Firefly Algorithm Based on Level-Based Attracting and Variable Step Size

Zhao, Jia; Chen, Wenping; Ye, Jun; Wang, Hui; Sun, Haoyu; Lee, Ivan

doi:10.1109/access.2020.2981656

Cited by 16 publications

(7 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Heuristic algorithms can solve various optimization problems including the feature selection. Due to their effectiveness and simplicity, many heuristic algorithms have been proposed for solving the feature selection problems, e.g., GA [15], PSO [16], GWO [17], flower pollination algorithm (FPA) [18], artificial bee colony (ABC) [19], bacterial foraging optimization (BFO) [20], BA [21], cuckoo search (CS) [22], firefly algorithm (FA) [23], whale optimization algorithm (WOA) [24], grasshopper optimization algorithm (GOA) [25]. Recently, more and more heuristic algorithms are proposed to deal with many kinds of optimization problems.…”

Section: Related Workmentioning

confidence: 99%

IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

Kang

Sun

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Feature selection is an effective method to eliminate irrelevant, redundant and noisy features, which improves the performance of classification and reduces the computational burden in machine learning. In this paper, an improved binary dragonfly algorithm (IBDA) which extends from the conventional dragonfly algorithm (DA) is proposed as a search strategy to design a wrapper-based feature selection method. First, a novel evolutionary population dynamics (EPD) strategy is introduced in IBDA to enhance the exploitation ability while ensuring population diversity of the algorithm. Second, IBDA proposes a novel crossover operator which establishes connections between the crossover rates and iterations so that making the algorithm can adjust the crossover rates of solutions dynamically, thereby balancing the exploitation and exploration of the algorithm. Finally, a binary mechanism is proposed to make the algorithm suitable for the binary feature selection problems. Simulations are conducted on 27 classical datasets from the UC Irvine Machine Learning Repository, and the results demonstrate that the proposed IBDA has better performance than some other comparison algorithms. Moreover, the effectiveness and performance of the proposed improved factors are evaluated by tests.

show abstract

Section: Related Workmentioning

confidence: 99%

IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

Kang

Sun

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…in Eq13 exp(x) = e x , ee is calculated as: ee = abs(cos(θ)) aa (14) θ is a random numbers from [0, 2π], the updated formula for aa is:…”

Section: B Vector Angle Parametermentioning

confidence: 99%

“…It jumps out of the local optimal value and improves the accuracy of the algorithm. Wang et al [13]proposes a firefly algorithm with domain attraction and a hierarchical attractive firefly algorithm is also proposed [14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Hybrid Firefly Algorithm Based on the Vector Angle Learning Mechanism

Meng

Wang

2020

IEEE Access

View full text Add to dashboard Cite

The firefly algorithm (FA) is one of the swarm intelligence algorithms which can solve global optimization problems accurately. In the traditional FA, the position of each firefly can only be updated by the brightness of other fireflies around it. As a result, it is simple to update the firefly position but easy to fall into local optimum. In this paper, a novel hybrid firefly algorithm based on the vector angle learning mechanism (HFA-VAL) is proposed, which can combine the advantages of both the firefly algorithm (FA) and differential evolution (DE) by the vector angle learning mechanism. HFA-VAL employs vector angle parameters to adaptively adjust the moving step length of firefly in order to avoid falling into local optimum. In the evolutionary process, the difference method is used to update the dominant leader, so as to improve the moving direction of other fireflies and expand the search ability. In order to understand the strengths and weaknesses of HFA-VAL, several experiments are carried out on 25 benchmark functions in CEC2005. Experimental results show that the performance of HFA-VAL algorithm is better than other the-state-of-art algorithms.INDEX TERMS Vector angle learning mechanism, hybrid firefly algorithm, differential evolution, logarithmic spiral parameter.

show abstract

“…Based on various search spaces of optimization issues, a large search step size is needed if the definition space of the optimization target is high. Otherwise, a small search step size is required, which will aid the algorithm's ability to acquire a variety of optimization issues [24].…”

mentioning

confidence: 99%

Optimizing Multi-Layer Perceptron using Variable Step Size Firefly Optimization Algorithm for Diabetes Data Classification

Behera¹,

Sarangi²,

Mishra³

et al. 2023

Int. J. Onl. Eng.

View full text Add to dashboard Cite

According to a survey conducted by the International Diabetes Federation, the proportion of people living with diabetes is gradually rising. Diabetes mellitus is a chronic disorder caused by elevated blood sugar levels. For the early diagnosis and treatment of diabetes patients, efficient machine-learning methods are needed. Data Classification is a significant subject in many areas of life, and it is also a very challenging job in data mining. Clinical data mining has recently gained attention in complicated healthcare challenges relying on healthcare datasets. The principal objective of classification is to classify all data in a given dataset to a certain class label. In the healthcare field, classification is commonly employed in much research articles. A hybrid method for diabetes data classification is suggested by integrating multilayer perceptron with a modified firefly optimization algorithm for diabetes data classification. The performance of the proposed hybrid multilayer perceptron variable step size firefly algorithm is compared with other hybrid models such as the hybrid multilayer perceptron particle swarm optimization algorithm, hybrid multilayer perceptron differential evolution algorithm, and hybrid multilayer perceptron firefly optimization algorithm. The performance of these models is calculated based on accuracy, precision, recall, F1 score, and mean square error. In comparison to other models, the proposed hybrid model produces superior outcomes for diabetes data classification.

show abstract

Firefly Algorithm Based on Level-Based Attracting and Variable Step Size

Cited by 16 publications

References 45 publications

IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

A Novel Hybrid Firefly Algorithm Based on the Vector Angle Learning Mechanism

Optimizing Multi-Layer Perceptron using Variable Step Size Firefly Optimization Algorithm for Diabetes Data Classification

Contact Info

Product

Resources

About