Metode Hibridasi Artificial Bee Colony dan Fuzzy K-Modes untuk Klasterisasi Data Kategorikal

2019 ◽  
Vol 4 (2) ◽  
pp. 36-42
Author(s):  
Khalid Khalid
Keyword(s):  
Breast Cancer ◽  
Objective Function ◽  
Artificial Bee Colony ◽  
Dissimilarity Measure ◽  
Data Repository ◽  
Congressional Voting ◽  
Bee Colony ◽  
F Measure

Fuzzy K-Modes (FKMO) merupakan metode klasterisasi data yang efektif untuk data kategorikal. Metode ini menggunakan metode fuzzy dan pencocokan ukuran ketidaksamaan (dissimilarity measure) yang sederhana untuk memutakhirkan titik pusat klaster dan mendapatkan solusi yang optimal. Meskipun demikian Fuzzy K-Modes memiliki kelemahan adanya kemungkinan berhenti dalam solusi lokal optimal. Artificial Bee Colony (ABC) merupakan metode optimasi yang efektif dan terbukti memiliki kemampuan mendapatkan solusi global. Penelitian ini mengusulkan penggunaan algoritma Artificial Bee Colony untuk melakukan optimasi terhadap Fuzzy K-Modes untuk klasterisasi data kategorikal (ABC-FKMO).  Implementasi Artifical Bee Colony untuk optimasi Fuzzy K-Modes terbukti mampu meningkatkan performa klasterisasi data kategorikal khususnya dalam aspek nilai Objective Function, F-Measure, dan Accuracy. Hasil pengujian dengan  dataset Soybean Disease, Breast Cancer dan Congressional Voting Records dari UCI data repository, menunjukkan rata-rata accuracy sebesar 0.991, 0.615, dan 0.867. Objective Function lebih baik rata rata sebesar 2,73 %, F-Measure lebih baik rata-rata sebesar 4,31 % dan Accuracy lebih baik rata-rata sebesar 5,16 %.

scholarly journals Klasterisasi Data Kategorikal Berbasis Fuzzy K-Modes Dan Artificial Bee Colony

2018 ◽  
Author(s):  
khalid Khalid
Keyword(s):  
Breast Cancer ◽  
Objective Function ◽  
Artificial Bee Colony ◽  
Dissimilarity Measure ◽  
Data Repository ◽  
Congressional Voting ◽  
Bee Colony ◽  
F Measure

Fuzzy K-Modes (FKMO) merupakan metode klasterisasi data yang efektif untuk data kategorikal. Metode ini menggunakan metode fuzzy dan pencocokan ukuran ketidaksamaan (dissimilarity measure) yang sederhana untuk memutakhirkan titik pusat klaster dan mendapatkan solusi yang optimal. Meskipun demikian Fuzzy K-Modes memiliki kelemahan adanya kemungkinan berhenti dalam solusi lokal optimal.Artificial Bee Colony (ABC) merupakan metode yang efektif untuk melakukan optimasi dan terbukti memiliki solusi global. Penelitian ini mengusulkan penggunaan algoritma Artificial Bee Colony untuk melakukan optimasi terhadap Fuzzy K-Modes (ABC-FKMO). Implementasi Artifical Bee Colony untuk optimasi Fuzzy K-Modes terbukti mampu meningkatkan performance klasterisasi khususnya dalam aspek nilai Objective Function, F-Measure, dan Accuracy. Hasil pengujian dengan dataset Soybean Disease, Breast Cancer dan Congressional Voting Records dari UCI data repository, menunjukkan rata-rata accuracy sebesar 0.991, 0.615, dan 0.867. Objective Function lebih baik rata rata sebesar 2,73 %, F-Measure lebih baik rata-rata sebesar 4,31 % dan Accuracy lebih baik rata-rata sebesar 5,16 %.

scholarly journals Green Product Module Partition Method Based on Improved Multi-objective Artificial Bee Colony Algorithm

2019 ◽  
Vol 301 ◽  
pp. 00021
Author(s):  
Wei Wei ◽  
Yang Zhan
Keyword(s):  
Life Cycle ◽  
Objective Function ◽  
Correlation Matrix ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Green Product ◽  
Multi Objective ◽  
Bee Colony ◽  
Axiomatic Design Theory ◽  
Module Partition

