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Privacy Based Data Publishing Model for Cloud Computing Environment

WIRELESS PERSONAL COMMUNICATIONS 2020年第4期113卷 2215-2241页

作者： Bibal Benifa, J. V. Venifa Mini, G. Indian Inst Informat Technol Kottayam Kerala India Noorul Islam Ctr Higher Educ Kumaracoil Tamil Nadu India

Cloud computing is a popular model for providing data storage services from remote computing facilities through internet. Security is known as an element for protecting sensitive information from vulnerable attacks and ensuring information confidentiality, integrity and authenticity. Privacy is the assurance that users could maintain complete control over their sensitive information. Cloud storage-based data publication is significant in medical field where it contains sensitive information such as nature of the disease, patient medical history, and effects of the illness. The publisher should not disclose any of the individual or sensitive information of the individuals with the research board while publishing the reports to the medical data analysts. Deciding on the nature of sensitivity, the user may be allowed to access the information from cloud environment that is a complex process. In order to ensure the complete privacy of individual medical history, the present research work employsk-anonymization to upgrade the privacy policies in the cloud storage. In addition to this, the genetic grey wolf optimization algorithm is employed to decide the data to be published based on the information preserved for privacy purposes. The proposed work is evaluated in a real cloud infrastructure with respect to privacy, utility and information losses. The results show that the proposed method is efficient for privacy-based data publication as it conceals the sensitive information effectively.

关键词： Privacy Utility Information loss Data publishing Anonymization gwo algorithm

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A novel approach for nature-based optimization algorithms: The threat factor approach

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第20期33卷 e6341-e6341页

作者： Toz, Metin Toz, Guliz Karamanaoglu Mehmetbey Univ Fac Engn Comp Engn Dept Karaman Turkey Karamanoglu Mehmetbey Univ Ermenek Vocat Sch Comp Technol Dept Karaman Turkey

Nature-inspired optimization algorithms especially those based on the hunting behaviors of the creatures assume that the hunting operations are performed in a safe environment. However, generally, there are threats in real-life for the hunter-animals. This paper focuses on these threat factors and proposes that they can be used to improve the searching abilities of the algorithms. Gray wolf optimization (gwo) algorithm was selected to present the proposed approach and it was assumed that there was a mountain lion as the threat factor living in the same habitat with the wolf pack. The relations between the two predators were modeled and used to improve the performance of the algorithm. Five experiments were conducted to test the performance of the proposed method and the results were compared with the gwo and four optimization algorithms from the literature. It is shown that the proposed algorithm obtained best results for 21 of the 50 benchmark functions, while its closest competitor achieved the best results for 16 functions. Besides, the results of the Wilcoxon signed-rank test indicated that the proposed method is superior to all other methods. In addition, it was shown that the threat factor approach does not cause a significant increase in the processing time.

关键词： gwo algorithm nature‐ inspired optimization threat factor approach

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Optimal sustainable order quantities for growing items

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JOURNAL OF CLEANER PRODUCTION 2021年 307卷 127216-127216页

作者： Gharaei, Abolfazl Almehdawe, Eman Univ Regina Fac Business Adm Regina SK Canada

Some industries, such as the livestock and poultry industries, produce "growing items," which can be defined as items whose weight and value constantly increase over time. This paper details the design and optimization of a sustainable inventory model for such items that considers the environmental effects of Greenhouse Gases (GHGs) emitted from produced manure, fermentation processes, and transportation, as well as their cost with respect to carbon tax. Furthermore, the proposed model also considers the growth patterns for all dead and live grown items, along with mortality and survival probabilities. Additionally, the sustainability of the period length and order quantity of items is optimized in order to minimize the cost function, while still satisfying the environmental constraints on the main sources of emissions. Through a series of examples, we show that our sustainable Economic Order Quantity (EOQ) model is suitable for applications dealing with growing items. To solve these examples, we utilized the "HHO-gwo" algorithm, which is a novel hybrid metaheuristic that combines the advantages of the Harris Hawks Optimizer (HHO) and Grey Wolf Optimization (gwo) algorithms to find much better solutions in fewer iterations. The performance of the HHO-gwo algorithm is compared to that of the HHO and gwo algorithms, as well as Interior Point (IP) solver, with respect to different measures such as quality of generated solutions and CPU-Time. The results indicated that, compared to the HHO and gwo algorithms on their own, the HHO-gwo algorithm generates much better solutions for the proposed model, while also minimizing CPU-Time. The results obtained for the model showed that considering sustainability factors for growing items not only increases the order quantity, but it also shortens the growth cycle significantly. Finally, sensitivity analyses of the parameters and decision variables identified some practical approaches that can be utilized by inventory managers in

关键词： Sustainability Growing items Inventory management Agri-business HHO algorithm gwo algorithm

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Identification of Fractional Order Transfer Function Model Using Biologically Inspired algorithms

Identification of Fractional Order Transfer Function Model U...

