scholarly journals Opposition Based Joint Grey Wolf-Whale Optimization Algorithm Based Attribute Based Encryption in Secure Wireless Communication

2021 ◽  
Author(s):  
M. Raja ◽  
S. Dhanasekaran ◽  
V. Vasudevan
Keyword(s):  
Wireless Communication ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Grey Wolf ◽  
Attribute Based Encryption ◽  
Whale Optimization

scholarly journals Opposition Based Joint Grey Wolf-whale Optimization Algorithm Based Attribute Based Encryption in Secure Wireless Communication

2021 ◽  
Author(s):  
Raja M ◽  
Dhanasekaran s ◽  
Vasudevan v
Keyword(s):  
Optimization Algorithm ◽  
Performance Metrics ◽  
Whale Optimization Algorithm ◽  
Healthcare Sector ◽  
Grey Wolf ◽  
Time Period ◽  
Attribute Based Encryption ◽  
Whale Optimization ◽  
Encryption Method ◽  
Dynamic Key

Abstract At present times, medical image security becomes a hot research topic in the healthcare sector. This paper presents an efficient lightweight image encryption model based on the Dynamic key generating Attribute based encryption (ABE) method with Opposition based joint Grey Wolf-Whale Optimization Algorithm (OjGW-WOA). The proposed encryption method undergoes certain pre-encryption steps like rotation and random column addition steps. Once the pre-encryption steps are done, ABE with OjGW-WOA is incorporated, where the optimal key is generated based on entropy value. In addition, the oppositional based learning (OBL) concept is introduced to enhance the convergence rate and searching process of GWO and WOA algorithms. Next, the proposed encryption method is designed with a dynamic key generating model that generates updated keys during every time period. Therefore, during decryption, two-level key verification is done. At the first decryption stage, the key corresponding to that particular time period is required, then the original key is generated from that key and then employed for decrypting the original data. The proposed method is simulated using MATLAB tool and a detailed comparative results analysis is carried out. The performance of the proposed work is validated with the aid of performance metrics like Peak Signal to Noise Ratio (PSNR), number of changing pixel rate (NPCR) and unified averaged changed intensity (UACI). The experimental results stated that the presented model has resulted to a higher PSNR of 62.29dB, NPCR of 99.23%, and UACI of 23.67%.

Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm

2021 ◽  
pp. 107754632110034
Author(s):  
Ololade O Obadina ◽  
Mohamed A Thaha ◽  
Kaspar Althoefer ◽  
Mohammad H Shaheed
Keyword(s):  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Robot Manipulator ◽  
Moving Average ◽  
Identification Problem ◽  
Whale Optimization Algorithm ◽  
Grey Wolf Optimizer ◽  
Parametric Modelling ◽  
Grey Wolf ◽  
Whale Optimization

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.

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

2019 ◽  
Vol 9 (18) ◽  
pp. 3755 ◽  
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.

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

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.

scholarly journals Grey Wolf and Weighted Whale Algorithm Optimized IT2 Fuzzy Sliding Mode Backstepping Control with Fractional-Order Command Filter for a Nonlinear Dynamic System

2021 ◽  
Vol 11 (2) ◽  
pp. 489
Author(s):  
Seongik Han
Keyword(s):  
Fuzzy Logic ◽  
Fractional Order ◽  
Optimization Algorithm ◽  
Sliding Mode ◽  
Fuzzy Logic System ◽  
Backstepping Control ◽  
Whale Optimization Algorithm ◽  
Grey Wolf ◽  
Whale Optimization ◽  
Logic System

In this study, a fractional-order sliding mode backstepping control method was proposed, which involved the use of a fractional-order command filter, an interval type-2 fuzzy logic system approximation method, and a grey wolf and weighted whale optimization algorithm for multi-input multi-output nonlinear dynamic systems. For designing the stabilizing controls of the backstepping control, a novel fractional-order sliding mode surface was suggested. Further, the transformed errors that occurred during the recursive design steps were easily compensated by the controllers constructed using a new fractional-order command filter. Thus, the differentiation issue of the virtual control in the conventional backstepping control design could be bypassed with a simpler controller structure. Subsequently, the unknown plant dynamics were approximated by an interval type-2 fuzzy logic system. The uncertainties, such as the approximation error and the external disturbance, were compensated by the fractional-order sliding mode control that was added in the backstepping controller. Furthermore, the controller parameters and the fuzzy logic system were optimized via a grey wolf and weighted whale optimization algorithm to obtain a faster tuning process and an improved control performance. Simulation results demonstrated that the fractional-order sliding mode backstepping control scheme provides enhanced control performance over the conventional backstepping control system. Thus, in this paper, a fractional-order sliding mode surface and fractional-order backstepping control are studied, which provide more rapid convergence and enhanced robustness. Furthermore, a hybrid grey wolf and weighted whale optimization algorithm are proposed to provide an improved learning performance than those of conventional grey wolf optimization and weighted whale optimization methods.

scholarly journals Improved grey wolf algorithm for optimization problems

2021 ◽  
Vol 22 (3) ◽  
pp. 1573
Author(s):  
Hafiz Maaz Asgher ◽  
Yana Mazwin Mohmad Hassim ◽  
Rozaida Ghazali ◽  
Muhammad Aamir
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Research Work ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Grey Wolf ◽  
Exploration And Exploitation ◽  
Grey Wolf Optimization ◽  
Whale Optimization

The grey wolf optimization (GWO) is a nature inspired and meta-heuristic algorithm, it has successfully solved many optimization problems and give better solution as compare to other algorithms. However, due to its poor exploration capability, it has imbalance relation between exploration and exploitation. Therefore, in this research work, the poor exploration part of GWO was improved through hybrid with whale optimization algorithm (WOA) exploration. The proposed grey wolf whale optimization algorithm (GWWOA) was evaluated on five unimodal and five multimodal benchmark functions. The results shows that GWWOA offered better exploration ability and able to solve the optimization problem and give better solution in search space. Additionally, GWWOA results were well balanced and gave the most optimal in search space as compare to the standard GWO and WOA algorithms.

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