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An automatic IoT application placement using an improved equilibrium optimizer algorithm in fog computing

INTERNATIONAL JOURNAL OF INTELLIGENT COMPUTING AND CYBERNETICS 2025年第1期18卷 153-182页

作者： Sahragard, Khadijeh Maroosi, Ali Ghobaei-Arani, Mostafa Univ Torbat Heydarieh Dept Comp Engn Torbat Heydarieh Iran Islamic Azad Univ Dept Comp Engn Qom Branch Qom Iran

Purpose - The rapid proliferation of Internet of Things (IoT) devices across various domains has created a demand for real-time computing resources that traditional cloud computing models struggle to meet. Fog computing, which brings computation resources closer to IoT devices, has emerged as a promising solution. An automatic service placement framework is needed to use fog computing resources efficiently. Design/methodology/approach- In this study, first a three-layer independent service framework is introduced to define relationships between IoT devices and fog layers, facilitating automatic application deployment. Next, an enhanced version of the equilibrium optimizer (EO) algorithm, inspired by physics, is designed for service placement in fog computing environments. Findings - Simulations reveal that the proposed approach surpasses existing methods, achieving a 99% success rate compared to the closest alternative's 93%. The algorithm also significantly reduces waiting and planning times for service placement, proving its efficiency and effectiveness in optimizing IoT service deployment in fog computing. Research limitations/implications - One of the primary limitations is the computational complexity involved in dynamically adjusting to real-time changes in network conditions and IoT workloads. Although improved EO offers improvements in placement efficiency, it may not be fully optimized for highly fluctuating environments. Another important limitation is the uncertainty in node resources. Fog computing environments often face unpredictable changes in the availability and capacity of resources across nodes. This uncertainty can affect the algorithm's ability to consistently make optimal decisions for IoT service placement. Practical implications - From a practical perspective, the implementation of the proposed framework and the improved EO algorithm can drastically enhance the efficiency of IoT service deployment in fog computing systems. Organizations that

关键词： Fog service placement equilibrium optimizer algorithm Fog computing Internet of things

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Impact of carbon emission and preservation investment on a manufacturing system for deteriorating item with price and greenness dependent demand via equilibrium optimizer algorithm

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2025年第5期16卷 1813-1829页

作者： Ali, Hachen Akhtar, Fleming Guin, Sudipta Pakhira, Pritam Kumar Shaikh, Ali Akbar Ahmad, Izhar Univ Burdwan Dept Math Burdwan 713104 India King Fahd Univ Petr & Minerals Dept Math Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Ctr Intelligent Secure Syst Dhahran 31261 Saudi Arabia

The environmental sustainability and eco-friendliness of a product, sometimes known as its "greenness," can have a wide range of effects on various levels, including the human, social, economic, and environmental. In terms of a company's income, profitability, market positioning, and client relationships, the price of a product is crucial. Choosing the ideal price/value ratio is a crucial choice that necessitates careful evaluation of numerous internal and external considerations. Combining this impact, in this study, a production model is developed for deteriorating goods, considering preservation technology investment and product's green level and selling price dependent demand. The manufacturing firm's rate of carbon emissions increases corresponding to production's rate and time. Also, the cost per unit of produced items increases as the green level rises. The primary goal of this study is to find the optimal policy of the manufacturer to maximize the average profit. Due to the highly nonlinearity of the objective function, the associated optimization problem of our suggested model cannot be solved analytically. Five well-known metaheuristics [equilibrium optimizer algorithm (EOA), Artificial Electric Field algorithm (AEFA), Grey Wolf optimizer algorithm (GWOA), Whale optimizer algorithm (WOA) and Sparrow Search algorithm (SSA)] are used to find the best-found solutions. From the results of different statistical test (ANOVA, Wilcoxon rank sum and Friedman tests) and convergence rate of different algorithms, it is evident that EOA perform better than other metaheuristics. A numerical example is taken into consideration in order to assess the validity of the suggested model. Finally, the effects of changes in different inventory parameters are studied on the best-found solutions by performing sensitivity analyses and draw a conclusion.

