KERNEL-BASED EXPONENTIAL GREY WOLF OPTIMIZER FOR RAPID CENTROID ESTIMATION IN DATA CLUSTERING

2016 ◽  
Vol 78 (11) ◽  
Author(s):  
Amolkumar Narayan Jadhav ◽  
Gomathi N.
Keyword(s):  
Data Clustering ◽  
Objective Evaluation ◽  
Multidimensional Data ◽  
Grey Wolf Optimizer ◽  
Similar Data ◽  
Grey Wolf ◽  
Data Set ◽  
Wide Range ◽  
Centroid Estimation ◽  
Two Parameters

Clustering finds variety of application in a wide range of disciplines because it is mostly helpful for grouping of similar data objects together. Due to the wide applicability, different algorithms have been presented in the literature for segmenting large multidimensional data into discernible representative clusters. Accordingly, in this paper, Kernel-based exponential grey wolf optimizer (KEGWO) is developed for rapid centroid estimation in data clustering. Here, KEGWO is newly proposed to search the cluster centroids with a new objective evaluation which considered two parameters called logarithmic kernel function and distance difference between two top clusters. Based on the new objective function and the modified KEGWO algorithm, centroids are encoded as position vectors and the optimal location is found for the final clustering. The proposed KEGWO algorithm is evaluated with banknote authentication Data Set, iris dataset and wine dataset using four metrics such as, Mean Square Error, F-measure, Rand co-efficient and jaccord coefficient. From the outcome, we proved that the proposed KEGWO algorithm outperformed the existing algorithms.   

EKEGWO: Enhanced Kernel-Based Exponential Grey Wolf Optimizer for Bi-Objective Data Clustering

2019 ◽  
Vol 27 (04) ◽  
pp. 669-688 ◽  
Author(s):  
Amolkumar Narayan Jadhav ◽  
Gomathi N.
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Fitness Function ◽  
Multidimensional Data ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Widespread Application ◽  
Clustering Techniques ◽  
Cluster Distance ◽  
F Measure

The widespread application of clustering in various fields leads to the discovery of different clustering techniques in order to partition multidimensional data into separable clusters. Although there are various clustering approaches used in literature, optimized clustering techniques with multi-objective consideration are rare. This paper proposes a novel data clustering algorithm, Enhanced Kernel-based Exponential Grey Wolf Optimization (EKEGWO), handling two objectives. EKEGWO, which is the extension of KEGWO, adopts weight exponential functions to improve the searching process of clustering. Moreover, the fitness function of the algorithm includes intra-cluster distance and the inter-cluster distance as an objective to provide an optimum selection of cluster centroids. The performance of the proposed technique is evaluated by comparing with the existing approaches PSC, mPSC, GWO, and EGWO for two datasets: banknote authentication and iris. Four metrics, Mean Square Error (MSE), F-measure, rand and jaccord coefficient, estimates the clustering efficiency of the algorithm. The proposed EKEGWO algorithm can attain an MSE of 837, F-measure of 0.9657, rand coefficient of 0.8472, jaccord coefficient of 0.7812, for the banknote dataset.

scholarly journals A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2147 ◽  
Author(s):  
Zhihang Yue ◽  
Sen Zhang ◽  
Wendong Xiao
Keyword(s):  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimization Method ◽  
Grey Wolf Optimizer ◽  
Local Optimum ◽  
Grey Wolf ◽  
Optimal Search ◽  
Fireworks Algorithm ◽  
Wide Range ◽  
Search Capability

Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA.