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International Conference on Automation (AUTOMATION)

作者： Dziedzic, Klaudia AGH Univ Sci & Technol Krakow Poland

ISBN: (纸本)9783030132736;9783030132729

This paper presents the identification of a non-integer order model for the heat transfer process using the particle swarm optimization algorithm (PSO), cockroach swarm optimization algorithm (CSO), gray wolf optimizer algorithm (gwo) and fminsearch function. In the beginning, fractional order systems have been discussed. Then an overview of individual optimization methods was prepared. Simulations have been carried out for all used the algorithms.

关键词： Fractional order systems Identification PSO algorithm fminsearch CSO algorithm gwo algorithm

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Optimal Scheduling of HVAC Set-point for Electricity Peak Reduction and Avoid Rebound Phenomenon 28

Optimal Scheduling of HVAC Set-point for Electricity Peak Re...

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28th Iranian Conference on Electrical Engineering (ICEE)

作者： Talebi, Ashkan Hatami, Alireza Bu Ali Sina Univ Dept Elect Engn Hamadan Hamadan Iran

ISBN: (数字)9781728172965

ISBN: (纸本)9781728172965

Air conditioners, as the largest commercial and domestic consumers, play an important role in demand response programs. It should be noted that unplanned participation of these loads in demand response programs can lead to a new peak in the power grid;this effect is called the rebound phenomenon. In this study, using weather forecasting data, and using electricity load profile prediction, the optimal 24-hour scheduling of the thermostat set-point of the air conditioners has been implemented in a way that HVAC systems contribute to demand response programs and no rebound phenomenon could be observed due to proper scheduling. The weather forecast has been obtained through a Markov chain. Since this forecast has uncertainties, various scenarios have been developed to manage the risk of these uncertainties. The electricity load profile is forecasted by using GMDH Shell software load forecasting tool. The gwo met heuristic algorithm is used to solve the optimization problem. The effectiveness of the above method has been compared with the direct load control method and the uncontrolled method, and the results show that this scheduling method can have a significant effect on the air conditioning systems' participation in demand response programs. Also using this method, the rebound coefficient is measured 3.5% which is very desirable.

关键词： Rebound effect HVAC systems Optimal Scheduling gwo algorithm demand response

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Tuning the Fractional Order PID Controller in the Forced Air Heating System Using Biologically Inspired algorithms

Tuning the Fractional Order PID Controller in the Forced Air...

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International Conference on Automation (AUTOMATION)

作者： Dziedzic, Klaudia AGH Univ Sci & Technol Krakow Poland

ISBN: (纸本)9783030409715;9783030409708

The main objective of this research is to present the tuning of the fractional order PID controller for the forced air heating system using the particle swarm optimization algorithm (PSO), cockroach swarm optimization algorithm (CSO), grey wolf optimizer algorithm (gwo). In preliminaries, fractional calculus is discussed. Then, all three biological algorithms are briefly presented. Obtained simulation results allow comparison of individual algorithms in terms of overshoot, settling time and performance criteria (IAE, ITAE).

关键词： Fractional order systems FOPID PSO algorithm CSO algorithm gwo algorithm

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Optimal Power Flow-Based Combined Economic and Emission Dispatch Problems Using Hybrid PSgwo algorithm

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2019年第9期28卷

作者： Rex, C. R. Edwin Selva Beno, M. Marsaline Annrose, J. Lourdes Mt Coll Engn & Technol Dept EEE Mullanginavilai India St Xaviers Catholic Coll Engn Dept Elect & Elect Engn Nagercoil India St Xaviers Catholic Coll Engn Dept Comp Sci & Engn Nagercoil India

This paper proposes a new and efficient hybrid approach combining two meta-heuristic methods for solving economic and emission dispatch problems. We used particle swarm optimization (PSO)-based gray wolf optimization to solve this problem. Additionally, the nonlinear control parameter is employed to balance the global search and local search ability of the algorithm and improve the convergence speed of the algorithm. At the same time, the idea of PSO is introduced, which utilizes the best value of the individual and the best value of the wolf pack to update the position information of each gray wolf. This method preserves the best position information of the individual and avoids the algorithm falling into a local optimum. The optimal power flow (OPF)-based CEED problem is formulated with the combination of fuel cost, fuel emission with penalty function, real power loss and voltage deviation. The proposed approach is implemented in MATLAB working platform and tested by IEEE 30 bus system with different test cases. Moreover, the CEED problem-solving performance of PSgwo algorithm is examined by 3-unit, 6-unit and 40-unit test systems. Then, the obtained results are compared with conventional methods to highlight the benefits of the proposed algorithm in reducing the fuel cost, fuel emission, voltage deviation and power losses, respectively. The experimental results show that the proposed approach provides accurate solutions for all types of objective solutions.

