A secure data protection technique for healthcare data in the cloud using homomorphic encryption and Jaya–Whale optimization algorithm

2019 ◽  
Vol 10 (06) ◽  
pp. 1950040 ◽  
Author(s):  
I. Sudha ◽  
R. Nedunchelian
Keyword(s):  
Optimization Algorithm ◽  
Data Protection ◽  
Secure Communication ◽  
Homomorphic Encryption ◽  
Optimal Solution ◽  
Original Data ◽  
Whale Optimization Algorithm ◽  
Superior Performance ◽  
Whale Optimization ◽  
Secure Data

Cloud computing has become a powerful mechanism for initiating secure communication among users. The advancements in the technology lead to provide various services, like accessing network, resources, and platform. However, handling large datasets and security are major issues in the cloud systems. Hence, this paper proposes a technique, namely, Jaya–Whale Optimization (JWO), which is the integration of Jaya algorithm and Whale optimization algorithm (WOA) and adapts homomorphic encryption for initiating secure data transmission in the cloud. The original data are preserved by generating Data Protection (DP) coefficient using the proposed JWO algorithm. In the proposed algorithm, the fitness is calculated based on privacy and utility parameters for selecting the optimal solution. Also, the sanitized data are generated by EXORing the Key Information Product (KIP) matrix and key vector. Finally, the data owner provides the key to the users for retrieving the original data from the sanitized data. The experimentation is carried out using Cleveland, Hungarian, and Switzerland datasets in terms of BD, accuracy, and fitness and the analysis shows that the proposed JWO provides superior performance in terms of BD, accuracy, and fitness parameters with values 0.720, 0.822, and 0.722.

Whale optimization algorithm based on lateral inhibition for image matching and vision-guided AUV docking

2020 ◽  
pp. 1-12
Author(s):  
Zheping Yan ◽  
Jinzhong Zhang ◽  
Jialing Tang
Keyword(s):  
Lateral Inhibition ◽  
Optimization Algorithm ◽  
Image Matching ◽  
Autonomous Underwater Vehicle ◽  
Optimal Solution ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Inhibition Mechanism ◽  
Whale Optimization ◽  
Accuracy And Stability

The accuracy and stability of relative pose estimation of an autonomous underwater vehicle (AUV) and a target depend on whether the characteristics of the underwater image can be accurately and quickly extracted. In this paper, a whale optimization algorithm (WOA) based on lateral inhibition (LI) is proposed to solve the image matching and vision-guided AUV docking problem. The proposed method is named the LI-WOA. The WOA is motivated by the behavior of humpback whales, and it mainly imitates encircling prey, bubble-net attacking and searching for prey to obtain the globally optimal solution in the search space. The WOA not only balances exploration and exploitation but also has a faster convergence speed, higher calculation accuracy and stronger robustness than other approaches. The lateral inhibition mechanism can effectively perform image enhancement and image edge extraction to improve the accuracy and stability of image matching. The LI-WOA combines the optimization efficiency of the WOA and the matching accuracy of the LI mechanism to improve convergence accuracy and the correct matching rate. To verify its effectiveness and feasibility, the WOA is compared with other algorithms by maximizing the similarity between the original image and the template image. The experimental results show that the LI-WOA has a better average value, a higher correct rate, less execution time and stronger robustness than other algorithms. The LI-WOA is an effective and stable method for solving the image matching and vision-guided AUV docking problem.

scholarly journals Application of Whale Optimization Algorithm to Inverse Scattering of an Imperfect Conductor with Corners

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Kun-Chou Lee ◽  
Pai-Ting Lu
Keyword(s):  
Inverse Scattering ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Spline Interpolation ◽  
Optimal Solution ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Hunting Behavior ◽  
Whale Optimization ◽  
The Moment

In this paper, the whale optimization algorithm (WOA) is applied to the inverse scattering of an imperfect conductor with corners. The WOA is a new metaheuristic optimization algorithm. It mimics the hunting behavior of humpback whales. The inspiration results from the fact that a whale recognizes the location of a prey (i.e., optimal solution) by swimming around the prey within a shrinking circle and along a spiral-shaped path simultaneously. Initially, the inverse scattering is first transformed into a nonlinear optimization problem. The transformation is based on the moment method solution for scattering integral equations. To treat a target with corners and implement the WOA inverse scattering, the cubic spline interpolation is utilized for modelling the target shape function. Numerical simulation shows that the inverse scattering by WOA not only is accurate but also converges fast.