Some Words About Nature-Inspired Computing

2022 ◽  
pp. 1-9
Author(s):  
Mohamed Arezki Mellal
Keyword(s):  
Artificial Intelligence ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Swarm Optimization ◽  
Plant Propagation ◽  
Cuckoo Optimization Algorithm ◽  
Propagation Algorithm ◽  
Wide Range ◽  
Nature Inspired Computing ◽  
Computing Approach

The use of artificial intelligence (AI) in various domains has drastically increased during the last decade. Nature-inspired computing is a strong computing approach that belongs to AI and covers a wide range of techniques. It has successfully tackled many complex problems and outperformed several classical techniques. This chapter provides the original ideas behind some nature-inspired computing techniques and their applications, such as the genetic algorithms, particle swarm optimization, grey wolf optimizer, ant colony optimization, plant propagation algorithm, cuckoo optimization algorithm, and artificial neural networks.

scholarly journals IMPLEMENTASI ALGORITMA GREY WOLF OPTIMIZER (GWO) DI TOKO CITRA TANI JEMBER

2019 ◽  
Vol 19 (2) ◽  
pp. 65
Author(s):  
Vidiyanti Lestari ◽  
Ahmad Kamsyakawuni ◽  
Kiswara Agung Santoso
Keyword(s):  
Simplex Method ◽  
Primary Data ◽  
Grey Wolf Optimizer ◽  
Multiple Constraints ◽  
Grey Wolf ◽  
Population Parameters ◽  
Storage Media ◽  
Optimal Value ◽  
Comparison Of The Results ◽  
Two Parameters

Generally, optimization is defined as the process of determining the minimum or maximum value that depends on the function of the goal, even now there are many problems regarding optimization. One of them is the problem regarding the selection of goods to be included in a limited storage medium called Knapsack problem. Knapsack problems have different types and variations. This study will solve the problem of bounded knapsack multiple constraints by implementing the Grey Wolf Optimizer (GWO) algorithm. The problem of bounded knapsack multiple constraints has more than one subject with the items that are inserted into the dimension storage media can be partially or completely inserted, but the number of objects is limited. The aim of this study is to determine the results of using the Grey Wolf Optimizer (GWO) algorithm for solving the problem of multiple constraints bounded knapsack and compare the optimal solutions obtained by the simplex method using the Solver Add-In in Microsoft Excel. The data used in this study is primary data. There are two parameters to be tested, namely population parameters and maximum iteration. The test results of the two parameters show that the population parameters and maximum iterations have the same effect, where the greater the value of the population parameters and the maximum iteration, the results obtained are also getting closer to the optimal value. In addition, based on the results of the final experiment it is known that the comparison of the results of the GWO algorithm and the simplex method has a fairly small percentage deviation which indicates that the GWO algorithm produces results that are close to the optimal value. Keywords: GWO algorithm, Knapsack, Multiple Constraints Bounded Knapsack.

scholarly journals Development of a new semi-empirical parameterization for below-cloud scavenging of size-resolved aerosol particles by both rain and snow

2013 ◽  
Vol 6 (4) ◽  
pp. 5901-5945 ◽  
Author(s):  
X. Wang ◽  
L. Zhang ◽  
M. D. Moran
Keyword(s):  
Linear Relationship ◽  
Aerosol Particle ◽  
Field Experiments ◽  
Aerosol Particles ◽  
Particle Sizes ◽  
Data Set ◽  
Wide Range ◽  
Cloud Scavenging ◽  
Two Parameters ◽  
Semi Empirical

Abstract. A parameter called the scavenging coefficient Λ is widely used in aerosol chemical transport models (CTMs) to describe below-cloud scavenging of aerosol particles by rain and snow. However, uncertainties associated with available size-resolved theoretical formulations for Λ span one to two orders of magnitude for rain scavenging and nearly three orders of magnitude for snow scavenging. Two recent reviews of below-cloud scavenging of size-resolved particles recommended that the upper range of the available theoretical formulations for Λ should be used in CTMs based on uncertainty analyses and comparison with limited field experiments. Following this recommended approach, a new semi-empirical parameterization for size-resolved Λ has been developed for below-cloud scavenging of atmospheric aerosol particles by both rain (Λrain) and snow (Λsnow). The new parameterization is based on the 90th percentile of Λ values from an ensemble data set containing calculated using all possible "realizations" of available theoretical Λ formulas and covering a large range of aerosol particle sizes and precipitation intensities (R). For any aerosol particle size of diameter d, a strong linear relationship between the 90th-percentile log10(Λ) and log10(R), which is equivalent to a power-law relationship between Λ and R, is identified. The log-linear relationship, which is characterized by two parameters (slope and y-intercept), is then further parameterized by fitting these two parameters as polynomial functions of aerosol size d. A comparison of the new parameterization with limited measurements in the literature in terms of the magnitude of Λ and the relative magnitudes of Λrain and Λsnow suggests that it is a reasonable approximation. Advantages of this new semi-empirical parameterization compared to traditional theoretical formulations for Λ include its applicability to below-cloud scavenging by both rain and snow over a wide range of particle sizes and precipitation intensities, ease of implementation in any CTM with a representation of size-distributed particulate matter, and a known representativeness based on the consideration in its development of all available theoretical formulations and field-derived estimates for Λ(d) and their associated uncertainties.

