scholarly journals Optimasi Cluster Pada Fuzzy C-Means Menggunakan Algoritma Genetika Untuk Menentukan Nilai Akhir

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Putri Elfa Mas`udia ◽  
Retantyo Wardoyo
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Cluster Center ◽  
Local Optimum ◽  
Membership Degree ◽  
Clustering Method ◽  
Final Grade ◽  
Fuzzy C Means ◽  
Group A ◽  
The Difference

AbstrakNilai akhir mahasiswa dapat ditentukan dengan berbagai cara, beberapa diantaranya menggunakan range nilai, standart deviasi, dll. Dalam penelitian ini akan ditawarkan sebuah metode baru untuk menentukan nilai akhir mahasiswa menggunakan clustering dalam hal ini adalah Fuzzy C-Means.Fuzzy C-Means digunakan untuk mengelompokkan sejumlah data dalam beberapa cluster. Tiap data memiliki derajat keanggotaan pada masing-masing cluster antara 0-1 yang diukur melalui fungsi objektif. Pada Fuzzy C-Means ini fungsi objektif diminimumkan menggunakan iterasi yang biasanya terjebak dalam optimum lokal. Algoritma genetika diharapkan dapat menangani masalah tersebut karena algoritma genetika berbasis evolusi yaitu dapat mencari individu terbaik melalui operasi genetika (seleksi, crossover, mutasi) dan dievaluasi berdasarkan nilai fitness. Penelitian ini bertujuan untuk mengoptimasi titik pusat cluster pada Fuzzy C-Means menggunakan algoritma genetika. Hasilnya, bahwa dengan menggunakan GFS didapatkan fungsi objektif yang lebih kecil daripada menggunakan FCM, walaupun membutuhkan waktu yang relative besar. Meskipun selisih antara FCM dan GFS tidak terlalu besar namun hal tersebut berpengaruh pada anggota cluster  Kata kunci— clustering, Fuzzy C-Means, algoritma genetika AbstractThe final grade of students could be determined in various ways, some of which use a range of values, deviation standard, etc. In this study will be offered a new method for determining final grades of students by using the clustering method. In this research the clustering method that will be used is the Fuzzy C-Means (FCM).Fuzzy C-Means is used to group a number of data in multiple clusters. Each data has a degree of membership (the range value of membership degree is 0-1). Membership degree is measured through the objective function. In Fuzzy C-Means,  objective function is minimized by using iteration and is usually trapped in a local optimum. Genetic algorithm is expected to handle these problems. The operation of genetic algorithm based on evolution that is able to find the best individuals through genetic operations (selection, crossover and mutation) and evaluated based on fitness values.This research aims to optimize the cluster center point of FCM by using genetic algorithms. The result of this research shows that by combining the Genetic Algorithm with FCM could obtained a smaller objective function than using FCM, although it takes longer in execution time. Although the difference of objective function that produced by FCM and FCM-Genetic Algorithm combination is not too big each other, but it takes effect on the cluster members. Keywords— clustering, fuzzy c-means, genetic algorithm

Back Analysis of Material Parameters of Concrete Dam Using Genetic Algorithm

2012 ◽  
Vol 562-564 ◽  
pp. 1955-1958
Author(s):  
Jin Bao Liu ◽  
Shou Ju Li ◽  
Wei Zhu
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Optimization Problem ◽  
Back Analysis ◽  
Water Level Change ◽  
Concrete Dam ◽  
Material Parameters ◽  
Level Change ◽  
Inversion Procedure ◽  
The Difference

The inverse problem of parameter identification is deal with by minimizing an objective function that contains the difference between observed and calculated dam displacements. The optimization problem of minimizing objective function is solved with genetic algorithm. The calculated dam displacements are simulated by using finite element method according to water level change acting on dam upstream. The practical dam displacements are observed on the dam crest. The investigation shows that the forecasted dam displacements agree well with observed ones. The effectiveness of proposed inversion procedure is validated.

scholarly journals A complexity task of optimization in logistic distribution: A new approach to the green multi-objective vehicle routing problem

2022 ◽  
Vol 38 (1) ◽  
Author(s):  
Ferreira J. ◽  
Steiner M.
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Logistic Distribution ◽  
Nsga Ii ◽  
Routing Problem ◽  
Multi Objective ◽  
The Difference ◽  
Stage 1

