scholarly journals A Novel Fuzzy Clustering with Metaheuristic based Resource Provisioning Technique in Cloud Environment

2021 ◽  
pp. 08-16
Author(s):  
Ahmed N. Al Al-Masri ◽  
◽  
◽  
Manal Nasir
Keyword(s):  
Resource Provisioning ◽  
Population Diversity ◽  
Grey Wolf ◽  
Cloud Environment ◽  
Grey Wolf Optimization ◽  
Configurable Computing ◽  
Local Optima ◽  
Simulation Process ◽  
De Algorithm ◽  
Fuzzy C Means Clustering

Cloud Computing (CC) becomes a commonly available tool to enable quick, on-demand services from a shared pool of configurable computing resources which can be allocated and utilized. Resource provisioning is a major issue in CC environment which ensures guaranteed outcomes on the applications related to CC. This study introduces an efficient fuzzy c-means clustering (FCM) with hybrid grey wolf optimization (GWO) and differential evolution (DE) algorithm, called FCM-GWODE for resource provisioning in cloud environment. The aim of the FCM-GWODE technique is to allocate the resources in such a way that the resource utilization can be accomplished. In addition, the FCM technique with metaheuristics is applied to partition the resources and scalable searching process can be minimized. Moreover, the GWODE algorithm is derived by resolving the local optima issue of the GWO and improve the population diversity using DE. A comprehensive simulation process takes place using CloudSim tool and the results are inspected interms of several evaluation metrics. The simulation results highlighted the supremacy of the FCM-GWODE technique over the other methods.

Low-Carbon Job Shop Scheduling Problem with Discrete Genetic-Grey Wolf Optimization Algorithm

2020 ◽  
Vol 19 (01) ◽  
pp. 1-14
Author(s):  
Jiuchun Gu ◽  
Tianhua Jiang ◽  
Huiqi Zhu ◽  
Chao Zhang
Keyword(s):  
Optimization Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Population Diversity ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Low Carbon ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Local Optima

The workshop scheduling has historically emphasized the production metrics without involving any environmental considerations. Low-carbon scheduling has attracted the attention of many researchers after the promotion of green manufacturing. In this paper, we investigate the low-carbon scheduling problem in a job shop environment. A mathematical model is first established with the objective to minimize the sum of energy-consumption cost and completion-time cost. A discrete genetic-grey wolf optimization algorithm (DGGWO) is developed to solve the problem in this study. According to the characteristics of the problem, a job-based encoding method is first employed. Then a heuristic approach and the random generation rule are combined to fulfill the population initialization. Based on the original GWO, a discrete individual updating method the crossover operation of the genetic algorithm is adopted to make the algorithm directly work in a discrete domain. Meanwhile, a mutation operator is adopted to enhance the population diversity and avoid the algorithm from getting trapped into the local optima. In addition, a variable neighborhood search is embedded to further improve the search ability. Finally, extensive simulations are conducted based on 43 benchmark instances. The experimental data demonstrate that the proposed algorithm can yield better results than the other published algorithms.

scholarly journals A Feature Weighted Fuzzy Clustering Algorithm Based on Multistrategy Grey Wolf Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yongli Liu ◽  
Zhonghui Wang ◽  
Hao Chao
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Opposition Based Learning ◽  
Fuzzy Clustering Algorithm ◽  
The Impact

Traditional fuzzy clustering is sensitive to initialization and ignores the importance difference between features, so the performance is not satisfactory. In order to improve clustering robustness and accuracy, in this paper, a feature-weighted fuzzy clustering algorithm based on multistrategy grey wolf optimization is proposed. This algorithm cannot only improve clustering accuracy by considering the different importance of features and assigning each feature different weight but also can easily obtain the global optimal solution and avoid the impact of the initialization process by implementing multistrategy grey wolf optimization. This multistrategy optimization includes three components, a population diversity initialization strategy, a nonlinear adjustment strategy of the convergence factor, and a generalized opposition-based learning strategy. They can enhance the population diversity, better balance exploration and exploitation, and further enhance the global search capability, respectively. In order to evaluate the clustering performance of our clustering algorithm, UCI datasets are selected for experiments. Experimental results show that this algorithm can achieve higher accuracy and stronger robustness.