scholarly journals Specialios struktūros daugiasluoksnis perceptronas daugiamačiams duomenims vizualizuoti

2009 ◽  
Vol 50 ◽  
pp. 358-364
Author(s):  
Laura Ringienė ◽  
Gintautas Dzemyda
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Multilayer Perceptron ◽  
Data Clustering ◽  
Multidimensional Data ◽  
Feed Forward Neural Network ◽  
Data Set ◽  
Multidimensional Data Visualization ◽  
Radial Basis ◽  
Hidden Layer

Pasiūlytas ir ištirtas radialinių bazinių funkcijų ir daugiasluoksnio perceptrono junginys daugiamačiams duomenis vizualizuoti. Siūlomas vizualizavimo būdas apima daugiamačių duomenų matmenų mažinimą naudojant radialines bazines funkcijas, daugiamačių duomenų suskirstymą į klasterius, klasterį charakterizuojančių skaitinių reikšmių nustatymą ir daugiamačių duomenų vizualizavimą dirbtinio neuroninio tinklo paskutiniame paslėptajame sluoksnyje.Special Multilayer Perceptron for Multidimensional Data VisualizationLaura Ringienė, Gintautas Dzemyda SummaryIn this paper a special feed forward neural network, consisting of the radial basis function layer and a multilayer perceptron is presented. The multilayer perceptron has been proposed and investigated for multidimensional data visualization. The roposedvisualization approach includes data clustering, determining the parameters of the radial basis function and forming the data set to train the multilayer perceptron. The outputs of the last hidden layer are assigned as coordinates of the visualized points.

scholarly journals Heavy metals risk assessment in water and bottom sediments of ICOLLs in Northern Poland

2018 ◽  
Keyword(s):  
Heavy Metals ◽  
Risk Assessment ◽  
Bottom Sediments ◽  
Sea Water ◽  
Ecological Status ◽  
Multidimensional Data ◽  
Lake Basin ◽  
Sample Type ◽  
Data Set ◽  
Wide Range

<p>Natural diversity of intermittently closed and open lakes and lagoons (ICOLLs) depends on mutual interactions of several factors: (i) an impact of sea water and land background; (ii) temporary meteorological situation; (iii) hydrological conditions; and (iv) the shape of lake basin. However, some regional, local or even sudden impacts including anthropogenic ones create their final ecological status. To identify heavy metals risk assessment in ICOLLs located in Polish coastline wide range of them were determined in water and bottom sediment samples collected in 10 water reservoirs. Multidimensional data set of 20 variables was explored by the use of chemometrics according to seasonality (Spring, Summer, Autumn), sample type (water, sediment) and level of isolation (fully isolated, partially and fully connected lakes). The results showed that 70.5% and 77% of the data variance can be explained by the use of principal component analysis for waters and sediments, respectively. Waters of fully isolated or partially connected lakes are more abundant with Ir, Nd and Sm, while less abundant with Pr and Sr. Bottom sediments taken from Jamno lake show significant contamination by heavy metals of the highest environmental concern (Al, Cr, Cu, Ni, Ti and Zn).</p>

A Modified Grey Wolf Optimizer Based Data Clustering Algorithm

2020 ◽  
Vol 35 (1) ◽  
pp. 63-79
Author(s):  
Ramin Ahmadi ◽  
Gholamhossein Ekbatanifard ◽  
Peyman Bayat
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Grey Wolf Optimizer ◽  
Grey Wolf
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