WGC: Hybridization of exponential grey wolf optimizer with whale optimization for data clustering

This article presents a novel hybrid algorithm based on the grey-wolf optimizer and whale optimization algorithm, referred here as grey-wolf optimizer–whale optimization algorithm, for the dynamic parametric modelling of a four degree-of-freedom master–slave robot manipulator system. The first part of this work consists of testing the feasibility of the grey-wolf optimizer–whale optimization algorithm by comparing its performance with a grey-wolf optimizer, whale optimization algorithm and particle swarm optimization using 10 benchmark functions. The grey-wolf optimizer–whale optimization algorithm is then used for the model identification of an experimental master–slave robot manipulator system using the autoregressive moving average with exogenous inputs model structure. Obtained results demonstrate that the hybrid algorithm is effective and can be a suitable substitute to solve the parameter identification problem of robot models.

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Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

Applied Sciences ◽

10.3390/app9183755 ◽

2019 ◽

Vol 9 (18) ◽

pp. 3755 ◽

Cited By ~ 49

Author(s):

Wei Chen ◽

Haoyuan Hong ◽

Mahdi Panahi ◽

Himan Shahabi ◽

Yi Wang ◽

...

Keyword(s):

Optimization Algorithm ◽

Landslide Susceptibility ◽

Heuristic Algorithms ◽

Whale Optimization Algorithm ◽

Economic Losses ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Prediction Ability ◽

Inference System ◽

Whale Optimization

The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA and GWO are used as two meta-heuristic algorithms to improve the prediction performance of the ANFIS-based methods. In addition, the step-wise weight assessment ratio analysis (SWARA) method is used to obtain the initial weight of each class of landslide influencing factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO models are assessed using the measures of the receiver operating characteristic (ROC) curve and root-mean-square error (RMSE). The experiments demonstrated that the obtained results of modelling process from the SWARA to the SAWRA-ANFIS-GWO model were more accurate and that the proposed methods have satisfactory prediction ability. Specifically, prediction accuracy by area under the curve (AUC) of SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-GWO, and SWARA-ANFIS-WOA models were 0.831, 0.831, 0.850, 0.856, and 0.869, respectively. Due to adaptability and usability, the proposed prediction methods can be applied to other areas for landslide management and mitigation as well as prevention throughout the world.

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

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488519500296 ◽

2019 ◽

Vol 27 (04) ◽

pp. 669-688 ◽

Cited By ~ 1

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.

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

Jurnal Teknologi ◽

10.11113/.v78.8057 ◽

2016 ◽

Vol 78 (11) ◽

Cited By ~ 2

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.

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Grey Wolf Optimizer Based Web usage Data Clustering with Enhanced Fuzzy C Means Algorithm

International Journal of Data Mining Techniques and Applications ◽

10.20894/ijdmta.102.006.001.003 ◽

2017 ◽

Vol 6 (1) ◽

pp. 12-16

Author(s):

P. Selvaraju ◽

◽

B. Kalaavathi ◽

Keyword(s):

Data Clustering ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Fuzzy C Means ◽

Web Usage ◽

Fuzzy C Means Algorithm ◽

Usage Data

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A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem

Mathematical Problems in Engineering ◽

10.1155/2021/3517145 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Jafar Ababneh

Keyword(s):

Cloud Computing ◽

Energy Consumption ◽

Task Scheduling ◽

Virtual Machines ◽

Hybrid Approach ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Use Of Resources ◽

Whale Optimization ◽

Resources Assessment

In the context of cloud computing, one problem that is frequently encountered is task scheduling. This problem has two primary implications, which are the planning of tasks on virtual machines and the attenuation of performance. In order to address the problem of task scheduling in cloud computing, requisite nontraditional optimization attitudes to attain the optima of the problem, the present paper puts forth a hybrid multiple-objective approach called hybrid grey wolf and whale optimization (HGWWO) algorithms, that integrates two algorithms, namely, the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA), with the purpose of conjoining the advantages of each algorithm for minimizing costs, energy consumption, and total execution time needed for task implementation, beside that improving the use of resources. Assessment of the aims of the proposed approach is carried out with the help of the tool known as CloudSim. As pointed out by the results of the experimental work undertaken, the proposed approach has the capability of performing at a superior level by comparison to the original algorithms GWO and WOA on their own with regard to costs, energy consumption, makespan, use of resources, and degree of imbalance.

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Enhanced whale optimization algorithm for sizing of hybrid wind/photovoltaic/diesel with battery storage in Algeria desert

Wind Engineering ◽

10.1177/0309524x211056529 ◽

2021 ◽

pp. 0309524X2110565

Author(s):

Adel Yahiaoui ◽

Abdelhalim Tlemçani

Keyword(s):

Renewable Energy ◽

Optimization Algorithm ◽

Optimization Problem ◽

Energy System ◽

Whale Optimization Algorithm ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Hybrid Renewable Energy System ◽

Whale Optimization ◽

Hybrid Renewable Energy

This paper focuses on the optimization and operation of the renewable energy power sources for electrification of isolated rural city in Algeria desert. For this purpose, a system composed by photovoltaic (PV), wind turbine (WT), diesel generator (DG), and battery bank (BB) as well as for storing the energy in the electrical form to meet the load. In the present paper we are interested in evolutionary algorithms for solving optimization problem of hybrid renewable energy system. A new meta-heuristic algorithm namely whale optimization algorithm (WOA) is used to solve optimization problem of cost of energy (COE) and total net present cost (TNPC) including reliability evaluation by using basic probabilistic concept in order to find Loss of Power Supply Probability (LPSP). The WOA mimics the social behavior of humpback whales. This algorithm is inspired by the bubble-net hunting strategy. Three recent algorithms, particle swarm optimization (PSO), grey wolf optimizer (GWO), and modified grey wolf optimizer (M-GWO) are also implemented in this work. For examining the accuracy, stability, and robustness of proposed optimization technique two case studies have been tested. The results of simulations and comparison with other methods exhibit high accuracy and validity of the proposed whale optimization algorithm to solve optimization problem of hybrid renewable energy system.

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