Modular design is an important design method in the mass customization for manufacturing industry. The purpose of this paper is to meet diverse market demands while reducing the impact of products on the ecological environment. Firstly, aiming at the product life cycle process, this paper summarizes the problems encountered in each stage of the product, and introduces five green product module partition principles. Then, through the component correlation matrix, the resource greenness objective function based on the whole life cycle and the polymerization degree objective function based on the component correlation matrix are established respectively by the axiomatic design theory which makes the product mapping from functional domain to structural domain. Next, an improved artificial bee colony algorithm is proposed. Based on the artificial bee colony algorithm, the algorithm applies congestion strategy and fast nondominated sorting strategy to solve the module partition problem of product platform with multi-objective optimization, and a uniformly distributed pare to solution set is generated. Through above steps, the optimization results of module partition are obtained. Finally, an application example of aircraft tail horizontal stabilizer parts is given, and the advantages of the algorithm are proved by comparing with other algorithms.

scholarly journals Using new artificial bee colony as probabilistic neural network for breast cancer data classification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Habib Shah
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Bee Colony ◽  
Probabilistic Neural Network ◽  
Breast Cancers ◽  
Breast Cancer Data ◽  
Data Set ◽  
Content Type ◽  
Cancer Data ◽  
Bee Colony

PurposeBreast cancer is an important medical disorder, which is not a single disease but a cluster more than 200 different serious medical complications.Design/methodology/approachThe new artificial bee colony (ABC) implementation has been applied to probabilistic neural network (PNN) for training and testing purpose to classify the breast cancer data set.FindingsThe new ABC algorithm along with PNN has been successfully applied to breast cancers data set for prediction purpose with minimum iteration consuming.Originality/valueThe new implementation of ABC along PNN can be easily applied to times series problems for accurate prediction or classification.

scholarly journals Prognostic of RUL based on Echo State Network Optimized by Artificial Bee Colony

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Edgar J. Amaya ◽  
Alberto J. Alvares
Keyword(s):  
Artificial Bee Colony ◽  
Hybrid Approach ◽  
Remaining Useful Life ◽  
Data Repository ◽  
Echo State Network ◽  
Health State ◽  
Future Health ◽  
Large Space ◽  
Abc Algorithm ◽  
Bee Colony

Prognostic is an engineering technique used to predict the future health state or behavior of an equipment or system. In this work, a data-driven hybrid approach for prognostic is presented. The approach based on Echo State Network (ESN) and Artificial Bee Colony (ABC) algorithm is used to predict machine’s Remaining Useful Life (RUL). ESN is a new paradigm that establishes a large space dynamic reservoir to replace the hidden layer of Recurrent Neural Network (RNN). Through the application of ESN is possible to overcome the shortcomings of complicated computing and difficulties in determining the network topology of traditional RNN. This approach describes the ABC algorithm as a tool to set the ESN with optimal parameters. Historical data collected from sensors are used to train and test the proposed hybrid approach in order to estimate the RUL. To evaluate the proposed approach, a case study was carried out using turbofan engine signals show that the proposed method can achieve a good collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). The experimental results using the engine data from NASA Ames Prognostics Data Repository RUL estimation precision. The performance of this model was compared using prognostic metrics with the approaches that use the same dataset. Therefore, the ESN-ABC approach is very promising in the field of prognostics of the RUL.

scholarly journals Multiple Crack Detection in Thick-walled Pipes Using Artificial Bee Colony Algorithm

2021 ◽  
Author(s):  
Amir Banimahd ◽  
Mohammad Amir Rahemi
Keyword(s):  
Objective Function ◽  
Stiffness Matrix ◽  
Crack Detection ◽  
Artificial Bee Colony ◽  
Dynamic Properties ◽  
Mass Matrix ◽  
Multiple Cracks ◽  
Unknown Parameters ◽  
Abc Algorithm ◽  
Bee Colony