关键词： Greenness of the products Deterioration Preservation technology Carbon emission investment Green level dependent production cost equilibrium optimizer algorithm

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A hybrid equilibrium optimizer algorithm for multi-level image segmentation

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2021年第4期18卷 4648-4678页

作者： Qi, Hong Zhang, Guanglei Jia, Heming Xing, Zhikai Northeast Forestry Univ Sch Informat & Comp Engn Harbin Heilongjiang Peoples R China Sanming Univ Sch Informat Engn Sanming Fujian Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan Peoples R China

Threshlod image segmentation is a classic method of color image segmentation. In this paper, we propose a hybrid equilibrium optimizer algorithm for multi-level image segmentation. When multi-level threshold method calculates the neighborhood mean and median of color image, it takes huge challenge to find the optimal threshold. We use the proposed method to optimize the multi-level threshold method and get the optimal threshold from the color image. In order to test the performance of the proposed method, we select the CEC2015 dataset as the benchmark function. The result shows that the proposed method improves the optimization ability of the original algorithm. And then, the classic images and wood fiber images are taken as experimental objects to analyze the segmentation result. The experimental results show that the proposed method has a good performance in Uniformity measure, Peak Signal-to-Noise Ratio and Feature Similarity Index and CPU time.

关键词： wood fiber image segmentation 3DOtsu equilibrium optimizer algorithm grasshopper optimization algorithm hybrid optimization algorithm

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Optimal Scheduling of Intelligent Building with Photovoltaic Energy Storage System Using Competitive Mechanism Integrated Multi-objective equilibrium optimizer algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第11期47卷 14641-14655页

作者： Yi, Lingzhi Li, Guanghua Chen, Kefu Liu, Jiankang Fan, Lu Gao, Xieyi Xiangtan Univ Coll Automat & Elect Engn Xiangtan Hunan Peoples R China Hunan Engn Res Ctr Multienergy Cooperat Control T Xiangtan Hunan Peoples R China Hunan Elect Appliance Res Inst Co Ltd Hunan Changsha Peoples R China Was Grp Ltd Changsha Hunan Peoples R China

In recent years, distributed energy has been gradually applied in residential electricity consumption, and smart devices have been rapidly developed among residential households. This paper establishes a model of optimal scheduling system for building load, taking into account the needs of grid side and customer side, and takes the total cost of electricity consumption and total deviation of electricity consumption of building residents as the optimization objectives. Then, a multi-objective equilibrium optimizer algorithm integrated competition mechanism (CMOEO) is proposed to solve the multi-objective optimization scheduling problem of building loads with multi-decision variables. The algorithm is improved from the equilibrium optimizer algorithm by introducing the competition mechanism, replacing the original exploration and exploitation constant coefficients with dynamic factors and integrating differential evolution. Through the comparison experiments of different algorithms and the different capacity of photovoltaic energy storage system (PESS), the results show that the CMOEO has fast convergence speed, strong merit-seeking ability and better population diversity. In addition, the configuration of PESS is conducive to reducing the cost of electricity for building residents and playing the role of peak-shaving and valley-filling.

关键词： Load scheduling CMOEO equilibrium optimizer algorithm Multi-objective optimization Photovoltaic energy storage system

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Integrated Volt/Var Control and Conservation Voltage Reduction in Unbalanced Distribution Networks Considering Smart PV Inverter Control Using equilibrium optimizer algorithm 2

Integrated Volt/Var Control and Conservation Voltage Reducti...

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2nd Annual IEEE Kansas Power and Energy Conference (KPEC)

作者： Ahanch, Mojtaba McCann, Roy Univ Arkansas Elect Engineeering Dept Fayetteville AR 72701 USA

ISBN: (纸本)9781665441193

This paper introduces a new method for integrated Volt/Var control and Conservation Voltage Reduction (CVR) in unbalanced power distribution networks considering high penetration of PV generation. The mixed integer nonlinear programming (MINLP) based Volt/Var-CVR problem is solved using equilibrium optimizer (EO) algorithm. The method coordinates the use of smart PV inverters along with voltage regulating equipment including voltage regulators (VRs) and on-load tap changers (OLTCs). The objective of the study is to improve energy conservation and minimize busses voltage deviations and reduce the legacy device operations through implementing the Volt/Var control mode of smart PV inverters in the IEEE 34-bus case study system. The proposed model uses COM interface to make an interaction between OpenDSS and MATLAB programs to find the optimum solution of the Volt/Var-CVR problem. Simulation results authenticate the efficacy of the proposed method and demonstrate a considerable increase in the energy saving and power loss reduction.