Logistic distribution involves many costs for organizations. Therefore, opportunities for optimization in this respect are always welcome. The purpose of this work is to present a methodology to provide a solution to a complexity task of optimization in Multi-objective Optimization for Green Vehicle Routing Problem (MOOGVRP). The methodology, illustrated using a case study (employee transport problem) and instances from the literature, was divided into three stages: Stage 1, “data treatment”, where the asymmetry of the routes to be formed and other particular features were addressed; Stage 2, “metaheuristic approaches” (hybrid or non-hybrid), used comparatively, more specifically: NSGA-II (Non-dominated Sorting Genetic Algorithm II), MOPSO (Multi-Objective Particle Swarm Optimization), which were compared with the new approaches proposed by the authors, CWNSGA-II (Clarke and Wright’s Savings with the Non-dominated Sorting Genetic Algorithm II) and CWTSNSGA-II (Clarke and Wright’s Savings, Tabu Search and Non-dominated Sorting Genetic Algorithm II); and, finally, Stage 3, “analysis of the results”, with a comparison of the algorithms. Using the same parameters as the current solution, an optimization of 5.2% was achieved for Objective Function 1 (OF{\displaystyle _{1}}; minimization of CO{\displaystyle _{2}} emissions) and 11.4% with regard to Objective Function 2 (OF{\displaystyle _{2}}; minimization of the difference in demand), with the proposed CWNSGA-II algorithm showing superiority over the others for the approached problem. Furthermore, a complementary scenario was tested, meeting the constraints required by the company concerning time limitation. For the instances from the literature, the CWNSGA-II and CWTSNSGA-II algorithms achieved superior results.

scholarly journals A Study of Spatiotemporal Distribution of Mobility-On-Demand in Generating Pick-Up/Drop-Offs Location Placement

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 746-766
Author(s):  
Ryan K. Gunawan ◽  
Susilawati Susilawati
Keyword(s):  
Genetic Algorithm ◽  
Spatial Distribution ◽  
Spatiotemporal Distribution ◽  
Walking Distance ◽  
Clustering Method ◽  
Location Optimization ◽  
Demand Distribution ◽  
On Demand ◽  
Time Periods ◽  
The Difference

The location placement of pick-up/drop-offs of ride hailing usually only considers spatial distribution within a certain area. Previous studies often mapped out the potential hotspots for pick-up/drop-offs, benefitting the ride-hailing company and not considering the passengers. Therefore, in this study, we incorporated the spatiotemporal distribution of mobility-on-demand on generating pick-up/drop-off location placement using a genetic algorithm considering the walking distance and minimum demand data served within the radius. The data collected are analyzed through the clustering method, and the resulting cluster centers are fed into the location optimization algorithm. The genetic algorithm is used to optimize the location placement with the consideration of the user’s walking distance and demand. The algorithm is able to find an appropriate placement and determine reliable pick-up/drop-off stations within the study area based on observed spatiotemporal demand despite the difference in demand distribution through different time periods.

Fuzzy support vector machine with joint optimization of genetic algorithm and fuzzy c-means

2021 ◽  
pp. 1-17
Author(s):  
Ming-Ai Li ◽  
Ruo-Tu Wang ◽  
Li-Na Wei
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Joint Optimization ◽  
Cluster Center ◽  
Support Vector ◽  
Classification Problems ◽  
Fuzzy Support Vector Machine ◽  
Local Optima ◽  
Fuzzy C Means ◽  
Initial Cluster

BACKGROUND: Motor imagery electroencephalogram (MI-EEG) play an important role in the field of neurorehabilitation, and a fuzzy support vector machine (FSVM) is one of the most used classifiers. Specifically, a fuzzy c-means (FCM) algorithm was used to membership calculation to deal with the classification problems with outliers or noises. However, FCM is sensitive to its initial value and easily falls into local optima. OBJECTIVE: The joint optimization of genetic algorithm (GA) and FCM is proposed to enhance robustness of fuzzy memberships to initial cluster centers, yielding an improved FSVM (GF-FSVM). METHOD: The features of each channel of MI-EEG are extracted by the improved refined composite multivariate multiscale fuzzy entropy and fused to form a feature vector for a trial. Then, GA is employed to optimize the initial cluster center of FCM, and the fuzzy membership degrees are calculated through an iterative process and further applied to classify two-class MI-EEGs. RESULTS: Extensive experiments are conducted on two publicly available datasets, the average recognition accuracies achieve 99.89% and 98.81% and the corresponding kappa values are 0.9978 and 0.9762, respectively. CONCLUSION: The optimized cluster centers of FCM via GA are almost overlapping, showing great stability, and GF-FSVM obtains higher classification accuracies and higher consistency as well.

scholarly journals K-Means and Fuzzy C-Means Optimization using Genetic Algorithm for Clustering Questions