Big Data Security Based Framework Using Metaheuristic Approaches in Cloud Environment

2020 ◽  
Vol 17 (6) ◽  
pp. 2716-2723
Author(s):  
Jyoti Parashar ◽  
Munishwar Rai
Keyword(s):  
Big Data ◽  
Data Security ◽  
Security Analysis ◽  
Scientific Workflow ◽  
Main Concern ◽  
Grey Wolf ◽  
Cloud Environment ◽  
Grey Wolf Optimization ◽  
Total Cost ◽  
The Cost

Security is the main concern in Big Data analysis. In the security analysis, most of the approaches that provide an effective security yet uses high amount of resources like memory or storage and time delay in order to increase the cost of processing. This paper emphasis on improvement based on utilization of resources by means of optimization of encryption approach. The optimisation approach uses a convex optimization with minimum amount of time or storage and such factors effects the total cost of processing. The proposed approach uses GWO optimization of Blow fish slices. The experiment involves the use of two types of dataset such as tweet and scientific workflow. The exper- imental analysis shows how effectively blow fish improves Grey wolf optimization (GWO). In addition, the other experiment uses particle swarm optimization (PSO) and flower pollination approach (FPA). Although the analysis represents that these approaches are not very much effective as compared to GWO.

scholarly journals An Improved Grey Wolf Optimization Algorithm with Variable Weights

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Zheng-Ming Gao ◽  
Juan Zhao
Keyword(s):  
Bat Algorithm ◽  
Pso Algorithm ◽  
The Novel ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Local Optima ◽  
Variable Weights ◽  
Ant Lion Optimization ◽  
Controlling Parameter ◽  
The Social

With a hypothesis that the social hierarchy of the grey wolves would be also followed in their searching positions, an improved grey wolf optimization (GWO) algorithm with variable weights (VW-GWO) is proposed. And to reduce the probability of being trapped in local optima, a new governing equation of the controlling parameter is also proposed. Simulation experiments are carried out, and comparisons are made. Results show that the proposed VW-GWO algorithm works better than the standard GWO, the ant lion optimization (ALO), the particle swarm optimization (PSO) algorithm, and the bat algorithm (BA). The novel VW-GWO algorithm is also verified in high-dimensional problems.

A Novel Variable Weight Grey Wolf Optimization Algorithm in Medical Image Fusion

2021 ◽  
Vol 11 (5) ◽  
pp. 1501-1508
Author(s):  
K. Elaiyaraja ◽  
M. Senthil Kumar
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Discrete Wavelet ◽  
Grey Wolf ◽  
Fusion Model ◽  
Grey Wolf Optimization ◽  
Local Optima ◽  
Medical Image Fusion ◽  
Variable Weight ◽  
Edge Features

Medical image fusion (MIF) is essential in clinical domain that integrates the multi-modal medical features to a unique frame known as fused image which finds utility in diagnosis process. Scaling based approaches are the commonly used multimodal MIF model where the generalized scaling has a stationary scale value selection that enhances the fusion quality Discrete Wavelet Transform (db4)-based approaches give a maximum amount of approximation in multi-modal medical image fusion, while using less edge features. For generating efficient edge features, Laplacian filtering (LF) approach is employed. This paper introduces an optimized Laplacian Wavelet Mask (OLWM) based fusion model for multi-modal MIF using Variable Weight Grey Wolf Optimization (VW-GWO). An enhanced GWO algorithm with variable weights (VW-GWO) is faced with the idea of using the social hierarchy of the grey wolves to locate the searching positions. Besides, to minimize the possibility of trapping into local optima, an efficient parameter control mechanism is employed. The VW-GWO algorithm has the capability to choose the control variables of the GWO algorithm in an automated way. A set of medical images, including MR-SPECT, MR-PET, MR-CT and MR: T1–T2 of brain scans, validates the proposed VW-GWO algorithm. The simulation outcome showed that the effectiveness of the VW-GWO algorithm seems to be much higher over the compared methods under various dimensions.

Evaluation of Grey Wolf Optimization Algorithm on Rigid and Flexible Receptor Docking

2020 ◽  
Author(s):  
Kin Meng Wong ◽  
Shirley Siu
Keyword(s):  
Scoring Function ◽  
Ligand Docking ◽  
Receptor Protein ◽  
Pose Prediction ◽  
Grey Wolf ◽  
Success Rates ◽  
Autodock Vina ◽  
Grey Wolf Optimization ◽  
Structure Based Drug Design ◽  
Docking Program

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein in current structure-based drug design. In this paper, we evaluate the performance of grey wolf optimization (GWO) in protein-ligand docking. Two versions of the GWO docking program – the original GWO and the modified one with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments show that the GWO programs have enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO methods are competitive in pose ranking but lower in success rates than AutoDockFR. Successful redocking of all the flexible cases to their holo structures reveals that inaccurate scoring function and lack of proper treatment of backbone are the major causes of docking failures.

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