关键词： Conservation Voltage Reduction Volt/Var Control Smart PV equilibrium optimizer algorithm OpenDSS

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Tuning Improvement of Power System Stabilizer using Hybrid Harris Hawk Optimization-equilibrium optimizer algorithm 6

Tuning Improvement of Power System Stabilizer using Hybrid H...

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6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Prakasa, Mohamad Almas Robandi, Imam Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia

ISBN: (纸本)9798350399615

A Harris Hawk Optimization (HHO) become popular in recent works to resolve various power system cases, including tuning Power System Stabilizer (PSS). However, HHO has several challenges, such as premature convergence can occur due to less diversity in solutions with an unbalanced exploration and exploitation process. Therefore, this paper proposed The Hybrid Harris Hawk Optimization-equilibrium optimizer algorithm (HHHO-EOA) to enhance HHO performance and acquire better optimal tuning for PSS. The EOA is a well-balanced exploration and exploitation algorithm. A Linearized Heffron-Phillips model for Single-Machine Infinite Bus (SMIB) is modeled to evaluate the proposed algorithm in tuning PSS. Various performance indices are used as the objective functions. The HHHO-EOA has a significantly better enhancement in the convergence curve with consistent results than previous algorithms. The minimum objective function achieved by the proposed algorithm is 78.3% and 60% lower than conventional EOA and HHO. Moreover, the PSS based on the proposed algorithm is 10.24% and 19.69% better in damping the overshoot than conventional EOA and HHO in both frequency and power angle deviation responses. © 2022 IEEE.

关键词： equilibrium optimizer algorithm Harris Hawk Optimization Hybrid algorithm Power System Stabilizer

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Enhancement of K-means clustering in big data based on equilibrium optimizer algorithm

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JOURNAL OF INTELLIGENT SYSTEMS 2023年第1期32卷

作者： Al-kababchee, Sarah Ghanim Mahmood Algamal, Zakariya Yahya Qasim, Omar Saber Univ Mosul Dept Math Mosul 41002 Iraq Univ Al Hamdaniya Educ Coll Dept Math Bartella 41019 Iraq Univ Mosul Dept Stat & Informat Mosul 41002 Iraq Univ Warith Al Anbiyaa Coll Engn Karbala 56001 Iraq

Data mining's primary clustering method has several uses, including gene analysis. A set of unlabeled data is divided into clusters using data features in a clustering study, which is an unsupervised learning problem. Data in a cluster are more comparable to one another than to those in other groups. However, the number of clusters has a direct impact on how well the K-means algorithm performs. In order to find the best solutions for these real-world optimization issues, it is necessary to use techniques that properly explore the search spaces. In this research, an enhancement of K-means clustering is proposed by applying an equilibrium optimization approach. The suggested approach adjusts the number of clusters while simultaneously choosing the best attributes to find the optimal answer. The findings establish the usefulness of the suggested method in comparison to existing algorithms in terms of intra-cluster distances and Rand index based on five datasets. Through the results shown and a comparison of the proposed method with the rest of the traditional methods, it was found that the proposal is better in terms of the internal dimension of the elements within the same cluster, as well as the Rand index. In conclusion, the suggested technique can be successfully employed for data clustering and can offer significant support.

关键词： clustering penalized method equilibrium optimizer algorithm K-means feature selection data mining swarms

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Hybrid Henry gas solubility optimization and the equilibrium optimizer for feature selection: real cases with Twitter spam detection

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KNOWLEDGE AND INFORMATION SYSTEMS 2024年第5期66卷 3055-3084页

作者： Legoui, Khaoula Zineb Maza, Sofiane Attia, Abdelouahab Houssein, Essam H. Univ Mohamed Bachir Ibrahimi Bordj Bou Arreridj Comp Sci Dept Bordj Bou Arreridj Algeria Univ Mohamed Bachir Ibrahim Bordj Bou Arreridj LMSE Lab Lab Mat & Elect Syst Bordj Bou Arreridj 34000 Algeria Minia Univ Fac Comp & Informat El Minia Egypt

