization method is introduced to pre-process the dataset, which reduces the dimension of the data, removes redundant features, and improves the data distribution. Then the RBFFALFM is used to predict product quality. Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.

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Section: Swarm Intelligence Optimization Algorithmmentioning

confidence: 99%

Section: Dataset and Settingsmentioning

confidence: 99%

Product Quality Prediction Based on RBF Optimized by Firefly Algorithm

Han,

Wang,

Chen

et al. 2024

J. of Syst. Eng. Electron.

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“…The literature reported that the proposed method could reduce the complexity of the problem. Rahul and Banyal discussed the applications of FA in big data analysis of medical and engineering fields 33 …”

Section: Recent Work On Famentioning

confidence: 99%

“…Rahul and Banyal discussed the applications of FA in big data analysis of medical and engineering fields. 33 Aggarwal and Kumar applied FA to vehicle routing problem with time windows (VRPTW). 34 Different distance metrics (Brute-Curtis, hamming, and cartesian) and were tested on Solomon benchmark problems.…”

Section: Recent Work On Famentioning

confidence: 99%

An efficient firefly algorithm based on modified search strategy and neighborhood attraction

Wang

Zhou

et al. 2021

Int J Intell Syst

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Firefly algorithm (FA) is a popular swarm intelligence optimization algorithm. Though FA was employed to solve various optimization problems, it still has some deficiencies, such as high complexity, slow convergence rate, and low precision of solutions. To tackle these issues, this paper proposes an efficient FA based on modified search strategy and neighborhood attraction (namely MSSNaFA). In MSSNaFA, there are four main modifications. First, a novel search strategy based on dimension differences is designed. The attractiveness in the original FA is related to the Euclidean distance, while our new method uses the differences of each dimension for two fireflies to compute the attractiveness. Then, a modified neighborhood attraction mechanism is utilized to reduce the computational complexity. When the current solution is selected, it will move to the global best solution based on the new movement strategy. Third, for each firefly, three neighborhood search operations are carried out based on a preset probability. Lastly, the step factor is adaptively adjusted in the search process. Performance validation between MSSNaFA and four other FA variants show the effectiveness of our approach.

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“…However, in the process of optimizing PID control parameters, the swarm intelligence optimization algorithms have some problems, such as complex parameter setting, limited global optimization capability, weak adaptability, and low precision. The FA is a novel swarm intelligence algorithm, which has been widely used in scientific computing and engineering applications due to its simple algorithm idea, few parameters to be adjusted, and easy implementation of the program 16 , 17 . Specifically, FA shows better performance in many scientific problems, but it still has some limitations, such as slow convergence and the tendency to trap local optimality in complex problems.…”

Section: Introductionmentioning

confidence: 99%

Parameter optimization of PID controller for water and fertilizer control system based on partial attraction adaptive firefly algorithm

Huang

Tian

Liu

et al. 2022

Sci Rep

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Proportional Integral Derivative (PID) control is the main control method in the process of agricultural water and fertilizer regulation, and its parameter setting directly affects the control effect of water and fertilizer regulation. However, the traditional PID parameters are adjusted manually such as using the critical proportionality method, which is time-consuming and difficult to achieve optimal control effects. To solve the optimal combination of PID control parameters and improve the control effect of water and fertilizer regulation, a partial attraction adaptive firefly algorithm (PAAFA) is proposed in this paper. Specifically, a partial attraction strategy is designed to speed up the convergence of the PAAFA and reduce the oscillation problem at the late stage of the algorithm. In addition, an adaptive inertia weight operator is proposed to balance the global search capability and local search capability of PAAFA and avoid the algorithm from trapping in the local optimum. Subsequently, to test the performance of PAAFA, the algorithm is subjected to a series of simulation experiments and bench tests with the latest methods, i.e., genetic algorithm (GA), Adaptive genetic algorithm (AGA), and firefly algorithm (FA) applied to PID parameter optimization problems. The simulation results demonstrate that the regulation times of the response curve of PAAFA-based PID control are reduced by 22.75%,10.10%and 20.61%, respectively, compared with GA, AGA, and FA. The bench test results show that the PAAFA-based PID control has the smallest relative error, and best control accuracy compared to GA, AGA, and FA, with an average relative error reduction of 3.99, 2.42, and 3.50 percentage points respectively.

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Firefly algorithm: an optimization solution in big data processing for the healthcare and engineering sector

Cited by 11 publications

References 69 publications

Product Quality Prediction Based on RBF Optimized by Firefly Algorithm

Product Quality Prediction Based on RBF Optimized by Firefly Algorithm

An efficient firefly algorithm based on modified search strategy and neighborhood attraction

Parameter optimization of PID controller for water and fertilizer control system based on partial attraction adaptive firefly algorithm

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