关键词： Optimal power flow economic dispatch emission dispatch combined economic and emission dispatch gwo algorithm particle swarm optimizer

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gwo based controller design for Riverol-Pilipovik water treatment plant

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JOURNAL OF STATISTICS & MANAGEMENT SYSTEMS 2020年第2期23卷 463-470页

作者： Chodavarapu, Madan Mohan Shaw, Binod Singh, Vinay Pratap Natl Inst Technol Dept Elect Engn Raipur 492010 Chhattisgarh India Malaviya Natl Inst Technol Dept Elect Engn Jaipur 302017 Rajasthan India

In this work, grey wolf optimization (gwo) based controller design is proposed for Riverol-Pilipovik (RP) water treatment system. The RP water treatment system considered in this work is basically an interacting two-input-two-output (TITO) system. The interacting TITO system is first converted into two non-interacting loops by designing decoupler. For the obtained non-interacting loops, two separate proportional-integral-derivative (PID) controllers should be designed by minimizing the integral-of-squared-error (ISE). Constructed ISE is minimized using recently proposed gwo algorithm. For showing the efficiency of gwo assisted controllers, two test cases are considered in this work. The results obtained show that gwo based PID controllers are excellent in performance.

关键词： Decoupling gwo algorithm Reverse Osmosis Riverol-Pilipovik System Water Treatment

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A hybrid fuzzy-PID controller based on gray wolf optimization algorithm in power system

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EVOLVING SYSTEMS 2019年第2期10卷 273-284页

作者： Ghanamijaber, Mobin Islamic Azad Univ Ardabil Branch Dept Engn Ardebil Iran

This paper addresses a grey wolf optimizer to optimally set the PID controller with low pass filter for simulating multi area restructured power system with considered superconducting magnetic energy storage plant that works under various loading conditions and uncertainties. To make a real model, the reheat effect nonlinearity of the steam turbine and governor is considered into automatic generation control (AGC) model. The proposed AGC model is formulated as a nonlinear constrained optimization problem which is solved by an intelligent algorithm so that it shows powerful search in optimization problem especially the proposed AGC. The effectiveness of the proposed technique is demonstrated on a deregulated power system with possible contracted scenarios under large load demand and area disturbances in comparison with other method through several performance indices. Obtained results show that the proposed control strategy achieves good robust performance for a wide range of system parameters and load changes in the presence of system nonlinearities and is superior to the other controllers.

关键词： PID controller Multi area gwo algorithm SMES Nonlinear power system

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Unsupervised hyperspectral feature selection based on fuzzy c-means and grey wolf optimizer

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2019年第9期40卷 3344-3367页

作者： Xie, Fuding Lei, Cunkuan Li, Fangfei Huang, Dan Yang, Jun Liaoning Normal Univ Coll Urban & Environm 850 Huanghe Rd Dalian Liaoning Peoples R China Liaoning Normal Univ Coll Comp Sci Dalian Liaoning Peoples R China

Hyperspectral image (HSI) with hundreds of narrow and consecutive spectral bands provides substantial information to discriminate various land-covers. However, the existence of redundant features/bands not only gives rise to increasing of computation time but also interferes the classification result of hyperspectral images. Obviously, it is a very challenging problem how to select an effective feature subset from original bands to reduce the dimensionality of the hyperspectral dataset. In this study, a novel unsupervised feature selection method is suggested to remove the redundant features of HSI by feature subspace decomposition and optimization of feature combination. Feature subset decomposition is achieved by the fuzzy c-means (FCM) algorithm. The optimal feature selection is based on the optimization process of grey wolf optimizer (gwo) algorithm and maximum entropy (ME) principle. To evaluate the effectiveness of the proposed method, experiments are conducted on three well-known hyperspectral datasets, Indian Pines, Pavia University, and Salinas. Six state-of-the-art feature selection methods are used to compare with the proposed method. Experimental results successfully confirm the superior performance of our proposal with respect to three classification accuracy indices overall accuracy (OA), average accuracy (AA) and kappa coefficient (kappa).

关键词： hyperspectral image feature selection FCM algorithm gwo algorithm

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