The rapid spread and daily usage of social networks have made them vulnerable to spammers. Therefore, detecting and eliminating spam and spammers has become more than necessary to reduce the risks that it poses to users' security. In order to achieve this goal, it is crucial to determine the exact features that help identify and classify whether a user is spam or not. This paper proposes a wrapper-based method for selecting the most important features. It is based on combining two recent metaheuristic algorithms, the Henry Gas Solubility Optimization algorithm (HGSO) and the equilibrium optimizer algorithm (EO), with the goal of choosing a small and most influential subset of features that give good performance and help in the spammer profile detection process. For the purpose of showing the ability of the proposed method to achieve the desired goals, several comparisons are conducted on a modified Social Honeypot dataset. The first comparison is made between HGSOEO and the two algorithms (HGSO and EO) that were used to develop the proposed algorithm to prove the power of hybridization. The two next comparisons are made against some classical filter- and wrapper-based feature selection methods. The last comparison is carried out against some well-known metaheuristic algorithms for feature selection. Experiments and analysis of the results show that the proposed model is more accurate than the algorithms and methods that we compared it to.

关键词： Feature selection, Henry Gas Solubility Optimization algorithm equilibrium optimizer algorithm Twitter spam detection

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A novel fractional order PID plus derivative (PI^λD^μD^μ²) controller for AVR system using equilibrium optimizer

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2021年第3期40卷 722-743页

作者： Tabak, Abdulsamed Necmettin Erbakan Univ Engn Fac Dept Mech Engn Konya Turkey

Purpose The purpose of this paper is to improve transient response and dynamic performance of automatic voltage regulator (AVR). Design/methodology/approach This paper proposes a novel fractional order proportional-integral-derivative plus derivative ((PIDD mu 2)-D-lambda-D-mu) controller called FOPIDD for AVR system. The FOPIDD controller has seven optimization parameters and the equilibrium optimizer algorithm is used for tuning of controller parameters. The utilized objective function is widely preferred in AVR systems and consists of transient response characteristics. Findings In this study, results of AVR system controlled by FOPIDD is compared with results of proportional-integral-derivative (PID), proportional-integral-derivative acceleration, PID plus second order derivative and fractional order PID controllers. FOPIDD outperforms compared controllers in terms of transient response criteria such as settling time, rise time and overshoot. Then, the frequency domain analysis is performed for the AVR system with FOPIDD controller, and the results are found satisfactory. In addition, robustness test is realized for evaluating performance of FOPIDD controller in perturbed system parameters. In robustness test, FOPIDD controller shows superior control performance. Originality/value The FOPIDD controller is introduced for the first time to improve the control performance of the AVR system. The proposed FOPIDD controller has shown superior performance on AVR systems because of having seven optimization parameters and being fractional order based.

关键词： Automatic voltage regulator equilibrium optimizer algorithm PID controller FOPID controller PIDA controller Optimization Control system Voltage regulation Design optimization methodology

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Design of I-PD Controller Based Modified Smith Predictor for Processes With Inverse Response and Time Delay Using equilibrium optimizer

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IEEE ACCESS 2023年 11卷 14636-14646页

作者： Dogruer, Tufan Tokat Gaziosmanpasa Univ Dept Elect & Elect Engn TR-60250 Tokat Turkiye

In the process control industry, it is arduous to control some integrating or unstable processes since they involve time delays and have an inverse response. Conventional controllers such as PID cannot provide sufficient control performance alone in the control of these systems. This article proposes a control algorithm based on an I-PD-based Smith predictor for the control of time-delayed integrating or unstable inverse processes. The controller parameters are tuned by using the equilibrium optimizer (EO) algorithm, which is presented in the literature in 2020, in the proposed control approach. The EO algorithm aims to determine the optimal controller parameters by minimizing the error and control signal using a multi-objective function based on ITAE performance criterion. Thus, the controller parameters that will provide the set-point tracking and disturbance rejection control most properly can be determined. Simulation studies are conducted based on different process structures to evaluate the performance of the proposed method. The proposed method is compared with studies from the literature in terms of set-point tracking, parameter uncertainties, control signals, and disturbance rejection. It is seen that the transient responses and disturbance rejection of the time-delayed and inverse response integrating or unstable processes are improved with the proposed method.

关键词： Process control Mathematical models Tuning Delay effects Prediction algorithms Inverse problems equilibrium optimizer algorithm I-PD controller design inverse response non-minimum phase system smith predictor time delay systems

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