A Hybrid Data Clustering Using Firefly Algorithm Based Improved Genetic Algorithm

Maheshwar, .; Kaushik, Keshav; Arora, Vikram

doi:10.1016/j.procs.2015.08.018

Cited by 23 publications

(7 citation statements)

References 10 publications

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“…Table 4 shows the performance (the mean RI and mean ARI values) for the S1 data set with different fitness functions (F1, F2, F3) for different population sizes (10,20,50) and different iterations of the algorithms. The mean values were calculated for 20 runs of each firefly variant.…”

Section: Resultsmentioning

confidence: 99%

“…There have been a few studies that have focused on combining FA with evolutionary algorithms to address clustering tasks. Using an FA-based improved genetic algorithm, Kaushik and the team presented a hybrid algorithm [20] whose performance was better when compared to canonical FA and canonical GA separately when applied to four benchmark data sets: Iris, Glass, Brest cancer, and Wine (taken from the UCI repository [11]). Another combination of FA and genetic algorithms was applied to select the appropriate cluster heads of a wireless sensor network [6].…”

Section: Related Workmentioning

confidence: 99%

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Finding Playing Styles of Badminton Players Using Firefly Algorithm Based Clustering Algorithms

Ariyaratne,

Ilankoon,

Samarasinghe

et al. 2023

csci

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Cluster analysis can be defined as applying clustering algorithms with the goal of finding hidden patterns or groupings in a dataset. Different clustering methods provide different solutions for the same dataset. Traditional clustering algorithms are popular, but handling big data sets is beyond the ability of such methods. We propose three big data clustering methods, based on the Firefly Algorithm (FA). Three different fitness functions were defined on FA using inter cluster distance, intra cluster distance, silhouette value and Calinski-Harabasz Index. The algorithms find the most appropriate cluster centers for a given data set. The algorithms were tested with four popular synthetic data sets and later applied on two badminton data sets to identify different playing styles of players based on physical characteristics. The results specify that the firefly algorithm could generate better clustering results with high accuracy. The algorithms cluster the players to find the most suitable playing strategy for a given player where expert knowledge is needed in labeling the clusters. Comparisons with a PSO based clustering algorithm (APSO) and traditional algorithms point out that the proposed firefly variants work similarly as the APSO method and surpass the performance of traditional algorithms.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Finding Playing Styles of Badminton Players Using Firefly Algorithm Based Clustering Algorithms

Ariyaratne,

Ilankoon,

Samarasinghe

et al. 2023

csci

View full text Add to dashboard Cite

show abstract

“…GP evolves a program that forecasts the actual output from an experimental data file of inputs and outputs [22,23]. The GP Discipulus software uses Java, C/C ++ , and assembly interpreter [24] to write a program that maps inputs onto output data.…”

Section: Genetic Programmingmentioning

confidence: 99%

Mathematical Modelling of a Friction Stir Welding Process to Predict the Joint Strength of Two Dissimilar Aluminium Alloys Using Experimental Data and Genetic Programming

Yunus

Alsoufi

2018

Modelling and Simulation in Engineering

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Friction stir welding (FSW) is the most popular and efficient method of solid-state joining for similar as well as dissimilar metals and alloys. It is mostly used in applications for aerospace, rail, automotive, and marine industries. Many researchers are currently working with different perspectives on this FSW process for various combinations of materials. The general input process parameters are the thickness of the plate, axial load, rotational speed, welding speed, and tilt angle. The output parameters are joint hardness, % of elongation, and impact and yield strengths. Genetic programming (GP) is a relatively new method of evolutionary computing with the principal advantage of this approach being to evaluate efficacious predictive mathematical models or equations without any prior assumption regarding the possible form of the functional relationship. This paper both defines and illustrates how GP can be applied to the FSW process to derive precise relationships between the output and input parameters in order to obtain a generalized prediction model. A GP model will assist engineers in quantifying the performance of FSW, and the results from this study can then be utilized to estimate future requirements based on the historical data to provide a robust solution. The obtained results from the GP models showed good agreement with experimental and target data at an average prediction error of 0.72%.

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“…The application of fuzzy set which can be used on clustring problem is Fuzzy Clustering Means (FCM) [2]. In previous researches, some methods have been applied in clustering problem like K-Means [3], Kohonen Network or Neural Network [4], Genetic Algorithm [5].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Membership Degree of Fuzzy Inference System (FIS) and Fuzzy Clustering Means (FCM) in Weather Data Using Firefly Algorithm

Rahmalia

2021

CAUCHY

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In weather clustering, there are many variables which can be observed such as air temperature, humidity, sunlight intensity, and so on. In this research, Takagi-Sugeno Fuzzy Inference System (FIS) will be used for forecasting the sunlight intensity based on temperature and humidity and Fuzzy Clustering Means (FCM) will be used for clustering them based on fuzzy set. From the data consisting of temperature, humidity, and sunlight intensity, we will forecast sunlight intensity and cluster them into two clusters, three clusters, and four clusters by FCM method. In FIS method, the membership degree are often generated by trial and error. Also, the optimization of the initial of membership degree are required in FCM. Because the initial of membership degree are often generated by trial and error, in this research, we use heuristic method like Firefly Algorithm to optimize the membership degree. From the simulations, Firefly Algorithm can optimize the membership degree of FIS for forecasting the data with minimum Mean Square Error (MSE) and the initial of membership degree of FCM with two clusters, three clusters, and four clusters with minimum objective value.

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A Hybrid Data Clustering Using Firefly Algorithm Based Improved Genetic Algorithm

Cited by 23 publications

References 10 publications

Finding Playing Styles of Badminton Players Using Firefly Algorithm Based Clustering Algorithms

Finding Playing Styles of Badminton Players Using Firefly Algorithm Based Clustering Algorithms

Mathematical Modelling of a Friction Stir Welding Process to Predict the Joint Strength of Two Dissimilar Aluminium Alloys Using Experimental Data and Genetic Programming

Optimizing the Membership Degree of Fuzzy Inference System (FIS) and Fuzzy Clustering Means (FCM) in Weather Data Using Firefly Algorithm

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