2021 ◽  
Vol 1 (1) ◽  
pp. 1-9
Author(s):  
Siti Sendari ◽  
Agung Bella Putra Utama ◽  
Nastiti Susetyo Fanany Putri ◽  
Prasetya Widiharso ◽  
Rizki Jumadil Putra
Keyword(s):  
Genetic Algorithm ◽  
Development Strategy ◽  
Educational Game ◽  
Difficulty Level ◽  
Clustering Methods ◽  
Clustering Method ◽  
Fuzzy C Means ◽  
The Development Strategy ◽  
Data Grouping ◽  
Game Developers

The grouping of data can be used in the development strategy of an educational game application. The process of grouping data that initially behaved differently into several groups that now behaved more uniformly. As well as grouping the data on the difficulty level of the questions on the educational game question board. This grouping of questions is needed to get the dominant values ​​that will be the characteristics of each group of questions that exist. The clustering method is quite widely used to overcome problems related to data grouping. This clustering is a method of grouping based on the size of the proximity, the more accurate the cluster formed, the clearer the similarity of the difficulty level of the questions. Thus, educational game developers can determine the strategy for placing the existing questions more precisely. Many clustering methods can be used to group the data on this question, including K-Means and Fuzzy C-Means (FCM) which are then optimized using the Algorithm Genetics. From the results of the research conducted, optimization gives better results for clustering questions.

scholarly journals Optimalisasi Penyaluran Bantuan Pemerintah Untuk UMKM Menggunakan Metode Fuzzy C-Means

2021 ◽  
Vol 5 (3) ◽  
pp. 474-482
Author(s):  
Anggara Cahya Putra ◽  
Kristoko Dwi Hartomo
Keyword(s):  
Economic Activity ◽  
Real Data ◽  
Membership Degree ◽  
Clustering Method ◽  
Micro Scale ◽  
Fuzzy C Means ◽  
Cash Assistance ◽  
Validation Test ◽  
A Value ◽  
Priority Levels

Indonesian MSMEs were very seriously affected by the Covid-19 pandemic, which caused the Indonesian economy has experienced deceleration. The Indonesian government has taken several steps to keep economic activity running, such as direct cash assistance for micro-scale businesses but is having problems in obtaining real data so that assistance is not on target, the clustering method using Fuzzy C-Means (FCM) is used for grouping MSME data. FCM allows the data to be a member of all clusters in which each cluster has a membership degree value of 0-1. The data used is from the website of the Sleman Regency Cooperatives and SME Service. FCM classifies MSME data based on the attributes of revenue, assets and number of workers. This research resulted in grouping MSME data into 3 priority levels for MSMEs in obtaining assistance, namely high priority, medium priority, and low priority. The results of this study show that the number of MSMEs with high priority is 23,023 MSMEs, medium priority is 9,774 MSMEs and low priority is 3,159 MSMEs. The validation test of the FCM method uses the Partition Coefficient Index (PCI) which has a value of 0.826 which means that value good because it is close to 1.  

scholarly journals Peningkatan Performa Cluster Fuzzy C-Means pada Klastering Sentimen Menggunakan Particle Swarm Optimization

2019 ◽  
Vol 6 (4) ◽  
pp. 349
Author(s):  
Rimbun Siringoringo ◽  
Jamaluddin Jamaluddin
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Particle Swarm ◽  
Rand Index ◽  
Convergence Time ◽  
Cluster Center ◽  
Product Review ◽  
Swarm Optimization ◽  
Fuzzy C Means ◽  
F Measure