A Cloud-based Conceptual Framework for Multi-Objective Virtual Machine Scheduling using Whale Optimization Algorithm

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Nadim Rana ◽  
Muhammad Shafie Abd Latiff ◽  
Shafi'i Muhammad Abdulhamid
Keyword(s):  
Conceptual Framework ◽  
Virtual Machine ◽  
Optimization Algorithm ◽  
Virtual Machines ◽  
Optimal Solution ◽  
Machine Scheduling ◽  
Whale Optimization Algorithm ◽  
Multi Objective ◽  
Whale Optimization ◽  
Virtual Machine Scheduling

Virtual machine scheduling in the cloud is considered one of the major issue to solve optimal resource allocation problem on the heterogeneous datacenters. With respect to that, the key concern is to map the virtual machines (VMs) with physical machines (PMs) in a way that maximum resource utilization can be achieved with minimum cost. Due to the fact that scheduling is an NP-hard problem, a metaheuristic approach is proven to achieve a better optimal solution to solve this problem. In a rapid changing heterogeneous environment, where millions of resources can be allocated and deallocate in a fraction of the time, modern metaheuristic algorithms perform well due to its immense power to solve the multidimensional problem with fast convergence speed. This paper presents a conceptual framework for solving multi-objective VM scheduling problem using novel metaheuristic Whale optimization algorithm (WOA). Further, we present the problem formulation for the framework to achieve multi-objective functions.

scholarly journals Whale Optimization Algorithm with Applications to Power Allocation in Interference Networks

2021 ◽  
Vol 50 (2) ◽  
pp. 390-405
Author(s):  
Yongwen Du ◽  
Xiquan Zhang ◽  
Wenxian Zhang ◽  
Zhangmin Wang
Keyword(s):  
Power Allocation ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Whale Optimization Algorithm ◽  
Local Optimum ◽  
Network Computing ◽  
Archimedean Spiral ◽  
Network Resource ◽  
Communication Performance ◽  
Whale Optimization

Power allocation plays a pivotal role in improving the communication performance of interference-limitedwireless network (IWN). However, the optimization of power allocation is usually formulated as a mixed-integernon-linear programming (MINLP) problem, which is hard to solve. Whale optimization algorithm (WOA)has recently gained the attention of the researcher as an efficient method to solve a variety of optimizationproblems. WOA algorithm also has the disadvantages of low convergence accuracy and easy to fall into local optimum.To solve the above problems, we propose Cosine Compound Whale Optimization Algorithm (CCWOA).First of all, its unique cosine nonlinear convergence factor can balance the rate of the whole optimization processand prevent the convergence speed from being too fast. Secondly, the inertia weight and sine vector canincrease the probability of jumping out of the local optimal solution. Finally, the Archimedean spiral can reducethe risk of losing the optimal solution. A representative benchmark function is selected to test the convergencerate of CCWOA algorithm and the optimization performance of jumping out of local optimum. Compared withthe representative algorithms PFP and GAP, the optimization effect of CCWOA is almost consistent with theabove two algorithms, and even exceeds 4% - 6% in numerical value. The advantage of CCWOA is that it haslower algorithm complexity, which has a good advantage when the network computing resources are fixed. Inaddition, the optimization effect of CCWOA is higher than that of WOA, which lays a good foundation for furtherapplication of swarm intelligence optimization algorithm in network resource allocation.

scholarly journals Closed-Loop Supply Chain Design and Pricing in Competitive Conditions by Considering the Variable Value of Return Products Using the Whale Optimization Algorithm

2021 ◽  
Vol 13 (12) ◽  
pp. 6663
Author(s):  
Muhammad Salman Shabbir ◽  
Ahmed Faisal Siddiqi ◽  
Lis M. Yapanto ◽  
Evgeny E. Tonkov ◽  
Andrey Leonidovich Poltarykhin ◽  
...  
Keyword(s):  
Supply Chain ◽  
Optimization Algorithm ◽  
Closed Loop ◽  
Optimal Solution ◽  
Supply Chain Design ◽  
Whale Optimization Algorithm ◽  
Special Focus ◽  
Closed Loop Supply Chain ◽  
Proposed Model ◽  
Whale Optimization

In today’s competitive environment, organizations, in addition to trying to improve their production conditions, have a special focus on their supply chain components. Cooperation between supply chain members always reduces unforeseen costs and speeds up the response to customer demand. In the new situation, according to the category of return products and their reprocessing, supply chains have found a closed-loop structure. In this research, the aim was to design a closed-loop supply chain in competitive conditions. For this purpose, the key decisions of this chain included locating retail centers, adjusting the inventory of chain members, and selling prices of final products, optimally determined. For this purpose, a nonlinear integer mathematical model is presented. One of the most important innovations of this research was considering the variable value for return products. Then, in order to solve the proposed model, a whale optimization algorithm was developed. Numerical results from the sample examples showed that the whale algorithm had a very good performance in terms of response quality and speed-of-action in finding the optimal solution to this problem.

scholarly journals Optimal Solution of Combined Heat and Power Dispatch Problem Using Whale Optimization Algorithm

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Optimization Algorithm ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Energy System ◽  
Combined Heat And Power ◽  
Whale Optimization Algorithm ◽  
Computational Time ◽  
Whale Optimization ◽  
Input Variables ◽  
Working Region

In this article whale optimization algorithm (WOA) has been applied to solve the combined heat and power economic dispatch (CHPED) problem. The CHPED is energy system which provides both heat and power. Due to presence of valve point loading and the prohibited working region, the CHPED problems become more complex one. The main objective of CHPED problem is to minimize the total cost of fuel as well as heat with fulfill the load demand. This optimization technique shows several advantages like having few input variables, best quality of solution with rapid computational time. The recommended approach is carried out on three test systems. The simulation results of the present work certify the activeness of the proposed technique.