An analytical method for diagnosis of cracks in thick-walled pipes with a circular hollow section is investigated in this study. In the proposed method, the defect is assumed to be a non-leaking crack, which is modeled by a massless linear spring with infinitesimal length at the crack location. In order to find the cracks in the pipe, the vibration-based method related to the modal properties of the pipe is utilized. In the modal analysis, the mass and stiffness matrices influence the dynamic properties of the pipe. It is assumed that the mass matrix remains unchanged after the crack initiation, while the corresponding stiffness matrix changes. The stiffness matrix of a cracked element can be formulated by the finite element method with two unknown parameters: location and depth of the crack. Using the eigensolution for an undamped dynamic system to formulate the objective function yields to a complicated optimization problem, which can be solved by an iterative numerical optimization method. Among the optimization approaches, the Artificial Bee Colony (ABC) algorithm is a simple and flexible technique for minimizing the objective function. In this paper, the analytical model is utilized to find the size and position of cracks in a pipe using the ABC algorithm and subsequently some numerical examples are examined in order to assess the accuracy of the method. The results show that the proposed method is able to acceptably estimate the location and depth of multiple cracks in the straight pipes as well as curved ones.

scholarly journals MS Location Estimation Based on the Artificial Bee Colony Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5597
Author(s):  
Chien-Sheng Chen ◽  
Jen-Fa Huang ◽  
Nan-Chun Huang ◽  
Kai-Sheng Chen
Keyword(s):  
Objective Function ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Mobile Station ◽  
Location Estimation ◽  
Accurate Estimation ◽  
Time Of Arrival ◽  
Localization Accuracy ◽  
Abc Algorithm ◽  
Bee Colony

With the mature technology of wireless communications, the function of estimating the mobile station (MS) position has become essential. Suppressing the bias resulting from non-line-of-sight (NLSO) scenarios is the main issue for a wireless location network. The artificial bee colony (ABC) algorithm, based on the depiction of bee swarm’s foraging characteristics, is widely applied to solve optimization problems in several fields. Based on three measurements of time-of-arrival (TOA), an objective function is used to quantify the additional NLOS error on the MS positioning scheme. The ABC algorithm is adopted to locate the most precise MS location by minimizing the objective function value. The performance of the proposed positioning methods is verified under various error distributions through computer simulations. Meanwhile, the localization accuracy achieved by other existing methods is also investigated. According to the simulation results, accurate estimation of the MS position is derived and therefore the efficiency of the localization process is increased.

Artificial bee colony–based Kalman filter hybridization for three–dimensional position estimation of a quadrotor

2020 ◽  
Vol 92 (10) ◽  
pp. 1523-1532 ◽  
Author(s):  
Aziz Kaba ◽  
Emre Kiyak
Keyword(s):  
Kalman Filter ◽  
Objective Function ◽  
Artificial Bee Colony ◽  
Mathematical Proof ◽  
Position Estimation ◽  
Measurement Noise ◽  
Content Type ◽  
Penalty Term ◽  
Bee Colony ◽  
Noise Statistics

Purpose The purpose of this paper is to introduce an artificial bee colony-based Kalman filter algorithm along with an extended objective function to ensure the optimality of the estimator of the quadrotor in the presence of unknown measurement noise statistics. Design/methodology/approach Six degree-of-freedom mathematical model of the quadrotor is derived. Position controller for the quadrotor is designed. Kalman filter-based estimation algorithm is implemented in the sensor feedback loop. Artificial bee colony-based hybrid algorithm is used as an optimization method to handle the unknown noise statistics. Existing objective function is extended with a penalty term. Mathematical proof of the extended objective function is derived. Results of the proposed algorithm is compared with de facto genetic algorithm-based Kalman filter. Findings Artificial bee colony algorithm-based Kalman filter and extended objective function duo are able to optimize the measurement noise covariance matrix with an absolute error as low as 0.001 [m2]. Proposed method and function is capable of reducing the noise from 2 to 0.09 [m] for x-axis, 3.4 to 0.14 [m] for y-axis and 3.7 to 0.2 [m] for z-axis, respectively. Originality/value The motivation behind this paper is to bring a novel optimization-based solution for the estimation problem of the quadrotor when the measurement noise statistics are unknown along with an extended objective function to prevent the infeasible solutions with mathematical convergence analysis.

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