<p class="Abstrak"><span class="fontstyle01">Fuzzy C-Means (FCM) merupakan algoritma klastering  yang sangat baik dan lebih fleksibel dari algoritma klastering konvensional. Selain kelebihan tersebut, kelemahan utama algoritma ini adalah sensitif terhadap pusat klaster. Pusat klaster yang sensitif mengakibatkan hasil akhir sulit di kontrol dan FCM  mudah terjebak  pada optimum lokal. Untuk mengatasi masalah tersebut, penelitian ini memperbaiki kinerja FCM dengan menerapkan Particle Swarm Optimization (PSO) untuk menentukan pusat klaster yang lebih baik. Penelitian ini diterapkan pada klastering sentimen dengan menggunakan data berdimensi tinggi yaitu ulasan produk yang dikumpulkan dari beberapa situs toko </span><span class="fontstyle01"><em>online</em></span><span class="fontstyle01"> di Indonesia. Hasil penelitian menunjukkan bahwa penerapan PSO pada pembangkitan pusat klaster FCM dapat memperbaiki performa FCM serta memberikan luaran yang lebih sesuai. Performa klastering yang menjadi acuan  adalah </span><span class="fontstyle01"><em>Rand Index</em></span><span class="fontstyle01">, </span><span class="fontstyle01"><em>F-Measure</em></span><span class="fontstyle01"> dan </span><span class="fontstyle01"><em>Objective Function <span lang="IN">Value</span></em></span><span class="fontstyle01"> (OFV). Untuk keseluruhan performa tersebut, FCM-PSO memberikan hasil yang lebih baik dari FCM. Nilai OFV yang lebih baik menunjukkan bahwa FCM-PSO tersebut membutuhkan waktu konvergensi yang lebih cepat serta penanganan </span><span class="fontstyle01"><em>noise</em></span><span class="fontstyle01"> yang lebih baik.</span></p><p class="Abstrak"><span class="fontstyle01"><br /></span></p><p><strong><em>Abstract</em></strong></p><p><br /><em>Fuzzy C-Means (FCM) algorithm is one of the popular fuzzy clustering techniques. Compared with the hard clustering algorithm, FCM is more flexible and fair. However, FCM is significantly sensitive to the initial cluster center and easily trapped in a local optimum. To overcome this problem, this study proposes and improved FCM with Particle Swarm Optimization (PSO) algorithm to determine a better cluster center for high dimensional and unstructured sentiment clustering. This study uses product review data collected from several online shopping websites in Indonesia. Initial processing product review data consists of Case Folding, Non Alpha Numeric Removal, Stop Word Removal, and Stemming. PSO is applied for the determination of suite cluster center. Clustering performance criteria are Rand Index, F-Measure and Objective Function Value (OFV). The results showed that FCM-PSO can provide better performance compared to the conventional FCM in terms of Rand Index, F-measure and Objective Function Values (OFV). The better OFV value indicates that FCM-PSO requires faster convergence time and better noise handling.</em></p>

scholarly journals Finding Best Clustering For Big Networks with Minimum Objective Function by Using Probabilistic Tabu Search

2019 ◽  
pp. 1837-1845
Author(s):  
Ali Falah Yaqoob ◽  
Basad Al-Sarray
Keyword(s):  
Tabu Search ◽  
Objective Function ◽  
Heuristic Algorithm ◽  
Similar Data ◽  
Clustering Method ◽  
Fuzzy C Means ◽  
Minimum Value ◽  
Data Elements ◽  
Minimum Objective Function

     Fuzzy C-means (FCM) is a clustering method used for collecting similar data elements within the group according to specific measurements. Tabu is a heuristic algorithm. In this paper, Probabilistic Tabu Search for FCM implemented to find a global clustering based on the minimum value of the Fuzzy objective function. The experiments designed for different networks, and cluster’s number the results show the best performance based on the comparison that is done between the values of the objective function in the case of using standard FCM and Tabu-FCM, for the average of ten runs.

scholarly journals Penerapan Algoritma Fuzzy C-Means Pada Penentuan Lokasi Gudang Pendukung PT. XYZ

AITI ◽  
2019 ◽  
Vol 16 (1) ◽  
pp. 31-48
Author(s):  
Brian Christian ◽  
Lukman Hakim
Keyword(s):  
Customer Satisfaction ◽  
Objective Function ◽  
Retail Sector ◽  
Objective Functions ◽  
Data Set ◽  
Number Of Clusters ◽  
Fuzzy C Means ◽  
The Difference ◽  
Function Difference ◽  
Extract Information

In data mining a series of processes are applied to extract information from a data set. PT. XYZ which has a lot of sales data that can be processed. In the company PT. XYZ which is engaged in the retail sector which has tight competition and develops rapidly, customer satisfaction is one of the things that need to be considered. It takes more than product quality, but service to customers is also important to win competition in sales and one that can be considered in customer satisfaction is the availability of products that customers want. Use of Clustering Method to classify objects based on similarity in characteristics, especially one of fuzzy clustering, that is, fuzzy C-means can be used to determine the distance and presence of each point in a cluster. In this study, fuzzy C-means is applied in determining the supporting warehouse of PT. XYZ is based on the clustering location of PT. XYZ which is represented as the Cartesian coordinates and the centroid of each cluster refers to the location of the supporting warehouse along with the grouping of outlets with the supporting warehouse.Using 3 clusters in 100 iterations the difference in objective function is 3.3e-8%, while the experiment using 4 clusters requires 39 iterations with an objective function difference of 1.1e-12%,then themore number of clusters will minimize the difference in objective functions for results with smaller errors.

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