scholarly journals Dynamic economic emission dispatch using whale optimization algorithm for multi-objective function

2021 ◽  
pp. 64-69
Author(s):  
M. F. Mehdi ◽  
A. Ahmad ◽  
S. S. Ul Haq ◽  
M. Saqib ◽  
M. F. Ullah
Keyword(s):  
Simulated Annealing ◽  
Differential Evolution ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Whale Optimization Algorithm ◽  
Power Network ◽  
Multi Objective ◽  
Whale Optimization ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

Introduction. Dynamic Economic Emission Dispatch is the extended version of the traditional economic emission dispatch problem in which ramp rate is taken into account for the limit of generators in a power network. Purpose. Dynamic Economic Emission Dispatch considered the treats of economy and emissions as competitive targets for optimal dispatch problems, and to reach a solution it requires some conflict resolution. Novelty. The decision-making method to solve the Dynamic Economic Emission Dispatch problem has a goal for each objective function, for this purpose, the multi-objective problem is transformed into single goal optimization by using the weighted sum method and then control/solve by Whale Optimization Algorithm. Methodology. This paper presents a newly developed metaheuristic technique based on Whale Optimization Algorithm to solve the Dynamic Economic Emission Dispatch problem. The main inspiration for this optimization technique is the fact that metaheuristic algorithms are becoming popular day by day because of their simplicity, no gradient information requirement, easily bypass local optima, and can be used for a variety of other problems. This algorithm includes all possible factors that will yield the minimum cost and emissions of a Dynamic Economic Emission Dispatch problem for the efficient operation of generators in a power network. The proposed approach performs well to perform in diverse problem and converge the solution to near best optimal solution. Results. The proposed strategy is validated by simulating on MATLAB® for 5 IEEE standard test system. Numerical results show the capabilities of the proposed algorithm to establish an optimal solution of the Dynamic Economic Emission Dispatch problem in a several runs. The proposed algorithm shows good performance over the recently proposed algorithms such as Multi-Objective Neural Network trained with Differential Evolution, Particle swarm optimization, evolutionary programming, simulated annealing, Pattern search, multi-objective differential evolution, and multi-objective hybrid differential evolution with simulated annealing technique.

scholarly journals An Improved Optimal Solution for the Directional Overcurrent Relays Coordination Using Hybridized Whale Optimization Algorithm in Complex Power Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 90418-90435 ◽  
Author(s):  
Tahir Khurshaid ◽  
Abdul Wadood ◽  
Saeid Gholami Farkoush ◽  
Jiangtao Yu ◽  
Chang-Hwan Kim ◽  
...  
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Whale Optimization Algorithm ◽  
Whale Optimization ◽  
Complex Power ◽  
Overcurrent Relays

Multifactor model for the association between time-averaged serum albumin and clinical factors of hemodialysis patients based on the whale optimization algorithm (Preprint)

2020 ◽  
Author(s):  
Cheng-Hong Yang ◽  
Sin-Hua Moi ◽  
Yin-Syuan Chen ◽  
Li-Yeh Chuang ◽  
Bo-Sheng Li ◽  
...  
Keyword(s):  
Feature Selection ◽  
Nutritional Status ◽  
Serum Albumin ◽  
Optimization Algorithm ◽  
Regression Models ◽  
Whale Optimization Algorithm ◽  
Superior Performance ◽  
Clinical Factors ◽  
Multifactor Model ◽  
Whale Optimization

BACKGROUND Time-averaged serum albumin (TSA) is commonly associated with clinical outcomes in hemodialysis (HD) patients and considered a surrogate indicator of nutritional status. Whale optimization (WO)-based feature selection algorithm could address the challenges associated with the complex characteristics of multifactor interactions and could be combined with regression models. OBJECTIVE The present study aimed to demonstrate an optimal multifactor TSA-associated model, which could be applied in the interpretation of the association between TSA and clinical factors in HD patients. METHODS A total of 829 HD patients who met the inclusion criteria were analyzed. Monthly serum albumin data tracked from January 2009 to December 2013 were converted into TSA categories based on a critical value of 3.5 g/dL. Multivariate logistic regression was used to analyze the association between TSA categories and multiple clinical factors using three types of feature selection models, namely the fully adjusted model, stepwise model, and whale optimization algorithm (WOA) model. RESULTS The WOA yielded the lowest Akaike Information Criterion (AIC) value, which indicated that the WOA could achieve superior performance in multifactor analysis when compared to the fully adjusted and stepwise models. The significant features in the optimal multifactor TSA-associated model included age, creatinine, potassium, and HD adequacy index (Kt/V level). CONCLUSIONS The WOA algorithm could select five features from 15 clinical factors, which is the minimum number of selected features required in multivariate regression models for optimal multifactor model construction to achieve high model performance. Therefore, the application of the optimal multifactor TSA-associated model could facilitate nutritional status monitoring in HD patients. CLINICALTRIAL All data were retrospectively collected using an approved data protocol (201800595B0) with a waiver of informed consent from patients.

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